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The cross-directional model predictive control (CD-MPC) tuning problem is investigated for multiple-array paper-making process with parametric uncertainty. A systematic spatial tuning method is proposed to tune the penalty matrices such that the harmful high frequency elements in each actuator profile can be eliminated. A multivariable temporal filter is employed to adjust the output references, and a temporal tuning algorithm is developed for the filter parameters to achieve the pre-specified 2 σ performance indices. The computational time of the proposed methods is reduced via event-based sampling strategy. The effectiveness of the techniques is shown via a CD system from paper-making industry. [ABSTRACT FROM AUTHOR]

This paper solves the controller tuning problem of machine-directional predictive control for multiple-input–multiple-output (MIMO) paper-making processes represented as superposition of first-order-plus-dead-time (FOPDT) components with uncertain model parameters. A user-friendly multi-variable tuning problem is formulated based on user-specified time domain specifications and then simplified based on the structure of the closed-loop system. Based on the simplified tuning problem and a proposed performance evaluation technique, a fast multi-variable tuning technique is developed by ignoring the constraints of the MPC. In addition, a technique to predict the computation time of the tuning algorithm is proposed. The efficiency of the proposed method is verified through Honeywell real time simulator platform with a MIMO paper-making process obtained from real data from an industrial site. [ABSTRACT FROM AUTHOR]

• A model-based approach is proposed for the design synthesis of autonomous articulated vehicles. • The dynamically coupled subsystems of an autonomous vehicle are devised and evaluated simultaneously. • A prediction vehicle model for articulated vehicles is developed. • The effectiveness of the proposed approached is demonstrated by numerical simulations. • The results presented in the paper may be used as guidelines for devising autonomous articulated vehicles. In conventional autonomous vehicle designs, the mechanical system is first devised, and the automated driving systems are subsequently added. The conventional method can be categorized as a sequential design approach. An autonomous vehicle consists of the subsystems of mechanical assembles, automated driving, etc. There exist dynamic couplings among these subsystems, and the sequential design method may not be the most effective. This observation initiates the motivation of this research, and the objective of this paper is thus to explore a more effective design method for autonomous vehicles and, in particular, autonomous articulated vehicles. To this end, this paper proposes a model-based design synthesis method, i.e., a concurrent design method, for autonomous articulated vehicles. The autonomous articulated vehicle assumes to be composed of the subsystems of a mechanical unit, a trajectory tracking-controller based on a model predictive control technique, and a motion-planner. The design synthesis of the autonomous articulated vehicle is constructed as a bi-level optimization problem. With the proposed concurrent design approach, the subsystems are optimally devised simultaneously. To examine the proposed approach, it is applied to the design of a car-trailer combination with automated steering. The insightful findings attained from the study may be used as guidelines for developing autonomous articulated vehicles. [ABSTRACT FROM AUTHOR]

This paper proposes a novel energy management strategy satisfying real-time and highly efficient energy management strategies for fuel cell hybrid electric vehicles (FCHEVs). The strategy is based on model predictive control (MPC), which integrates long short-term memory (LSTM) and dynamic programming (DP). A high-precision powertrain model of the investigated FCHEV is established for subsequent simulations. After training under several typical working conditions, LSTM is designed to forecast future power demands of the entire vehicle. Using the prediction results, the DP algorithm calculates the control scheme based on model predictive control. Considering the economy and durability of power sources, the results of four different control strategies are compared: thermostat, power following, traditional DP, and MPC. The MPC proposed in this paper reduces the total usage cost per 100 km on the test set by 9%, 33.5%, and −4.6%. • The economy and durability of vehicular fuel cell system are both considered. • The LSTM neural network is imported into MPC theory for vehicular power prediction. • The key parameters of MPC and LSTM are determined by referring to the greedy algorithm. • The computational load in MPC optimisation is reduced by simplifying DP algorithm. • The real-time performance of the supposed EMS on-board is validated. [ABSTRACT FROM AUTHOR]

Fuel cell hybrid electric vehicles (FCHEV) are one of the most promising new energy vehicles. The cost and lifetime of its powertrain have limited its commercial development. This paper proposed an energy management strategy based on nonlinear model predictive control (NMPC) technology to solve the economy and durability problem of FCHEVs. Based on Markov Monte Carlo(MCMC) method, a prediction model of multi-scale operating conditions is established, and dynamic programming(DP) is used to realize the optimal control in the predicted time domain. The "constant speed prediction" is innovatively adopted in the transition stage to improve the prediction accuracy and enable the model to be realized online. The ways to reduce calculating amount of NMPC are also discussed in this paper. This simplification leads to suboptimal fuel economy and durability of control system but can have obvious reduction in calculating time. The simulation results show that, compared with the thermostat strategy and the power following strategy, the degradation cost decrease of 11.1% and 23.9% and the total operation cost of NMPC decrease of 11.0% and 23.5% respectively. The NMPC strategy has better economy and durability than the rule-based energy management strategy, is close to the global optimal result obtained by dynamic programming and can meet the requirements of real-time control. • The economy and durability of vehicular fuel cell system are both considered. • Markov Chain Monte Carlo is imported into MPC theory for vehicular power prediction. • Adaptability to changing conditions is strengthened via "constant speed prediction". • The computational load in MPC optimization is reduced by simplifying DP algorithm. [ABSTRACT FROM AUTHOR]

This paper proposes a novel adaptive variable power sliding mode observer-based model predictive control (AVPSMO-MPC) method for the trajectory tracking of a Mecanum-wheeled mobile robot (MWMR) with external disturbances and model uncertainties. First, in the absence of disturbances and uncertainties, a model predictive controller that considers various physical constraints is designed based on the nominal dynamics model of the MWMR, which can transform the tracking problem into a constrained quadratic programming (QP) problem to solve the optimal control inputs online. Subsequently, to improve the anti-jamming ability of the MWMR, an AVPSMO is designed as a feedforward compensation controller to suppress the effects of external disturbances and model uncertainties during the actual motion of the MWMR, and the stability of the AVPSMO is proved via Lyapunov theory. The proposed AVPSMO-MPC method can achieve precise tracking control while ensuring that the constraints of MWMR are not violated in the presence of disturbances and uncertainties. Finally, comparative simulation cases are presented to demonstrate the effectiveness and robustness of the proposed method. • kinematic and dynamic constraints are handled efficiently. • The improved observer has a fast convergence rate without chattering. • The method can improve tracking performance. [ABSTRACT FROM AUTHOR]

Aerodynamic forces hold significant promise for controlling small-satellite formations in low Earth orbit. However, significant challenges still exist regarding complex input constraints and their application to multi-satellite formations. This paper proposes a constraint tightening-based control scheme for small-satellite formations, utilizing differential drag and lift as actuating forces. Two attitude adjustment strategies, namely the reorientation strategy and the single-radial rotation strategy, are compared in terms of their control effectiveness and applicability. Relative aerodynamic coefficients are employed as control inputs in view of their solid reachable region, followed by the development of a decoupling algorithm for determining pointing angles. Through a method that tightens the reachable region using liner models, the input constraints are linearized and can be explicitly expressed. Subsequently, an observer-based Model Predictive Control (MPC) algorithm is formulated to meet these linearized input constraints and provide feedforward compensation for system disturbances. This discrete algorithm enables practical onboard implementations owing to its low computational complexity. Additionally, a concept of overall computation for all satellites' attitudes is proposed to adapt the control scheme for multi-satellite formations. Hardware-in-the-loop simulations are conducted to evaluate the control scheme's performance in along-track and circular formations, accounting for uncertainties in aerodynamic forces and attitude dynamics. [ABSTRACT FROM AUTHOR]

