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This paper proposes a novel stable economic model predictive control (EMPC) strategy for constrained nonlinear systems with changing economic criteria. The traditional EMPC may lead to infeasibility and even instability of the closed-loop system when the criterion has been changed. Firstly, a generalised terminal constraint is introduced to ensure the recursive feasibility of the economic optimisation problem, which enforces the terminal states to converge to an arbitrary equilibrium state rather than a predetermined fixed one at the initial time. Then the stability constraint is constructed by solving an auxiliary optimisation problem online, which is the key to guaranteeing the asymptotic stability of the closed-loop system. Finally, sufficient conditions for feasibility and asymptotic stability are derived in the context of changing economic criteria. The main feature of the proposed strategy is that it does not need to know the changes of economic criterion in advance, and its properties and effectiveness are exemplified by simulations on a nonlinear chemical process example. [ABSTRACT FROM AUTHOR]

This paper explores event-triggered model predictive control (MPC) for constrained switched systems, incorporating a buffer-based anti-attack (BBAA) approach to ensure network security. Since the system state is unknown and disturbed, an observer-based $ H_{\infty } $ H ∞ MPC approach is used to estimate the system state and suppress disturbance. Furthermore, the estimated state and control input can be optimised by minimising the performance function. Considering that the system state is subject to denial of service (DoS) attacks in the feedback channel, the BBAA approach is used to reconstruct the estimated state, thereby reducing the impact of attacks on system performance. To improve the utilisation of network resources, a set of event-triggering mechanisms (ETMs) is used. In addition, a set of matrix inequality conditions is given to constrain the gains of controllers and observers. Then, the system $ H_{\infty } $ H ∞ performance is demonstrated by applying the average dwell time (ADT) and the Lyapunov function method, and the complex coupling between model switching, DoS attacks and event-triggering is analysed. Simulation results validate the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

This paper proposes an optimal lane-changing and control strategy to solve the collision avoidance of intelligent vehicles in highway driving. A quintic polynomial lane-changing trajectory model is established in the Frenet frame, and the trajectory planning problem is transformed into the optimisation problem of lane-changing target coordinates. To consider the multiple environmental factors on vehicle lane-changing, the feasible region of the lane-changing target point was generated based on the lateral acceleration limitation and the safe distance constraints. The performance evaluation indexes were established, considering efficiency, comfort, lateral slip, and lane offset. The multi-objective optimisation problem is solved in the feasible region, and the optimisation results were applied to the collision detection process. In addition, considering the nonlinear characteristics of tire force caused by sudden lateral movements of vehicles at high-speed, a vehicle dynamics model based on the Magic Formula is established, and Model Predictive Control is applied to track the expected trajectory. Simulation results show that this strategy can plan suitable lane-changing trajectories in high-speed working conditions and achieve vehicle collision avoidance. [ABSTRACT FROM AUTHOR]

Efficient filling strategies for hydrogen fuel cell vehicles are critical for hydrogen utilization efficiency at hydrogen fuelling stations. A novel event-triggered model predictive control framework is proposed in this article for the filling process of a hydrogen fuelling station, which involves multiple compressors, cascade storage tanks, and multiple dispensers. The filling process is formulated as a Mixed-Integer NonLinear Programming (MINLP) problem with the objective of minimizing the vehicle filling times and maximizing the hydrogen utilization efficiency. A solution approach that combines the mixed-integer linear programming and genetic algorithm is designed for solving the resulting MINLP problem. In addition, an event-triggered mechanism is proposed to increase the computational efficiency and to update the control inputs only when needed. Different sets of computational experiments are carried out to demonstrate the effectiveness of the mathematical formulation and the solution approach. [ABSTRACT FROM AUTHOR]

This work studies the application of a sample-based model Monte Carlo Model Predictive Control (MCMPC) to address discontinuous events, such as collisions. The inclusion of contact dynamics in the prediction steps of MCMPC renders it advantageous for the operation of underwater snake robots in restricted environments during rescue missions. The Curvature Derivative Control (CDC) method is employed for the purpose of controlling the joints, with the exception of the head joint. The snake robot body demonstrates adaptability to various environments with the assistance of the CDC. Results obtained from the simulation demonstrate that the snake robot is capable of autonomously navigating from its beginning position outside the region to a pipe as the entrance in order to get inside. The robot successfully traverses through various internal structures within the pipe and ultimately reaches the intended destination, guided by a predetermined cost function of MCMPC. By using the capabilities of CUDA GPU parallel computing, the computational time is substantially lowered. [ABSTRACT FROM AUTHOR]

The ongoing research on model predictive control (MPC) for building air conditioning systems predominantly centers on improving the predictive capabilities of system models. In this paper, the impacts of three additional pivotal factors on MPC performance are assessed by examining a generic MPC design for a typical variable air volume (VAV) system that serves large commercial buildings. The three factors encompass the nuanced reformulation of optimization, the judicious relaxation of constraints, and the meticulous tuning of parameters. Detailed case studies with an integrated Modelica and EnergyPlus model of the US Department of Energy's Commercial Reference Building are conducted. The results confirm that the optimization formulation, along with relaxation methods, significantly affects MPC performance in terms of energy savings, zonal thermal comfort level, and computational demand. They also reveal that the impact of the MPC control parameters on the energy savings and thermal comfort may vary by season and can be non-monotonic. [ABSTRACT FROM AUTHOR]