Electric Solar Wind Sails (E-Sails) are a new type of spacecraft propellantless propulsion system that gathers its energy from solar wind protons and is potentially useful for interplanetary missions. Although optimal interplanetary trajectories have been the subject of thorough research, the substantial variability of the solar wind necessitates the adoption of active guidance strategies, an area that has received significantly less scholarly attention. This paper proposes guidance algorithms for E-Sails based on Model Predictive Control (MPC), a modern control methodology based on online re-planning of the trajectory. To this end, first, properties of E-sail time-optimal orbits are studied applying Pontryagin's Minimum Principle, and then time-optimal orbits for missions to Mars and Jupiter are computed via direct transcription methods. Next, solar wind perturbations are modeled, posing a challenging saturation problem due to their high variability. Guidance strategies based on Shrinking Horizon and Receding Horizon Model Predictive Control (RHMPC) are developed, analyzed and compared using Monte Carlo simulations, successfully implementing MPC to E-sail guidance. Lastly, the RHMPC strategy is successfully tested with accurate historical solar wind data from the WSA-Enlil model. • Solar wind fluctuations implies need of guidance in E-sail interplanetary missions. • Optimal planning for E-sails is performed using direct and indirect methods. • Control saturation due to solar wind pressure is reduced by leaving control margins. • Model predictive control guidance methods are compared with Monte Carlo analysis. • Receding horizon strategy in tested with semi-empirical historical solar wind. [ABSTRACT FROM AUTHOR]

Energy process system positively contributes to the energy utilization efficiency, the energy complement, and the construction of a low-carbon sustainable energy system. The multiple energy subsystems are deep interdependent, therefore, significant operational risks exist in the energy process system. To avoid system risk and fulfill operation requirement, we propose the Safety-Index based model predictive control that coordinates the controller design and system safety in this work. The innovation of the method is to integrate the state-space model of process system with advanced controller design while accounting for system safety. Since the state-space model of the energy process system is described by coupled partial differential equations and ordinary differential equations, which leads to difficulties in modeling and solving, the main contribution of this work is to simplify the constrained optimization problem formed by model predictive controller. The paper addresses the compressed air energy storage system as case study. From the numerical simulations of the safety controller performance, it shows that the system safety can be guaranteed by control strategy which realizes the system operation target and reject the system external disturbances, which caused by environmental or operation change. • The paper addresses the modeling and dynamic safety control of compressed air energy storage system. • A control loop for safety operation that consists of controllers, system models, system constraints, and safety index is proposed. [ABSTRACT FROM AUTHOR]

This paper investigates the asteroid hovering problem using the Multiple-Overlapping-Horizon Multiple-Model Predictive Control method. The effectiveness of the predictive controllers in satisfying control constraints and minimizing the required control effort is making Model Predictive Control a desirable control method for asteroid exploration missions which consist of the asteroid hovering phase. However, the computational burden of Model Predictive Control is an obstacle to employing the asteroid's complex gravitational field model. As an alternative option, the Multiple Horizon Multiple-Model Predictive Control method has been introduced previously, which could provide a solution with the less computational burden with respect to the nonlinear Model Predictive Control. It was shown that it is not necessary to deduce the exact dynamics model to predict the system's behavior during a long period using this approach. However, the calculated control acceleration was not smooth enough because of the crisp borders of consecutive horizons, which may cause an image motion and degrades the geometric accuracy of high-resolution images in asteroid hovering missions. In this paper, the Multiple-Overlapping-Horizon Multiple-Model Predictive Control method is introduced instead to solve the problem of controlling acceleration fluctuations by overlapping consecutive horizons. Numerical simulation results are presented to validate the effectiveness of the proposed control method, and its advantage is demonstrated accordingly for the asteroid hovering problem in achieving the hovering position and velocity. [ABSTRACT FROM AUTHOR]

This paper deals with the problem of controlled atmospheric re-entry, which has recently gained growing interest from the aerospace scientific and industrial communities for the economic and environmental benefits related to the development of reusable space vehicles. In this context, an original technique based on the incremental formulation of the Model Predictive Control methodology is proposed to tackle the trajectory tracking problem of a small satellite equipped with a deployable heat shield in the de-orbiting phase, focusing on a range of altitude from 300 km to 130 km, i.e., before the atmosphere is dense enough to begin a terminal re-entry phase. The proposed control logic allows following a given pre-determined guidance law, limiting position offsets due to unmodelled disturbances or uncertainties in the knowledge of spacecraft parameters (e.g., drag coefficient). An extensive numerical simulation campaign to assess the control performances in different perturbing conditions has demonstrated its ability to reach a km-error level in the position error at the final altitude of 130 km. • Controlled deorbiting based on aerodynamic drag modulation is addressed. • An Incremental formulation of the MPC controller is proposed for trajectory-tracking. • Performance assessment is carried out in a realistic simulation environment. • The achieved tracking error is kept below 1 km at re-entry interface (130 km). [ABSTRACT FROM AUTHOR]

Human desire to explore Mars has persisted throughout history, and especially, it is required to achieve precise and safe landings on the Martian surface. The atmospheric entry phase plays a crucial role in ensuring mission success, and active research on entry guidance methods continues. This paper presents a comparative study between the two primary types of entry guidance methods: (1) path planning-tracking and (2) predictor–corrector methods by applying them to solve two distinct Mars atmospheric entry guidance problems: (a) robotic entry and (b) human entry missions. Optimization approaches are adopted for the path planning-tracking method. After generating an optimal reference trajectory based on a mission concept-defined cost function, the receding horizon control method is employed to minimize tracking errors. In the predictor–corrector method, two parameterized entry guidance approaches are explored and analyzed. A well-known numerical predictor–corrector method that utilizes a parameterized bank angle profile for entry trajectory generation is tested to demonstrate its robustness against random dispersions. On the other hand, another predictor–corrector type guidance method that assumes a polynomial shape to shape an altitude profile over range-to-go is adopted to show its range control capability. Numerical simulations reveal the pros and cons of each type of entry guidance method, and a suitable guidance method for each Mars entry mission is identified depending on the mission concept and requirements. The analysis results provide future work. • A comparative study between path planning-tracking and predictor-corrector guidance methods. • Mars atmospheric entry problem for robotic and human landing missions. • Optimization approaches for reference trajectory generation and path tracking. • Numerical predictor-corrector based on parameterized bank angle and altitude profiles. [ABSTRACT FROM AUTHOR]

Encountering complex traffic conditions such as the dynamic interference of preceding and rear vehicles and gradients, a control strategy that simultaneously considers inter-vehicle cooperative control and energy economy is one of the key technologies, that improves traffic efficiency and exploits the energy-saving potential of platoon vehicles. In this paper, a hierarchical optimization control strategy is proposed for the intelligent fuel cell hybrid electric vehicles (FCHEV) platoon in a network-connected environment. The hierarchical control framework consists of upper speed control and lower power distribution. In the upper layer, the improved particle swarm optimization (PSO) algorithm is applied to calculate the global optimal speed trajectory, and then the model predictive control (MPC) is adopted for global speed trajectory tracking and self-adaptation, which can ensure the ego vehicle tracks the pre-calculated speed trajectory, and can re-plan the vehicle speed under the condition of safety priority when sudden disturbances occur in the foreground. The lower layer utilizes Q-learning (QL) to achieve power distribution between hybrid power sources, reducing the number of fuel cell starts and stops, slowing battery degradation and improving vehicle economy. Simulation results based on complex road conditions show that the proposed controller has good energy economy and tracking performance. Under the condition of the slope, the total platoon cost of the proposed strategy is reduced by 3.99% compared with the constant speed cruise strategy, and under the interference condition, the total platoon consumption cost of the proposed strategy decreased by 6.79% compared with adaptive cruise control (ACC). • A hierarchical strategy of IFCHEV platoon in complex conditions is proposed. • Speed planning is conducted using offline optimization with real-time adjustment. • Online adjustment adopts fixed headway to ensure platoon stability and safety. • Complex working conditions are considered to guarantee strategy adaptability. [ABSTRACT FROM AUTHOR]