In limit conditions, autonomous vehicles face the risk of lateral instability. The integrated control of steering and braking, an important measure for improving the stability of autonomous vehicles, has been extensively studied. A novel steering and braking integrated model predictive path tracking control (PTC) based on a dimension reduction model is proposed in this study. This method aims at the dilemma of the real-time limitation of the current integrated model predictive PTC based on nonlinear vehicle dynamics in practical applications and the unsatisfactory control effect of the integrated model predictive PTC based on the linearised vehicle dynamics in limit conditions. The core concept of this study is to reduce the input dimension of the integrated controller model by designing a model dimension reduction method, thereby reducing the decision variables of the optimisation problem and improving the real-time performance. The model dimension reduction method is designed based on the optimal utilisation of tire force to ensure the control performance of the proposed integrated control method in limit conditions. The integrated control method based on the dimension reduction model is compared with several existing integrated control methods in limit conditions to demonstrate its validity and superiority. The simulation tests with Simulink and CarSim indicate that the proposed method can reduce the calculation time by more than 40 $ \% $ % on the premise of ensuring path tracking accuracy and vehicle stability in limit conditions. Moreover, the hardware-in-the-loop tests prove the practicability of the presented method. [ABSTRACT FROM AUTHOR]

This paper aims to establish a new event-triggered model predictive control (MPC) strategy for perturbed nonlinear systems working in a networked environment, which can effectively save the computation and communication resources while ensuring control performance. The core of the framework is a new event-triggered mechanism, which is built by combining the differential and integral (D-I) information of the error between the optimal state and the actual state in a pre-specified time horizon. Based on such a triggering mechanism, a D-I based event-triggered robust MPC algorithm is proposed to stabilise the system and reduce the computation/communication resource consumption. In addition, sufficient conditions to ensure the feasibility and stability of the proposed MPC algorithm and avoid Zeno behaviour are obtained through rigorous theoretical analysis. Finally, the proposed control framework is implemented in the nonlinear cart-damper-spring and autonomous underwater vehicle systems to verify its effectiveness. [ABSTRACT FROM AUTHOR]

The energy management strategy (EMS) of hybrid electric vehicle (HEV) is the key to optimize the energy conversion performance of the whole vehicle power system. The performance of EMS could be affected by vehicle parameters such as vehicle speed, battery state of charge (SOC) and the environmental parameters including the current following state jointly. Combining the following state of the vehicle with EMS can be beneficial to improve the road performance of HEV. In this paper, a multi-objective optimization framework for safe following, comfort and fuel economy of HEV is established based on model predictive control (MPC) under both urban and high-speed conditions, and the kinematic parameter of the preceding vehicle was predicted on the basis of third-order Markov model. Simulation tests including urban and high-speed working condition are carried out respectively and results show that the following vehicle maintains expected following safety and comfort. Meanwhile, compared to the typical rule-based strategy and equivalent consumption minimum strategy (ECMS), the fuel economy has also been increased by 29.8% and 9.2% in UDDS working condition, 25.2% and 6.1% in WLTC working condition, respectively. [ABSTRACT FROM AUTHOR]

A single-phase series custom power device (Se-CPD), based on model predictive control (MPC), is presented in this paper to compensate for erratic supply voltage conditions such as voltage sag, swell, and harmonics. The control strategy for Se-CPD encapsulates the generation of reference voltage and optimal switching signals for the voltage source inverter (VSI) of the Se-CPD. The reference voltage extraction from the distorted point of common coupling (PCC) voltage is synthesized using a second-order generalized integrator (SOGI) without any phase delay, unlike the case that uses a low-pass filter (LPF). An MPC realizes the generation of optimal switching signals for Se-CPD with its intuitive and straightforward implementation, without any modulation stage, and considers the converter's switching characteristics in the MPC algorithm itself. The simulation results, using MATLAB/Simulink and the real-time simulation results using the OPAL-RT OP4510 real-time simulator, are presented to validate the SOGI-MPC's effectiveness for voltage quality improvement. [ABSTRACT FROM AUTHOR]

This paper studies model predictive control (MPC) with control barrier function to solve the stability and safety problems of switched systems subject to input and state constraints. Considering that the system state is unmeasurable and disturbed, an observer-based MPC $ H_\infty $ H ∞ method is adopted to estimate the system state and suppress disturbances. A cost function is designed to optimise the state and control input of switched systems by minimising its upper bound, and the optimisation problem is further transformed into a feasibility problem. Moreover, a control barrier function is introduced to constrain the controller gains, which ensures that the initial state of switched systems converges to the steady state without entering the specified unsafe region. Furthermore, a set of matrix inequality conditions is given to express the input and state constraints. Then, the Lyapunov function and average dwell time (ADT) approach are used to obtain sufficient conditions for the asymptotic stability of switched systems. Finally, two simulation examples are given to verify the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

In recent times, the research in sustainable energy resources was integrated with the control approaches of various hybrid renewable energy systems for balancing the electricity generation. The accelerated hybrid energy storage systems and steadily growing energy consumption make the variability and the predictable behavior occur in the renewable energy sources, which requires an energy management system. This paper aims to control hybrid renewable energy systems for power management. The systems consist of photovoltaic (PV) arrays, diesel generators, wind systems with boost converter DC-DC, and inverters. The optimum of the real-time process is less due to prediction errors of multiple renewable sources, which track the output voltage and load current. Model predictive control (MPC) is proposed for problem-solving time operation to address this issue and compensate for the prediction error. The major impact factors, such as optimal split power, stability, reliability of energy, and desired dynamic responses, have been identified by applying the MPC technique. A simulation model has been constructed using MATLAB/Simulink environment to confirm the effectiveness of the MPC technique model. [ABSTRACT FROM AUTHOR]

Adaptive cruise control (ACC) is recognized as an effective method to improve vehicle safety and reduce driver workload. This paper proposes a whole hierarchical multi-level state ACC system. According to the function of ACC system, the three-level state ACC system is designed and the conversion mechanism between different states is put forward. As for the complex car-following mode, considering the variable headway safety distance and the road adhesion coefficient, the expected safety distance model is established, using the distance error and the speed error as fuzzy input, based on the fuzzy control algorithm, the following mode is obtained; considering vehicle safety, tracking capability and ride comfort, the control objectives are formulated into the model predictive control algorithm. A dynamic weight strategy is proposed to solve time-varying multi-objective control problems, where the weight can be adjusted with respect to different following conditions. The simulation results demonstrate that the car following performance of ACC with the proposed dynamic weighted MPC can provide better performance than that using constant weight MPC, and the multi-level state ACC system can display the control mode more intuitively. [ABSTRACT FROM AUTHOR]

To improve the fuel economy of ships with multi-energy hybrid power systems, we propose an energy management strategy for hybrid power ships based on condition identification and prediction. By constructing a condition identification model based on a support vector machine (SVM), the characteristic parameters of current operation conditions of the ships were analyzed and assessed to determine operation condition types of the ships in real-time. The model predictive control (MPC) method is used to distribute the output power of the main engine, storage battery, and supercapacitor. Using multiple sets of multi-step Markov models with different types of operating conditions as the power prediction model, the optimal prediction time domain length and initial SOC are selected through simulation. Finally, based on the operating condition identification results, the operating condition prediction model for the corresponding type of operating condition is automatically selected to improve the strategy performance.The results show that the proposed strategy can save 4.55% of fuel consumption compared with the strategy using only model predictive control.Using this strategy can effectively improve the fuel economy and energy efficiency, and promote the development of green ships. [ABSTRACT FROM AUTHOR]

An input shaper-based model predictive control scheme is proposed for an overhead crane to suppress the swing of the payload. A control-oriented mathematical model of the crane is firstly derived based on the small angle hypothesis. Furthermore, three kinds of commonly used shapers are discussed and a zero vibration derivative shaper is selected for the crane. Then, a model predictive swing suppression controller is designed for the crane based on the selected shaper. Constraints on the control input and its slew rate are respected. Besides, the constraint on the swing angle of the payload is also respected as an output constraint. The constrained solution is given in the form of standard quadratic programming. The designed control scheme combines open-loop and closedloop control techniques, achieving accurate positioning of the trolley and good swing suppression of the payload. A series of simulation demonstrates the effectiveness and advantages of the proposed scheme. [ABSTRACT FROM AUTHOR]

We present a novel approach to the portfolio selection problem for a multiperiod investor facing multiple risky assets, trading constraints, and return predictability. Our objective is to maximize mean-variance utility while addressing the computational challenges arising from the curse of dimensionality associated with dynamic programming in the presence of trading constraints. To overcome this, we employ model predictive control, a computationally efficient method for solving the problem. Additionally, we propose the use of a non-parametric Bayesian model, specifically the hierarchical Dirichlet process based Hidden Markov Model (HDP-HMM), to predict the multiperiod mean and covariance of returns. Then, we consider a time-varying maximum drawdown to adjust the risk aversion, which can effectively cope with the limit loss problems. Through extensive simulation studies and empirical analysis, we demonstrate that trading strategies based on our proposed method outperform existing approaches in out-of-sample performance. [ABSTRACT FROM AUTHOR]