This research focuses on a methodology for connecting the linear induction machine (LIM) using model predictive flux control (MPFC) fed by Kyocera solar cells. Additionally, it incorporates a fuzzy logic regulator (FLR) for maximum power point tracking (MPPT) control. The boost converter's characteristics are regulated using MPPT, allowing it to achieve a voltage gain of up to twice the input voltage. The finite state-model predictive flux control (FS-MPFC) is employed to synthesize the three-phase voltage source converter switching states. The paper proposes an improved FS-MPFC strategy for LIMs, eliminating the cost function's need for a weighting parameter. This is achieved by unifying its terms of traditional control methods and reducing the number of calculation steps. The validity and effectiveness of the introduced system are confirmed by extensive simulation results. The findings revealed that the system can accurately extract maximum power from the Kyocera solar cells and track the preset speed values. In addition, it can enhance performance by minimizing the thrust and primary flux under varying speed and load conditions. • The fuzzy logic regulator (FLR) is discussed to achieve MPPT. • A Linear Machine (LM) based on model predictive flux control (MPFC) fed by PV is provided. • The MPFC is proposed to avoid the WF and reduce the ripples compared to the MPTC method. • Extensive simulation analysis is performed in different cases via MATLAB-SIMULINK. [ABSTRACT FROM AUTHOR]

In this paper, an effective control and parameters identification scheme was proposed for high-precision trajectory tracking of space robotic manipulators. Our proposed control method employed a linear-extended-state-observer (LESO) based model predictive control (MPC) scheme. The feedback linearization approach was utilized to develop a model predictive controller. The optimization objective function was simplified to a standard quadratic programming (QP) problem for solution. To minimize the impact of the inaccuracy in the dynamics model, a LESO was designed to estimate the disturbance, and its convergence was demonstrated. In order to improve the accuracy of dynamic model, the physical consistency of inertial parameters was constrained using linear matrix inequality (LMI) techniques. Moreover, the dynamics parameters were estimated through semidefinite programming (SDP) techniques. To validate the effectiveness of the proposed control method, it was compared with existing controllers through simulations and experiments. The results demonstrated the superior performance of the proposed scheme, highlighting its potential for achieving high-precision trajectory tracking control in space robotic manipulators. • High-precision trajectory tracking control for space manipulator is investigated. • Feedback linearization based predictive model for MPC is established. • A LESO is designed and its convergence is proven. • A physical consistency parameters identification method is presented. • Both simulations and experiments are performed for validation. [ABSTRACT FROM AUTHOR]

This paper proposes a latent variable nonlinear iterative learning model predictive control method (LV-NILMPC) based on the dynamic fuzzy partial least squares (DFPLS) model to achieve trajectory tracking and process disturbance suppression in multivariable nonlinear batch processes. The dynamic and nonlinear characteristics of the physical system are constructed by integrating the T-S fuzzy model into the regression framework of the dynamic partial least squares (PLS) inner model. The decoupling and dimensionality reduction characteristics of the DFPLS model automatically decompose a multivariable nonlinear system into multiple univariate subsystems operating independently in the latent variable space. Based on the DFPLS model, we design LV-NILMPC controllers corresponding to each latent variable subspace to track the projection of the reference trajectories. Compared with the previous control method, the method proposed in this paper has a faster convergence rate and smaller tracking error. The method is suitable for nonlinear, multivariable and strong coupling batch processes. Finally, the application of two cases shows that the method is effective. • We extend the iterative learning model predictive control to nonlinear processes. • Partial least squares is employed to decouple the strongly coupled system. • The controller is designed in the latent variable space rather than the original space. • It has smaller tracking error and faster convergence speed than other methods. [ABSTRACT FROM AUTHOR]

Concentrated Solar Power plants (CSP) have the energy storage capability to generate electricity when sunlight is scarce. However, due to the highly non-linear dynamics of these systems, a simple linear controller will not be able to overcome the variable dynamics and multiple disturbance sources affecting it. In this paper, a deep Model Predictive Control (MPC) based on the Koopman operator is proposed and applied to control the Heat Transfer Fluid (HTF) temperature of a distributed-parameter model of the ACUREX solar collector field located at Almería, Spain. The Koopman operator is an infinite-dimensional linear operator that fully captures a system's non-linear dynamics through the linear evolution of functions of the state-space. However, one of the major problems is identifying a Koopman linear model for a non-linear system. Koopman eigenfunctions are involved in converting a non-linear model to a Koopman-based linear model. In this paper, a deep Long Short-Term Memory (LSTM) autoencoder is designed to calculate Koopman eigenfunctions of the solar collector field. The Koopman linear model is then used to design a linear MPC with terminal components to ensure closed-loop stability guarantees. Simulation results are utilized to show the satisfactory tracking performance of the proposed approach. [ABSTRACT FROM AUTHOR]

This paper introduces an integrated path tracking control strategy for autonomous vehicles. The proposed control strategy is based on a multi-input multi-output linear model predictive control (LMPC) with a fuzzy logic switching system. The designed MPC is based on Laguerre networks. The main target of the designed MPC is to produce the optimal control signals of the steering angle and the angular velocity while considering the physical constraints of the control signals and the measurements noise. Since the vehicle model is highly nonlinear and is operated over a wide range of operating points, different linearized models are obtained. The controller parameters for each linear model are designed and tuned. The gab metric analysis is used to select a number of these models to simplify the design of the proposed controller. Then, these models are combined using a fuzzy logic controller to switch between them. To test the proposed controller performance, different paths are generated using path planning algorithms. These paths simulate different vehicle maneuvers scenarios. The simulation results show that the designed tracking controller has a tracking performance on different designed paths better than that of a Linear quadratic gaussian (LQG) tracking controller, discussed in this paper. • MIMO model predictive control is designed based on Laguerre filter for autonomous vehicles. • Fuzzy system is used to smoothly switch between linearized models. • Gap-metric analysis is used to reduce the number of the linearized models. • Proposed multi-model controller perfectly tracks paths regardless their complexity. [ABSTRACT FROM AUTHOR]

In this paper, an optimized sliding surface predictive control of a three-phase voltage source inverter is introduced. In power electronics, the model predictive control method (MPC) is broadly used and applied to a wide range of energy conversion systems. However, analyzing the stability of the MPC is not a straightforward task, and Lyapunov-based approaches are used to examine the stability characteristics in most cases. MPC is a nonlinear control technique, and the traditional frequency-domain stability tools cannot be used to examine the closed-loop stability. Therefore, a poor design of the MPC without considering the stability may worsen the system performance. Even the norm choice of the objective function leads to closed-loop instability, for example, ℓ 1 norm is not a sufficient choice to guarantee the global asymptotical stability. Even though ℓ 1 norm offers a low computational burden during the online optimization process, the system may suffer from closed-loop instability. For all these reasons, a stability-guaranteed objective function design procedure is proposed in this paper. The proposed objective function selection process is based on the sliding-mode control theory. The objective function is reformulated as a sliding surface function, and the switching combination that satisfies the sliding mode control stability criteria is selected as an optimum input. The mathematical concepts are experimentally validated, and the results demonstrate the potency of the proposed strategy. • A novel stability guaranteed model predictive control method is presented. • The objective function is reformulated as a sliding surface function. • Steady-state performance is improved in three-phase voltage source inverter applications. • Comprehensive performance analyses are performed. • Theoretical concepts are experimentally validated using a real-hardware. [ABSTRACT FROM AUTHOR]