The paper proposes a traffic responsive control framework based on a Model Predictive Control (MPC) approach. The framework focuses on a centralized method, which can simultaneously compute the network decision variables (i.e., the green timings at each junction and the offset of the traffic light plans of the network). Furthermore, the framework is based on a hybrid traffic flow model operating as a prediction model and plant model in the control procedure. The hybrid traffic flow model combines two sub-models: an aggregate model (i.e., the Cell Transmission Model; CTM) and a disaggregate model (i.e., the Cellular Automata model; CA), using a transition cell to connect them. The whole framework is tested on a signalized arterial, performing several analyses to calibrate the MPC strategy and evaluate the traffic control approach using fixed and adaptive control strategies. All analyses are made in terms of total time spent, network total delay, queue lengths and degree of saturation. [ABSTRACT FROM AUTHOR]

The model predictive control (MPC) tries to find the best control output by optimizing among the predictions. In this context, determining the objective function of optimization is a critical process. This study discusses the velocity control of the mini brushed direct current (BDC) motor used in the actuator with MPC. Four different functions have been defined to design the most appropriate cost function for MPC by considering the parameters that may be effective in the motor dynamics such as velocity, current, power, and switching states. Software in the Loop (SIL), one of the modeling-based testing methods, was used within the scope of testing the algorithms. After the SIL tests, it has been observed that the mini BDC motor can be successfully controlled with simple adjustments to the cost function of the MPC. As a result of the analysis obtained the best results with the objective function consisting of velocity error, estimated current, and the difference between the two estimated velocity values. With this controller, the BDC motor can be controlled without overshoot and with a steady-state error of less than 2% under load. [ABSTRACT FROM AUTHOR]

For a class of nonlinear Markov jump systems (MJSs), an efficient robust model predictive control (MPC) is designed with bounded persistent perturbations and hard constraints. Nonlinearity is considered as a description of polyhedral form. First, affine control inputs are introduced for the polytopic MJSs to provide the expected performance. Quadratic boundedness is then used to deal with bounded persistent perturbations to improve the robustness of the closed loop system. Then the large amount of online MPC computation problem is solved efficiently by matrix partitioning. The efficient MPC strategy not only ensures that the closed-loop system is stochastically stable but also reduces the amount of on-line computation and improves the efficiency of on-line computation. Finally, a numerical example and a nonlinear mechanical mass-spring-damper system are applied to elucidate the proposed algorithm. [ABSTRACT FROM AUTHOR]

Extraction of low-dimensional latent space from high-dimensional observation data is essential to construct a real-time robot controller with a world model on the extracted latent space. However, there is no established method for tuning the dimension size of the latent space automatically, suffering from finding the necessary and sufficient dimension size, i.e. the minimal realization of the world model. In this study, we analyze and improve Tsallis-based variational autoencoder (q-VAE), and reveal that, under an appropriate configuration, it always facilitates making the latent space sparse. Even if the dimension size of the pre-specified latent space is redundant compared to the minimal realization, this sparsification collapses unnecessary dimensions, allowing for easy removal of them. We experimentally verified the benefits of the sparsification by the proposed method that it can easily find the necessary and sufficient six dimensions for a reaching task with a mobile manipulator that requires a six-dimensional state space. Moreover, by planning with such a minimal-realization world model learned in the extracted dimensions, the proposed method was able to exert a more optimal action sequence in real-time, reducing the reaching accomplishment time by around 20%. [ABSTRACT FROM AUTHOR]

The objective of this paper is to provide a stochastic framework to optimally avoid collision between a maneuverable spacecraft and a space object or debris. The satellite collision can be caused through a cyber-attack on a satellite by colliding it with a considered strategic satellite. Consequently, it is highly imperative that critical operational space assets be provided with autonomous collision avoidance systems. The collision avoidance methodology proposed in this paper will reduce the collision probability to an acceptable level and protect the satellite against indirect kinetic cyber-attacks initiated by designing optimal collision avoidance maneuvers using a stochastic model predictive control strategy. The collision probability is estimated using the available historical Two-Line Elements of determined objects, and the model predictive control scheme guarantees the safety of the space close approaches. The proposed and developed collision-avoidance countermeasure methodology is numerically simulated for the collision case study between the Iridium-33 and the Cosmos-2251 satellites. The results demonstrate and illustrate the effectiveness, capabilities, and advantages of our proposed methodology in avoiding probable collisions due to indirect kinetic cyber-attacks. [ABSTRACT FROM AUTHOR]

The wind turbine system is one of the most challenging control problems in power systems due to its fast and slow dynamics and the optimal and robust control of the doubly fed induction generator is particularly important in this system. To handle the desired performance under different conditions, this paper presents a new control method by combining the model predictive control method with the adaptive method. In the aforementioned method by decoupling the active and reactive power control, an adaptive method is used to estimate the uncertainty, disturbance, and nonlinear terms as well as the internal feedback design. Then, linear matrix inequality-based robust model predictive control is designed as external feedback. The Lyapunov method is used to demonstrate system stability under the proposed controller. The simulation results show the efficiency of the method under different scenarios. [ABSTRACT FROM AUTHOR]