Solar photovoltaic thermal system (SPTS) can fully tap solar energy resources to realize thermal and electric supply for users simultaneously, but the volatility and uncertainty of renewable energy and load cause the imbalance of energy supply. This paper proposes a multi-time scale optimal scheduling method for SPTS based on event-triggered model predictive control (ET-MPC) considering battery performance deterioration. Firstly, SPTS is divided into day-ahead and day-in optimization layers from different time scales. Day-ahead scheduling takes the minimization economic cost as the optimization function to obtain pre-scheduling plan, and day-in scheduling adapts rolling optimization scheme based on model predictive control (MPC) to reduce power fluctuations and achieve optimal scheduling adjustment. Moreover, the battery performance decay parameter is introduced into the MPC model and embedded into the optimization problem to prolong the cyclic life deterioration, and an event-triggered mechanism is introduced into MPC to improve the computational efficiency and reduce the communication frequency. Finally, the results show that the proposed double-layer scheduling method improves 4.15 %, 66 % and 13.39 % respectively regarding cost, power fluctuation and battery maintenance, and improve the calculation efficiency of 48.89 % compared with standard MPC method, which indicates that the proposed method has good economy, stability and execution efficiency. • This paper proposed energy scheduling for solar photovoltaic thermal system (SPTS). • Double-layer optimal scheduling is proposed to realize the economy and stability. • Battery service time is prolonged through defining performance index. • An event-triggered mechanism is introduced to MPC to reduce the communication burden. • The proposed method improves performance from different perspectives. [ABSTRACT FROM AUTHOR]

The research on bipedal robots with limited foot area is gaining increasing attention. To tackle the challenge of dealing with unknown disturbances in the environment, the adjustment of footstep placement plays a vital role in maintaining stable motion during bipedal walking. This paper introduces an innovative approach based on the relationship between the Divergent Component of Motion (DCM) and footstep. It utilizes a DCM prediction model to optimize the optimal speed for recovering the foothold position. The goal is to enable quick and relevant footstep selection for bipedal robots, thereby facilitating the swift recovery of robot speed. The paper provides insights into the process of designing the desired DCM for achieving an optimal average walking speed without relying on predefined footstep sequences. By establishing a state equation between the DCM and footstep placement, this approach enables the prediction of multiple footstep positions within a fixed walking cycle, thereby facilitating the desired average motion speed. Extensive numerical simulations are conducted to compare the proposed method with various conventional footstep adjustment algorithms. The results emphasize our method's ability to converge more rapidly to the target speed, even with minor step adjustments. To validate the feasibility and robustness of the algorithm, we conduct experiments on the bipedal robot BHR-B2. These experiments further confirm the algorithm's effectiveness. Given its promising performance, this algorithm holds potential for applications in legged robots with point feet. • The adjustment of the footstep is crucial for the speed recovery of bipedal walking. • A multi-step prediction footsteps adjustment algorithm based on DCM is proposed. • The desired speed of motion is mathematically related to the target DCM state. • Three adjustment algorithms were compared through simulation. • Algorithms have been applied and effectively validated on a bipedal platform. [ABSTRACT FROM AUTHOR]

This paper introduces an innovative approach for achieving optimal energy management (OEM) in renewable-based hybrid microgrids (HMGs). The HMGs exhibit enhanced efficiency, minimizing power loss while maximizing generated power, making them advantageous over traditional MGs. This new approach consists of a modified model predictive control based improved Firefly1to3 algorithm (MMPC-IFA1to3). Here, MMPC adeptly regulates converter switching phenomena, ensuring stability during uncertain conditions in HMG. On the other hand, FA1to3 states that for each member movement of a firefly population, occurs towards three members. Here, the improved nature of FA1to3 is achieved by using the chaotic function to fine-tune the regulation parameter. This unified method generates the best power references for both active and reactive powers, making switching operations in voltage source converters simpler. This paper mainly emphasizes active and reactive power management through objective function minimization. The proposed IFA1to3 approach effectively incorporates constraints to minimize costs, ensure power availability, and mitigate voltage deviations in renewable-based HMGs. Moreover, charging/discharging of energy storage devices and power exchange between the utility grid and DC MG are carried out through the MMPC-IFA1to3 algorithm by monitoring active and reactive loads simultaneously. The corresponding converters are modeled based on the bidirectional power flow to cope with active and reactive power management. Further, the robustness of the proposed approach is verified under different operating conditions, and outcomes are compared with benchmark techniques in a MATLAB/Simulink environment to evidence its superior EM in HMG. Finally, the proposed MMPC-IFA1to3 approach is validated in a real-time environment through the dSPACE DS 1104 embedded processor to evidence its industrial applications through its robustness and OEM during various case studies. • In the hybrid MG, MPC regulates converter of the connected sources, while MMPC controls the VSC converter. • A novel FA, combining FA1to3 with chaotic logistic function, optimizes power references generation in this study. • Proposed MMPC-IFA1to3 approach ensures voltage stability and effectively manages ARP during uncertainties in MG. • The proposed IFA1to3 is more effective for multi-objective functions optimization. • Real-time validation of the proposed approach in dSPACE DS1104 evidences its practical applicability. [ABSTRACT FROM AUTHOR]

This research paper presents a novel long-horizon move-blocking based direct power model predictive control (DPMPC) strategy, uniquely designed to enhance the dynamic performance of boost converters in the grid-connected mode within DC microgrids. A precise dynamic model is developed considering a boost converter connected to a DC-link supplying constant power and resistive loads. To predict the system's dynamic behavior over an extended interval and enhance its performance in the presence of constant power loads, the long-horizon based finite control set model predictive control (FCS-MPC) method is introduced. To address the computational complexity associated with the conventional long-horizon DPMPC approach, a move-blocking (MB) strategy is incorporated into FCS-MPC, reducing the computational burden while improving the dynamic performance of the boost converter. Unlike recent studies that utilize DPMPC with short prediction interval, this paper presents evidence that a longer prediction interval is crucial for maintaining system stable and enhancing the dynamic response and also utilizing MB to make the control implementable in the real-time. The simulation results conducted using MATLAB/Simulink demonstrate the effective performance of the proposed strategy across various operating conditions of the boost converter. Experimental results further validate the strategy's capability to enhance the dynamic performance of the boost converter. Importantly, the experimental findings confirm the feasibility of implementing the proposed long-horizon move-blocking DPMPC strategy in real-time applications. • Developing an accurate dynamic model for a boost converter. • Proposing a long-horizon direct power model predictive control strategy. • Limiting the number of calculations and the execution time of the proposed strategy using move-blocking approach. [ABSTRACT FROM AUTHOR]

Optimal energy management of microgrids enables efficient integration of renewable energies by considering all system flexibilities. For systems with significant seasonal imbalance between energy production and demand, it may be necessary to integrate seasonal storage in order to achieve fully decarbonized operation. This paper develops a novel model predictive control strategy for a renewable microgrid with seasonal hydrogen storage. The strategy relies on data-based prediction of the energy production and consumption of an industrial power plant and finds optimized energy flows using a digital twin optimizer. To enable seasonal operation, incentives for long-term energy shifts are provided by assigning a cost value to the storage charge and adding it to the optimization target function. A hybrid control scheme based on rule-based heuristics compensates for imperfect predictions. With only 6% oversizing compared to the optimal system layout, the strategy manages to deliver enough energy to meet all demand while achieving balanced hydrogen production and consumption throughout the year. • PV with seasonal H 2 storage enables fully sustainable operation of industrial microgrids. • Model predictive control overcomes perfect foresight limitation in energy management. • H 2 production for long-term storage is incentivized by adding an extra cost term. • Hybrid predictive control enables a low cost system with nearly perfect yearly operation. [Display omitted] [ABSTRACT FROM AUTHOR]