We propose a design of data-driven Model Predictive Control (MPC) using a suboptimal trajectory and the linear time-varying (LTV) models from data-driven trajectory optimisation that achieves offset-free tracking. Data-driven constrained differential dynamic programming (CDDP) is exploited to improve the trajectory iteratively without the knowledge of the nonlinear model. A trajectory is divided to the transient and steady state regions, controlled by the Linear time-varying MPC (LTVMPC) and the offset-free linear MPC (LMPC), respectively. We prove the feasibility of the proposed LTVMPC in the transient region, and the offset-free tracking property of LMPC. The proposed scheme is validated to a continuous stirred tank reactor (CSTR) process. Simulation studies show that the suboptimal trajectory and LTV models are generated by CDDP, and the proposed MPC achieves offset-free tracking and disturbance rejection for a set of initial conditions and set points in the operating region. [ABSTRACT FROM AUTHOR]

This paper discusses the modelling and control of a combined load frequency control (LFC) and automatic voltage regulator (AVR) system for a multi-area power system network with each generating area includes thermal, diesel, and solar thermal power plant. Superconducting magnetic energy storage is incorporated with the LFC loop to provide auxiliary control against instantaneous changes in active power demand. Model Predictive Controller (MPC) has been employed in this study to improve the performance of AVR and LFC loops, and controller weights are fine-tuned with the help of Harris Hawks Optimization (HHO) approach. The effectiveness of the proposed MPC-HHO algorithm has been examined by comparing its transient performance indices with that of conventional MPC, PID, FOPID, fuzzy PID, integral double derivative with derivative filter controller as well as Particle Swarm Optimization and Sine Cosine Algorithm based Model Predictive Controllers. The proposed MPC-HHO controller gives superior results than the above-mentioned controllers. [ABSTRACT FROM AUTHOR]

This paper addresses a non-fragile robust model predictive control design for a class of continuous-time uncertain systems with multiple state-delay and constrained control signals. The parameters of the system and the control gain are assumed to have perturbation in the additive form. The Lyapunov–Krasovskii functional approach is employed to derive sufficient conditions for determining a non-fragile robust state-feedback controller for all admissible uncertainties by minimising the upper bound of the defined quadratic cost function with respect to some linear matrix inequalities (LMIs). An additional inequality condition is imposed to address the constraint associated with the control signals. Numerical simulations are provided to assess the performance of the proposed controller. [ABSTRACT FROM AUTHOR]

Model Predictive Control (MPC) has attracted much attention and is widely used in power electronics. However, implementing the MPC algorithm is still a difficult task due to the fast dynamics of power converters and strict time constraints. In this paper, a computationally efficient MPC algorithm for grid-tied power converters based on the fast gradient projection method and invariant set theory is proposed. The algorithm is implemented and tested through hardware-in-the-loop simulations using Texas Instruments digital signal processors and Xilinx Field Programmable Gate Arrays platforms. [ABSTRACT FROM AUTHOR]

Model predictive control (MPC) of legged and humanoid robotic systems has been an active research topic in the past decade. While MPC for robotic systems has a long history, its paradigm such as problem formulations and algorithms has changed along with the recent drastic progress in robot hardware, computational processors, and algorithms. This survey paper reviews recent progress on MPC for legged and humanoid robotics from the following three points of view. First, we review a variety of dynamical models of robotic systems used in the MPC formulation. Second, we give an overview of MPC algorithms, particularly focusing on suitable ones for robotic problems. Finally, we introduce methods and applications of MPC for practical robotic problems from MPC based on reduced-order models to recent progress on MPC based on whole-body models. [ABSTRACT FROM AUTHOR]

This study proposes a motion planning method with strict obstacle avoidance constraints among polygonal-shaped objects for a tractor–trailer mobile robot. The conventional method generally expresses collision-free constraints by approximating a robot as one circle or several circles or ellipsoids. However, accomplishing moving tasks with minimum margins is desirable, with improved efficiency of space availability when multiple robots operate in one area such as a factory, restaurant, office, or warehouse. The target of this study is a tractor–trailer mobile robot that can transport more goods efficiently, while significantly reducing margins during movement. To control the target, multiple convex polygonal objects expressing the mobile robot and surroundings are transformed into linear simultaneous inequalities using 'Farkas' lemma.' Embedding inequalities into model predictive control yields a motion planner that allows tractor–trailer mobile robot movement and possesses non-holonomic constraints. The proposed model is validated through simulation and real machine experiments. [ABSTRACT FROM AUTHOR]

This work is concerned with the problem of planning trajectories and assigning tasks for a Multi-Agent System (MAS) comprised of differential drive robots. We propose a multirate hierarchical control structure that employs a planner based on robust Model Predictive Control (MPC) with mixed-integer programming (MIP) encoding. The planner computes trajectories and assigns tasks for each element of the group in real-time, while also guaranteeing the communication network of the MAS to be robustly connected at all times. Additionally, we provide a data-based methodology to estimate the disturbances sets required by the robust MPC formulation. The results are demonstrated with experiments in two obstacle-filled scenarios. [ABSTRACT FROM AUTHOR]