This paper presents a novel control strategy that provides active disturbance rejection predictive control on constrained systems with no nominal identified model. The proposed loop relaxes the modelling requirement to a fixed discrete-time state–space realisation of a first-order plus integrator plant despite the nature of the controlled process. A third-order discrete Extended State Observer (ESO) estimates the model mismatch and assumed plant states. Moreover, the constraints handling is tackled by incorporating the compensation term related to the total perturbation in the definition of the optimisation problem constraints. The proposal merges in a new way state–space Model Predictive Control (MPC) and Active Disturbance Rejection Control (ADRC) into an architecture suitable for the servo-regulatory operation of linear and non-linear systems, as shown through validation examples. • Active disturbance rejection is merged with state–space model predictive control. • System information is relaxed to a second-order general integral assumed plant. • The compensation term is used to reformulate the optimisation problem constraints. • Suitable for linear and nonlinear benchmarks with no nominal model. • System constraints are satisfied, and disturbance rejection is enhanced. [ABSTRACT FROM AUTHOR]

This paper aims to discuss and analyze the potentialities of Recurrent Neural Networks (RNN) in control design applications. The main families of RNN are considered, namely Neural Nonlinear AutoRegressive eXogenous, Echo State Networks, Long Short Term Memory, and Gated Recurrent Units. The goal is twofold. Firstly, to survey recent results concerning the training of RNN that enjoy Input-to-State Stability (ISS) and Incremental Input-to-State Stability (δ ISS) guarantees. Secondly, to discuss the issues that still hinder the widespread use of RNN for control, namely their robustness, verifiability, and interpretability. The former properties are related to the so-called generalization capabilities of the networks, i.e. their consistency with the underlying real plants, even in presence of unseen or perturbed input trajectories. The latter is instead related to the possibility of providing a clear formal connection between the RNN model and the plant. In this context, we illustrate how ISS and δ ISS represent a significant step towards the robustness and verifiability of the RNN models, while the requirement of interpretability paves the way to the use of physics-based networks. The design of model predictive controllers with RNN as plant's model is also briefly discussed. Lastly, some of the main topics of the paper are illustrated on a simulated chemical system. [ABSTRACT FROM AUTHOR]

The prediction of the occupancy in buildings is essential to design efficient energy control strategies that optimize consumption and reduce losses while guaranteeing the comfort of the occupants. For this reason, many works address the problem of detecting, estimating, and predicting buildings' occupancy using different techniques, devices, and technologies. The occupancy prediction process can be described in four stages: data acquisition, modeling, evaluation, and testing, which are closely related. This paper reviews the most relevant recent literature on building occupancy estimation and prediction, analyzing the key aspects of its stages. A detailed description of the variables and design considerations is presented, including measurement methods, sensor selection, modeling techniques, evaluation metrics, and different applications. Through its examination, this paper elaborates significant remarks on the interaction between the stages, providing an overview of the suitable design of the occupancy prediction process. Finally, current and future trends are discussed. • A systematic review of the occupancy estimation and prediction process is presented. • Data acquisition, modeling, evaluation, and testing are the four general stages. • The importance of sensor fusion in overcoming individual limitations is presented. • Occupancy detection methods include deterministic, stochastic, and machine learning. • Some potential future research directions are discussed. [ABSTRACT FROM AUTHOR]

With the increasing complexity of the power grid, the precision of the thermal power units' execution of automatic generation control (AGC) commands is gradually increasing the impact on the online regulation of the power grid. The deviation between the actual output of thermal power units and the AGC command of the grid will not only affect the consumption of new energy output, but also endanger the safe operation of the grid. This paper introduces "deep learning" technology to solve the problem. Firstly, an AGC command execution effect identification and confidence evaluation algorithm (ACEEI-CEA) is proposed. The algorithm builds a neural network model to accurately predict the unit output and the confidence evaluation of the prediction results. Next, a high-dimensional input preprocessing strategy based on variational autoencoder (VAE) is proposed to reduce the dimensionality of the model input attributes, improving the convergence and accuracy of the model. Finally, an AGC optimal command fast inversion solution method (AOCFISM) is designed. This method transforms the unit output deviation problem into an objective optimisation problem. And improve the efficiency of solving the optimal AGC command value by constraining the unit command value. The calculation results show that the error of the prediction results of the model proposed in this paper is 5% lower than that of the traditional neural network. The difference between the output value of the optimal AGC command and the expected output value obtained is less than 0.5 MV, which can support AGC online decision-making.© 2017 Elsevier Inc. All rights reserved. • An AGC command execution effect identification and confidence evaluation algorithm (ACEEI-CEA) is proposed. • Results show the ACEEI-CEA performs well in command prediction compared to the state-of-the-art methods. • ACEEI-CEA achieves confidence assessment of predicted values. • The pre-processing strategy based on variational autoencoder shortens the training time of the model. • The AGC optimal command fast inversion solution method (AOCFISM) can obtain the AGC optimal command value. [ABSTRACT FROM AUTHOR]

Deep neural networks have revolutionized many fields, including image processing, inverse problems, text mining and more recently, give very promising results in systems and control. Neural networks with hidden layers have a strong potential as an approximation framework of predictive control laws as they usually yield better approximation quality and smaller memory requirements than existing explicit (multi-parametric) approaches. In this paper, we first show that neural networks with HardTanh activation functions can exactly represent predictive control laws of linear time-invariant systems. We derive theoretical bounds on the minimum number of hidden layers and neurons that a HardTanh neural network should have to exactly represent a given predictive control law. The choice of HardTanh deep neural networks is particularly suited for linear predictive control laws as they usually require less hidden layers and neurons than deep neural networks with ReLU units for representing exactly continuous piecewise affine (or equivalently min–max) maps. In the second part of the paper we bring the physics of the model and standard optimization techniques into the architecture design, in order to eliminate the disadvantages of the black-box HardTanh learning. More specifically, we design trainable unfolded HardTanh deep architectures for learning linear predictive control laws based on two standard iterative optimization algorithms, i.e., projected gradient descent and accelerated projected gradient descent. We also study the performance of the proposed HardTanh type deep neural networks on a linear model predictive control application. [ABSTRACT FROM AUTHOR]

In this paper, a novel two-loop model predictive control (TLMPC) is proposed to enhance the dynamic performance of induction motors used in rolling mill applications. In such applications, two individual voltage source inverters feed induction motors which are connected to the grid in a back-to-back manner. The grid-side converter, responsible for controlling the DC-link voltage, plays a vital role in the dynamic performance of the induction motors. Its undesired performance deteriorates the speed control of induction motors, which is a crucial need in the rolling mill industry. The proposed TLMPC includes a short-horizon finite set model predictive control in the inner loop to control the power flow by finding the best switching state of the grid-side converter. Additionally, a long-horizon continuous set model predictive control is developed in the outer loop for adjusting the set point value of the inner loop by predicting the DC-link voltage in a limited time horizon. An identification approach is exploited to approximate the non-linear model of the grid-side converter in order to use it in the outer loop. The mathematical proof of the robust stability of the proposed TLMPC is provided and its real-time execution is also certified. Finally, the capability of the proposed approach is evaluated using MATLAB/Simulink. A sensitivity analysis to evaluate the effect of the model's inaccuracy and uncertainties on the performance of the proposed strategy is also provided. [Display omitted] • Proposing a novel TLMPC scheme for optimally adjusting the DC-link voltage set point. • Identifying the inner loop, and extracting a prediction model for the outer loop. • Guaranteeing the robust stability and real-time execution of the proposed TLMPC. [ABSTRACT FROM AUTHOR]