This paper proposes a self-triggered model predictive control algorithm for a class of discrete-time Markov jump linear systems with parametric uncertainties. A dual-mode design framework is employed to guarantee the optimality, feasibility and robust stability. The online control law is obtained by minimising the worst case of a novel cost function, which contains a self-triggered mechanism. Finally, a numerical example verifies the effectiveness and resource-saving capacity of the developed approach. [ABSTRACT FROM AUTHOR]

In this work, a predictive control framework is presented for feedback stabilisation of nonlinear systems. To achieve this, we integrate Koopman operator theory with Lyapunov-based model predictive control (LMPC). The main idea is to transform nonlinear dynamics from state-space to function space using Koopman eigenfunctions -- for control affine systems this results in a bilinear model in the (lifted) function space. Then, a predictive controller is formulated in Koopman eigenfunction coordinates which uses an auxiliary Control Lyapunov Function (CLF) based bounded controller as a constraint to ensure stability of the Koopman system. Remarkably, the feedback control design proposed in this work remains completely data-driven and does not require any explicit knowledge of the original system. Furthermore, due to the bilinear structure of the Koopman model, seeking a CLF is no longer a bottleneck for LMPC. Benchmark numerical examples demonstrate the utility of the proposed feedback control design. [ABSTRACT FROM AUTHOR]

It is important to control the heating system by following real-time demand, while considering the dynamic changes and non-uniform distributions of indoor environments. This paper presents a model predictive control (MPC) scheme for predicting indoor air temperatures at multiple points in a large factory building that consists of large irregular spaces and heat-generating equipment. Instead of using a full-blown dynamic simulation model (e.g. EnergyPlus), the authors developed a lumped simulation model. This model can accurately predict the temperatures and is, therefore, used for the optimal on/off control of 61 unit heaters installed in the factory building. Based on the MPC, energy savings of 56.3% were realized over three weeks, and the indoor air temperatures were maintained within a comfortable range. It is highlighted in the paper that this MPC approach based on the minimalistic lumped model can accurately predict indoor thermal behaviour and save significant energy. [ABSTRACT FROM AUTHOR]

An overview of studies on mechanistic model-based control of biodiesel production process is presented and discussed, focusing on control strategies including, among others, plantwide control, model predictive control, and optimal control. The paper highlights past studies that used advanced control strategies to improve the economic performance of biodiesel production. Based on the outcome of the literature review, there are limited applications of advanced control strategies in the biodiesel production process, especially within the plantwide control (PWC) framework. Only a few studies addressed the optimization and control of the biodiesel process beyond the transesterification reactor. Decentralized plantwide control architecture using a heuristic-based method with simulations in Aspen Plus Dynamic is currently employed to demonstrate how to facilitate and enhance the process performance of biofuel production. The review shows a need for a simple, effective, and easily implementable plantwide design control capable of achieving optimal profitability, controllability, and safety. [ABSTRACT FROM AUTHOR]

This study proposes an approach to the yaw and lateral stability of the vehicle that considers their steady-state trend. This study designs a new linear time-varying model predictive control (LTV-MPC) method based on the 2DOF vehicle model. The tire model curve of the magic formula is simplified into two straight lines to replace the nonlinear characteristic of tires and reduce the solution time of the controller under the premise of a good tracking effect. The lateral stiffness of tires is selected according to the predicted trend of stable state of the vehicle. Simulation results show that, under extreme conditions, the proposed method can effectively track the reference vehicle sideslip angle and yaw rate. The real-time performance of the controller is substantially improved compared with NMPC. [ABSTRACT FROM AUTHOR]

This paper presents a stability analysis tool for model predictive control (MPC) where control action is generated by optimising a cost function over a finite horizon. Stability analysis of MPC with a limited horizon but without terminal weight is a well-known challenging problem. We define a new value function based on an auxiliary one-step optimisation related to stage cost, namely optimal one-step value function (OSVF). It is shown that a finite horizon MPC can be made to be asymptotically stable if OSVF is a (local) control Lyapunov function (CLF). More specifically, by exploiting the CLF property of OSFV to construct a contractive terminal set, a new stabilising MPC algorithm (CMPC) is proposed. We show that CMPC is recursively feasible and guarantees stability under the condition that OSVF is a CLF. Checking this condition and estimation of the maximal terminal set are discussed. Numerical examples are presented to demonstrate the effectiveness of the proposed stability condition and corresponding CMPC algorithm. [ABSTRACT FROM AUTHOR]

Aiming at the problem of autonomous collision avoidance of unmanned vessels in the case of multiple vessels encountering at sea, this paper proposes a method for collision avoidance of vessels in open water based on the Mathematical Model Group (MMG) vessel motion mathematical model. This method uses Model Predictive Control (MPC) model algorithm, and considers vessel maneuverability and the International Regulations for Preventing Collision at Sea, 1972 (COLREGs), and uses fuzzy mathematics to analyze the collision risk of vessels during navigation, and then constructs the evaluation function of the collision avoidance algorithm. The vessel's autonomous collision avoidance is realized. The simulation results show that the algorithm can solve the problem of autonomous vessel collision avoidance in the case of multi-vessel encounters in open water, which verifies the effectiveness of the algorithm. [ABSTRACT FROM AUTHOR]