For real-used proton exchange membrane fuel cells (PEMFC), it is critical to design an effective controller and evaluate its performance. Current evaluations of controllers are often empirical or qualitative, and quantitative evaluation methods are lacking. In this paper, the quantifiable objective evaluation method is proposed for assessing the controller performance, including optimal control, adaptive control, variable structure control, and model-based control, aiming at the oxygen excess ratio. In the method, the anti-starvation, transient-state, steady-state, and multiple load-changing performances are comprehensively considered through weighting, rating, and especially the introduction of negative scores through the integration of four independent indexes. The importance and effectiveness of evaluation method are verified through the specific analysis of four controllers and the internal states of PEMFC. Besides, the evaluation method can be extended appropriately, such as considering the robustness, optimal output power, and other practical problems, which is significant for the development of PEMFC system controller. • Quantifiable method assessing effect of four controllers, superior to empirical one. • Method rates and weights the performance by four newly proposed indexes. • Method balances anti-starvation, transient, steady, and multi-load-changing. • Optimal PID performs best among fuzzy, robust, and model based control unexpectedly. • Method is beneficial for controller selection in real design of fuel cell controller. [ABSTRACT FROM AUTHOR]

Integrated energy systems (IES) with renewable energy systems (RES), carbon capture systems (CCS) and energy storage systems (ESS) are considered efficient in supporting the low-carbon energy supply with both economic and environmental benefits. Effective energy management is required to ensure the economical, environmental and reliable operation of the IES. However, the optimal IES operation is considered a non-trivial task due to the renewable generation uncertainty and the optimization of multiple contradictory objectives (e.g. economic, environmental and risk costs). This paper aims to provide a multi-level optimization model for the real-time optimal IES operation consisting of RES, ESS and CCS. This work quantifies the uncertainty by the Conditional Value at Risk (CVaR) theory in the optimization model. The uncertainty is further reduced by improving the operation strategy through a model predictive control (MPC)-based method. Also, the multi-objective optimization model is adopted to minimize the economic cost, carbon dioxide emissions (CDE) and primary energy consumption (PEC) for optimal energy scheduling in the intra-day stage. Based on the result of the intra-day stage, the feedback correction model is applied to adjust the schedule to balance the difference between the forecasting and actual values. Numerical results show that the proposed solution can provide the trade-off between economical and environmental performance. Through ablation experiments, the proposed method with feedback correction can carry out demand response with lower costs, CDE and PEC. The proposed solution is further confirmed with outperformed performance compared with single-objective optimization methods and other stochastic optimization methods. In addition, a robustness analysis is conducted to quantify the benefits of RES, ESS and CCS in IES. [ABSTRACT FROM AUTHOR]

High performance of HVAC connected Building Integrated Photovoltaic/Thermal (BIPV/T) systems relies on appropriate control. However, optimal control is often overlooked, resulting in systems that operate inefficiently. This paper investigates how model predictive control (MPC) can improve the operation of open loop air-based BIPV/T systems connected to multiple thermal applications. The BIPV/T system at the first institutional net-zero energy building in Canada, the Varennes library, is used as an archetype. The BIPV/T covers 16% of the south-facing roof and operates under a simple rule-based control strategy. The developed control and design strategies consider variations of this system, to achieve higher thermal utilization efficiency. A control-oriented BIPV/T model is developed and calibrated using monitored data. The BIPV/T airflow is regulated through MPC for simultaneous heat supply to an Energy Recovery Ventilator and air-to-water heat pump. The BIPV/T air flow is efficiently controlled, considering the connected thermal applications, environmental conditions, and PV temperature. Model-based control for BIPV/T systems can increase the amount of useful heat and reduce PV overheating. The MPC controller for the examined system reduced the building energy consumption compared to the business-as-usual operation by 40% and together with increased BIPV/T area can supply excess heat to adjacent buildings. [ABSTRACT FROM AUTHOR]

This paper presents a robust decoupling Model Predictive Controller (MPC) for constrained linear systems with bounded disturbances. The usual explicit decouplers are combined with a suitable augmented state–space representation in order to ensure recursive feasibility, robust constraint satisfaction, and cross-coupling reduction during set-point changes. The proposed approach is flexible with respect to the decoupler choice and the robust MPC algorithm. A robust MPC for tracking piecewise constant references with an artificial target based on nominal prediction is proposed in order to achieve offset-free piecewise constant reference tracking. Two case studies are presented to illustrate the effectiveness of the proposed decoupling strategy. • A robust decoupling MPC for tracking piecewise constant references has been proposed. • A new augmented model is used to provide a decoupled set-point tracking response. • Recursive feasibility and constraint satisfaction are ensured despite disturbances. [ABSTRACT FROM AUTHOR]

In this study, an observer-based model predictive control (MPC) algorithm is addressed for an uncertain discrete-time nonlinear networked control system (NCS) subject to hybrid malicious attacks by using interval type-2 Takagi–Sugeno (IT2 T–S) fuzzy theory. Hybrid malicious attacks, including two typical attacks, i.e., denial-of-service (DoS) attacks and false data injection (FDI) attacks, are considered in the communication networks. Under DoS attacks, the control signals will be interfered, which cause the degradation of signal-to-interference-plus-noise ratio, then lead to packets loss. Under FDI attacks, the false signals are injected and output signals are modified so that the system performance is deteriorated. For the NCS subject to hybrid attacks, a secure observer that can resist FDI attacks is devised and a fuzzy MPC algorithm that can solve the controller gains is proposed. Besides, by updating the bound of augmented estimation error, the recursive feasibility can be guaranteed. Finally, illustrative examples are given to show the effectiveness of proposed scheme. • The IT2 T–S fuzzy MPC scheme for NCS subject to uncertainties and hybrid attacks is investigated in this paper. • The uncertainties for the NCS appear not only in the membership functions but in both state and input matrices. • The hybrid attacks (DoS attack and FDI attack) in the communication channels of the NCS are considered. • The probability of packets loss depending on SINR is given and a secure observer is designed to estimate system states and FDI attacks. • By optimizing the performance objective function, an on-line fuzzy MPC algorithm is presented. [ABSTRACT FROM AUTHOR]

• The TM capture is first reformulated to nonlinear quadratic optimal control. • The high-efficiency NMPC with robustness and optimality is proposed for TM capture. • The mixed state and control constraints are integrated into the robust optimal control. • The proposed method guarantees the computational efficiency. This paper presents a nonlinear high-efficiency model predictive control (NHMPC) with constraints designed for the test mass (TM) capture phase of the drag-free satellite about gravitational wave observatory. To avoid collisions between test mass and satellite cavity, TM capture is the essential technology for drag-free satellite. Test mass is located inside an electrostatic suspension and locked by a clamp mechanism initially. The test masses are released with high initial offsets and velocities when the mechanism is retracted. The purpose of this phase is to guarantee the TM to be positioned at the cage center and attitude aligned with the local cage frame. Due to the low actuation authority of electrostatic suspension along with critical initial offsets and velocities, it is a challenging task to design the attitude and translation control schemes with simultaneous consideration of system performance and energy consumption. For the problem given above, the TM capture can be reformulated into a nonlinear quadratic optimal control problem with the state and input constraints. A nonlinear model predictive control (NMPC) structure is also designed to handle the noises by forming a closed loop. This control framework can realize optimality and robustness in a compromise. To improve the speed of solving high-dimensional nonlinear optimal control online for MPC, the indirect Chebyshev pseudospectral method with constraints is employed. The convergence and stability of the control loop are analyzed. Simulation examples show that the feasibility and performance of the designed control loop are verified compared with the existing methods and computation times in the millisecond range can be achieved. [ABSTRACT FROM AUTHOR]