A novel attitude control algorithm is developed for spacecrafts based on the model predictive control and proportional-integral observer (PIO) in the presence of constraints. The high dimensional nonlinear dynamics are firstly transformed into three single-input single-output linear structural subsystems with coupled actions. Then these actions are viewed as disturbances estimated by the PIOs, and their values are embedded into the models to improve the accuracy of prediction. The predictive controller is composed of the analytical solution and the heuristic constraint handling. In addition, the angular position information is only needed for implementation. Finally, several simulations are used to verify the effectiveness of the developed algorithm. The results show that the designed system compensates the coupled actions well and makes the attitude control performance good. [ABSTRACT FROM AUTHOR]

In this paper, a two-loop Model Predictive Controller (MPC) with an improved tube is proposed for industrial application with bounded uncertainties subject to input and state constraints. This scheme attempts to remove some existing obstacles against exploiting MPC in industrial applications, such as (i) risk and cost of a new controller replacement, (ii) difficulties of attaining a precise open-loop model of an industrial system and (iii) high computational burden of MPC methods. To this end, tube conservatism and calculation burden are reduced using the transient response of the error dynamics. Thus the feasible region of the MPC is enlarged and its computation time is reduced. To reduce modelling difficulties, the investigated approach does not require the open-loop model dynamics of the system and utilises the closed-loop model instead. On the other hand, it allows the existing inner-loop controller to remain unchanged without any manipulations, which results in eliminating a new controller replacement risk and cost. Additionally, for the proposed control method, robust stability and recursive feasibility are guaranteed without terminal gradients. Finally, an illustrative example is carried out in the simulation results to show the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

This paper focuses on the model predictive control (MPC) issue where the measurable state is released under an event-triggered mechanism (ETM) to implement MPC over an infinite horizon. A new dynamic ETM (DETM) is devised to conserve network resources, which contains an additive internal dynamic variable (IDV), a multiplicative adaptively adjusting variable, a time-varying weighting matrix and several flexible scalars. The MPC problem is formulated as a 'min–max' optimisation problem (OP), where a hard constraint and robust positive invariant set on the predictive state/IDV are considered simultaneously. By resorting to a Lyapunov-like function that depends on the IDV, we put forward an auxiliary OP with matrix-inequality-based constraints. By the feasible solutions of such an auxiliary OP, the feedback gain matrix is designed which ensures the asymptotic stability of the closed-loop system. Two examples are presented to demonstrate the validity of the devised DETM and the DETM-based MPC algorithm. The study verifies that the devised DETM has advantages over an existing counterpart in conserving network resources while achieving the desired performance. [ABSTRACT FROM AUTHOR]

Multi-level inversion can be obtained from a dual two-level inverter fed open-end winding induction motor (OEWIM) powered from a single dc power supply and a floating capacitor. In this paper, two new model predictive control (MPC) variants are proposed for this drive by simple use of a voltage-based control approach in place of current-based control to reduce the number of computations in the digital controller. With both the proposed MPC variants, the floating capacitor voltage is initialized and is tightly regulated. Improved computational efficiencies are accomplished with both the proposed MPC variants, and superior performance of the drive is obtained with reduced sampling time achievable by exploiting the reduced computational burden. Performance of the entire drive system is demonstrated over a wide motor speed range by extending into field weakening operation also. Besides tight capacitor voltage regulation, the rugged performance of the proposed MPC variants under speed and load changes is demonstrated through simulations and are experimentally validated. Faster execution times owing to a dramatic reduction in computations, compared to some of the existing control methods, superior current and motor torque characteristics of OEWIM are also showcased in support of the proposed MPC variants. [ABSTRACT FROM AUTHOR]

Self-triggered and event-triggered control have been widely implemented in various fields recently, especially in networked control systems. In order to save communication resources, trigger control conducts aperiodic control strategies instead of traditional time-driven scheme. Based on the self-triggered control mechanism, this paper investigates self-triggered strategy in model predictive control for nonlinear continuous-time networked system, selects aperiodic samples to ensure system performance while reducing computing energies and improving the resource usage efficiency. The usage of communication resources is quantified as a damping parameter that can characterize the communication effects in total cost function, such that the optimal sampling time and input trajectory can be obtained synchronously. In theoretical results, the feasibility of the proposed self-triggered strategy and the asymptotic stability of the nonlinear system are guaranteed. Furthermore, a simulation is given to illustrate the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

Coupled multiple-tank systems are very important for a wide range of industrial applications due to their unique uses. However, the liquid level control for the coupled two-tank multi-input multi-output (MIMO) system is quite challenging because it has strong nonlinearity and coupling, and it is susceptible to multiple external disturbances. For this process, this paper proposes a novel anti-disturbance control strategy consisting on a nonlinear composite hierarchical anti-disturbance predictive control (CHADPC). First, a model-based explicit nonlinear model predictive controller (ENMPC) is designed assuming that all disturbances are measurable and its global exponential stability is proved. Then, a nonlinear disturbance observer (DO) is designed to estimate the lumped disturbances. The composite controller handling the estimated disturbances is then proposed. Finally, simulation and experimental tracking control tests under perturbations and comparisons with recently reported works have been carried out to highlight the promising performance of the proposed ENMPC and CHADPC schemes. [ABSTRACT FROM AUTHOR]