In this paper, a new topology with two shunts flexible AC transmission system (FACTs) devices, thyristor binary switched capacitors (TBSC), and thyristor binary switched reactors (TBSR) based SVC has been developed, which are working in parallel. Both TBSC and TBSR are designed, and simulation results are obtained for the dynamic loading condition. Switching of capacitor and reactor banks with thyristor as a switch is obtained at transient-free conditions so that the significant problem of switching harmonics is eliminated. The coarse control of reactive power is obtained by selecting switchable capacitor banks in binary mode. For fine control, the TBSR bank is designed with a total value equal to the lowest step value of the TBSC bank. It is discrete stepwise compensation at a period of one cycle, and this reactive power compensation is almost continuous as per requirement. It has near-to-zero switchings and zero steady-state harmonics. The mathematical model of TBSC + TBSR has been identified with the system identification toolbox. Different control strategies are implemented as PID controller, Model predictive control, and Model reference adaptive control. The proposed SVC topology performance is discussed based on the performance parameters such as rise time, settling time, and peak overshoot using adaptive controllers. [ABSTRACT FROM AUTHOR]

Efficient energy production and consumption are fundamental points for reducing carbon emissions that influence climate change. Alternative resources, such as renewable energy sources (RESs), used in electricity grids, could reduce the environmental impact. Since RESs are inherently unreliable, during the last decades the scientific community addressed research efforts to their integration with the main grid by means of properly designed energy storage systems (ESSs). In order to highlight the best performance from these hybrid systems, proper design and operations are essential. The purpose of this paper is to present a so-called model predictive controller (MPC) for the optimal operations of grid-connected wind farms with hydrogen-based ESSs and local loads. Such MPC has been designed to take into account the operating and economical costs of the ESS, the local load demand and the participation to the electricity market, and further it enforces the fulfillment of the physical and the system's dynamics constraints. The dynamics of the hydrogen-based ESS have been modeled by means of the mixed-logic dynamic (MLD) framework in order to capture different behaviors according to the possible operating modes. The purpose is to provide a controller able to cope both with all the main physical and operating constraints of a hydrogen-based storage system, including the switching among different modes such as ON, OFF, STAND-BY and, at the same time, reduce the management costs and increase the equipment lifesaving. The case study for this paper is a plant under development in the north Norway. Numerical analysis on the related plant data shows the effectiveness of the proposed strategy, which manages the plant and commits the equipment so as to preserve the given constraints and save them from unnecessary commutation cycles. • A hydrogen-based ESS model includes several degradation costs of the electrolyzer and fuel cell. • Degradation costs concern working cycles and life spans of the electrolyzer and fuel cell. • Features, like warm and cold start, are considered through continuous and logical states combined. • An MPC controller minimizes the logical state switching between different operating modes. • The controller meets local load demand and maximizes revenue in the electricity market. [ABSTRACT FROM AUTHOR]

Dividing wall distillation columns (DWDCs) are effective technologies for distillation process intensifications. Azeotropic DWDCs (ADWDCs) are effective process intensification technologies to further intensify distillation process through combination of azeotropic distillation (AD) with DWDC. Investigations on the controllability and operation ability of ADWDC are mainly employing the traditional proportional–integral (PI) control schemes. Because of the inherent complicated configuration of ADWDC, the PI control leads to relatively poor dynamic performances. So as to enhance the dynamic performances, composition control scheme employing the advanced model predictive control (MPC) for an ADWDC separating furfural and water is studied in this paper. Although there are several studies employing MPC for DWDC, few investigation has been done on using MPC for ADWDC which is more interactive than DWDC. The dynamic results obtained in this paper using MPC are compared with the corresponding PI control scheme. Although the steady-state deviations and numbers of oscillations are similar employing PI control and MPC, the settling time and maximum deviations are smaller using MPC. MPC can considerably suppress the maximum deviation in face of +20% feed flow rate disturbance from -1.449% to -0.013%, and the corresponding settling time is reduced from 4.28 h to 2.20 h. These prove that MPC is more suitable than PI control for ADWDC. This paper provides new effective control scheme for the complex and highly interactive ADWDC, and verifies that MPC is a promising method for ADWDC to provide better dynamic performances with reduced maximum deviations and shortened settling time. • This paper verified the feasibility of model predictive control (MPC) for azeotropic dividing wall distillation column (ADWDC). • MPC provides better dynamic performances than PI control with reduced maximum deviations and shortened settling times. • MPC is a promising method for the complex and highly interactive ADWDC. [ABSTRACT FROM AUTHOR]

This paper presents a two-layer control structure to address parameter uncertainty within a plant. The lower layer is formulated as a nominal MPC that computes control actions to regulate the underlying plant, and the upper layer computes optimal set-point trajectories for the lower level to track. The upper layer is formulated as an optimization problem that takes into account the closed-loop behavior of uncertain plant scenarios under the action of nominal MPC. The upper layer facilitates the lower layer in avoiding constraint violations and producing less conservative control actions by assigning time-varying set-point trajectories to the nominal MPC. The benefits of this approach are illustrated through application to linear single-input–single-output transfer function case studies, and a nonlinear multi-input multi-output evaporator process. • The paper presents a novel two-layer robust MPC paradigm. • An upper level optimization problem computes set-point trajectories for nominal MPC. • Utilizes predicted closed-loop response of plant scenarios under constrained MPC. • Performance is demonstrated through application to linear and nonlinear case studies. [ABSTRACT FROM AUTHOR]

Ship manoeuvrability of Maritime Autonomous Surface Ships (MASS) revolutionise the maritime industry. However, this paradigm shift necessitates the advancement of manoeuvring control models to meet the complex demands of autonomous navigation. This paper addresses the need for an improved manoeuvring control model for MASS, particularly concerning path planning and tracking in the presence of wave loads. The paper establishes a comprehensive mathematical model for ship manoeuvring, considering forces acting on the ship's hull, propellers, rudders, and wave loads. A time optimisation model using a spatial reformulation approach is introduced. A nonlinear Model Predictive Control (MPC) model is presented for path planning and tracking, with a case study investigating the influence of wave load and comparing two control strategies. 10%–20% of time consumption increases if the wave load exists. This research bridges the gap in existing literature by incorporating wave loads into MPC-based control models for MASS. The findings shed light on the significance of wave loads in ship manoeuvring and provide valuable insights into effective control strategies for autonomous vessels operating in real-world sea conditions. • Introducing wave load to MPC-based path planning and trajectory tracking. • Analysing impact of wave loads to manoeuvring time. • Comparing the pros and cons of two manoeuvring control strategies under wave. [ABSTRACT FROM AUTHOR]

The problem addressed in this work is the enhancement of energy efficiency in buildings, primarily focused on heating control. The paper highlights the importance of addressing this challenge, given that HVAC systems are responsible for over 60% of total energy consumption in buildings, making them the largest energy consumers in both the residential and nonresidential sectors. This paper presents a methodology for developing a model predictive control (MPC) system based on artificial neural network (ANN) models, with the aim of improving the energy efficiency of buildings. This approach involves the creation of a training dataset using dynamic thermal simulation tools, enabling the learning of the ANN model that is responsible for predicting future states within an MPC optimization loop. Additionally, the implementation leverages advanced technologies such as Field-Programmable Gate Arrays (FPGAs), specifically the PYNQ board, Wi-Fi modules, and MQTT communication to enhance scalability, deployment, and adaptability. The study evaluates the proposed Neural Network Model Predictive Control (NNMPC) strategy implemented on the PYNQ, reporting a substantial reduction of 38.53% in heating energy consumption in buildings compared to a basic On/Off control approach for 24-hour simulation. This outcome underscores the solutions significant potential for energy savings in building heating systems. [ABSTRACT FROM AUTHOR]

• In many works, applied methods neglect control input and system constraints, which may impact global stabilization if these are not adequately handled. In this paper, the NMPC optimizes the behaviours of the system while ensuring the system constraints and improving the performance of the dual-arm robot. • Eliminating the impacts of external noise and imprecise models on the system performance through the RBFNN and facilitating the process of predicting the optimal behaviours for the NMPC. • Ensuring global stability and the convergence of RBFNN through the updating law based on the Lyapunov stability-taking contraction conditions designed based on the sliding mode controller. • Providing the pseudo-physical model and the comparison of the proposed control strategy with NMPC-RBF and SMC-RBF to highlight the effectiveness and feasibility of this control method. [Display omitted] This paper proposes a Lyapunov-based nonlinear model predictive control (LMPC) - based on adaptive Lyapunov to solve existing problems in nonlinear dual-arm systems such as system constraints and unknown external disturbances. In practice, the constraints tend to adversely affect the system's performance and stability. The nonlinear model predictive control NMPC is considered a promising candidate for handling system constraints while enhancing the robustness of the system. However, the rigour of the modeling procedure has a significant influence on the execution of the NMPC, system convergence cannot be assured in the face of modeling uncertainty. To solve this problem, the proposed controller takes into account external disturbances and unidentified parameters by using an adaptive mechanism constructed via the Radial Basis Function Neural Network (RBFNN). Furthermore, the dominant problem of the NMPC algorithm is the system stability which is considered by the Lyapunov theory backbones by a nonlinear Sliding Mode Control (SMC). The numerical simulations are carried out based on a pseudo-physical model to show the efficiency of the proposed control method. [ABSTRACT FROM AUTHOR]

The expected increase of hydrogen fuel cell vehicles has motivated the emergence of a significant number of studies on Hydrogen Refuelling Stations (HRS). Some of the main HRS topics are sizing, location, design optimization, and optimal operation. On-site green HRS, where hydrogen is produced locally from green renewable energy sources, have received special attention due to their contribution to decarbonization. This kind of HRS are complex systems whose hydraulic and electric linked topologies include renewable energy sources, electrolyzers, buffer hydrogen tanks, compressors and batteries, among other components. This paper develops a linear model of a real on-site green HRS that is set to be built in Zaragoza, Spain. This plant can produce hydrogen either from solar energy or from the utility grid and is designed for three different types of services: light-duty and heavy-duty fuel cell vehicles and gas containers. In the literature, there is a lack of online control solutions developed for HRS, even more in the form of optimal online control. Hence, for the HRS operation, a Model Predictive Controller (MPC) is designed to solve a weighted multi-objective online optimization problem taking into account the plant dynamics and constraints as well as the disturbances prediction. Performance is analysed throughout 210 individual month-long simulations and the effect of the multi-objective weighting, prediction horizon, and hydrogen selling price is discussed. With the simulation results, this work shows the suitability of MPC for HRS control and its significant economic advantage compared to the rule-based control solution. In all simulations, the MPC operation fulfils all required services. Moreover, results show that a seven-day prediction horizon can improve profits by 57% relative to a one-day prediction horizon; that the battery is under-sized; or that the MPC operation strategy is more resolutive for low hydrogen selling prices. [Display omitted] • Model Predictive control application for green hydrogen refuelling station. • Linear modelling of green hydrogen refuelling station. • Parameter sensitivity analysis of the multi-objective function. • Prediction horizon sensitivity analysis. • Model Predictive Control performance advantage compared to rule-based control. [ABSTRACT FROM AUTHOR]

This paper presents a secure model predictive damping control strategy for the wide-area power system (WAPS) under the DoS/deception attacks. Firstly, the model of WAPS under denial of service (DoS) attacks and deception attacks with the event-triggered mechanism (ETM) is established. Then, a wide-area model predictive damping controller is designed to damp out the interarea oscillations in the WAPS induced by cyber-attacks. Wide-area event-based model predictive damping control (WEMPDC) is proposed, by employing the wide-area information, the ETM is utilized to determine WEMPDC to defend against the cyber-attacks, in the meantime input-to-state stable in probability (ISSiP) of the closed-loop system is guaranteed. Furthermore, the validity of the WEMPDC is investigated by two-area four-machine and 16-machine 68-bus system. • A wide-area model predictive damping controller is designed to the WAPS induced by cyber-attacks. • Wide-area event-based model predictive damping control (WEMPDC) is proposed by employing the wide-area information. • The ETM is utilized to determine WEMPDC to defend against the cyber-attacks. [ABSTRACT FROM AUTHOR]

Collision avoidance is a priority task for ensuring the safety of a maritime transportation system. However, for a ship towing system, which is characterized by multiple vessels and physical connections, the research works about collision avoidance is limited. Thus, this paper proposes a speed and heading control-based conflict resolution of a ship towing system for collision avoidance. Two systems compose the core of the proposed conflict resolution: the risk assessment system and the coordination control system. The risk assessment is to identify the conflict and determine the time of avoiding action by calculating the index of conflict and the available maneuvering margin. The coordination control is based on the model predictive control (MPC) strategy to cooperatively control two tugboats for regulating the position, heading, and speed of the manipulated ship. Simulation experiments show that according to the index of conflict, the time cost, and the fuel consumption, a selected operation of combined heading and speed can be recommended for a ship towing system to provide a safer and more efficient towage manipulation. • Formulate the risk assessment criteria for a multi-vessel towing system. • Propose an MPC-based control method for a towing system to regulate multi-objectives. • Provide an optimal collision avoidance operation for a multi-vessel towing system. [ABSTRACT FROM AUTHOR]

• A data-driven optimization-based Model Predictive Control (MPC) formulation for the connected and automated Eco-driving on signalized arterials. • Mathematical formulations of location-based traffic control devices and road geometry constraints using the detailed information of interrupted flow facility locations such as traffic signals from High-Definition (HD) maps. • An online learning-based method to predict uncertain driving states using streaming connected vehicle data. • Model reformulations for tractable solutions based on strong duality theory and Semidefinite Relaxation technique. • Performance evaluation of the proposed connected and automated Eco-driving model using real-world data showing the capability of improving energy efficiency. For energy-efficient Connected and Automated Vehicle (CAV) Eco-driving control on signalized arterials under uncertain traffic conditions, this paper explicitly considers traffic control devices (e.g., road markings, traffic signs, and traffic signals) and road geometry (e.g., road shapes, road boundaries, and road grades) constraints in a data-driven optimization-based Model Predictive Control (MPC) modeling framework. This modeling framework uses real-time vehicle driving and traffic signal data via Vehicle-to-Infrastructure (V2I) and Vehicle-to-Vehicle (V2V) communications. In the MPC-based control model, this paper mathematically formulates location-based traffic control devices and road geometry constraints using the geographic information from High-Definition (HD) maps. The location-based traffic control devices and road geometry constraints have the potential to improve the safety, energy, efficiency, driving comfort, and robustness of connected and automated driving on real roads by considering interrupted flow facility locations and road geometry in the formulation. We predict a set of uncertain driving states for the preceding vehicles through an online learning-based driving dynamics prediction model. We then solve a constrained finite-horizon optimal control problem with the predicted driving states to obtain a set of Eco-driving references for the controlled vehicle. To obtain the optimal acceleration or deceleration commands for the controlled vehicle with the set of Eco-driving references, we formulate a Distributionally Robust Stochastic Optimization (DRSO) model (i.e., a special case of data-driven optimization models under moment bounds) with Distributionally Robust Chance Constraints (DRCC) with location-based traffic control devices and road geometry constraints. We design experiments to demonstrate the proposed model under different traffic conditions using real-world connected vehicle trajectory data and Signal Phasing and Timing (SPaT) data on a coordinated arterial with six actuated intersections on Fuller Road in Ann Arbor, Michigan from the Safety Pilot Model Deployment (SPMD) project. [ABSTRACT FROM AUTHOR]