Nonlinear effects and external disturbances can severely impact the control of an autonomous race car at the handling limits. State-of-the-art approaches do not take these uncertainties explicitly into account in the design process and are therefore prone to failure. To overcome this limitation, we present a robust control design based on tube model predictive control (TMPC). It is based on a simplified friction limited point-mass model and an additive disturbance for the lateral and longitudinal dynamics. Instead of nominal predictions, it leverages an approximate tube of reachable sets over the prediction horizon to guarantee constraint satisfaction. The resulting optimisation problem can be posed in the form of a standard quadratic programme by tightening the input and state constraints. The computational burden is therefore the same as in the nominal case. We benchmark our controller on a Hardware-in-the-Loop testbench with a nonlinear dual-track model and a combined Pacejka tyre model. The results demonstrate that the TMPC controller reduces the constraint violations while achieving comparable lap-times in contrast to an MPC controller and an infinite time LQR controller. It manages to apply caution when needed while maintaining a similar level of performance and is therefore considered to be superior in practical applications. [ABSTRACT FROM AUTHOR]

Integrating renewable energy and energy storage systems provides a way of operating the electrical grid system more energy efficiently and stably. Thermal storage and batteries are the most common devices for integration. However, it is not clear which integrated storage system performs better in terms of overall economics. Ice storage has low initial and maintenance costs, but there is an efficiency penalty for charging of storage and it can only shift electrical loads associated with building cooling requirements. A battery's round-trip efficiency, on the contrary, is quite consistent and batteries can be used to shift both HVAC and non-HVAC loads. However, batteries have greater initial costs and a shorter life. This research presents a tool, using model predictive control and optimal sizing, and provides a case study for comparing life-cycle economics of battery and ice storage systems for commercial buildings that have chillers for cooling and an on-site photovoltaic system. [ABSTRACT FROM AUTHOR]

Pivoting gait is an efficient way for robots to manipulate a heavy object. Although we can cope with the contact constraints of the pivoting gait by using the model predictive control (MPC), systems with complex dynamics, including the pivoting gait, usually require long horizons in the MPC and it leads to a heavy computational load. To overcome this problem, we introduce basis functions to parameterize the free variables in the MPC and formulate an optimization problem with new decision variables, which are coefficients of the basis functions. We especially introduce multiple basis functions and compare their performances in generating the robotic pivoting gait. As a result, the most effective reduction in the dimension of the free variables is achieved by using the Laguerre basis function and the computational efficiency of the MPC is greatly improved. The simulation and experiments show that the time cost of the generation of pivoting gaits by the proposed method is remarkably reduced and the generated pivoting gaits are feasible and robust where a dual-arm robot successfully manipulates a toy piano by the pivoting gait. [ABSTRACT FROM AUTHOR]

We study model predictive control for singular differential-algebraic equations with higher index. This is a novelty when compared to the literature where only regular differential-algebraic equations with additional assumptions on the index and/or controllability are considered. By regularisation techniques, we are able to derive an equivalent optimal control problem for an ordinary differential equation to which well-known model predictive control techniques can be applied. This allows the construction of terminal constraints and costs such that the origin is asymptotically stable w.r.t. the resulting closed-loop system. [ABSTRACT FROM AUTHOR]

This paper proposes a virtual-flux-based model predictive control (VF-MPC) scheme to remove the grid voltage sensor and enhance the grid current performance for AC/DC matrix converter under unbalanced grid voltages. By modelling the power flow in terms of the virtual flux (VF) and its 90° lagging signal, the grid current reference and grid voltage are obtained simply without the extraction of the VF positive and negative sequence components. Furthermore, the number of current vectors is increased from 9 to 39 vectors by generating 30 virtual current vectors to minimise the grid current tracking error. Especially, to reduce the computational burden due to the increased number of current vectors, we introduce a simple sector detection algorithm, which can effectively reduce the number of candidate current vectors from 39 to 8. The proposed VF-MPC scheme is compared with previous MPC schemes, and the control effectiveness is verified by simulation and experimental results. [ABSTRACT FROM AUTHOR]

Nuclear reactor core power level control and the average coolant temperature usually adopt independent control strategies, but tracking only one single core power makes the coolant temperature control risky in a disturbance situation. To solve this problem, a reference trajectory calculation based on the coolant outlet temperature under the steady state of the reactor power is designed; then, a model predictive controller with a dual objective optimization function, which also includes a relaxation factor, is constructed. By setting the tracking weight matrix in the controller, the tendency setting of dual target trajectory tracking can be realized. Simulation experiments verify the effectiveness of core power level control considering the coolant outlet temperature trajectory tracking. Furthermore, the tracking effect of different weight matrices is simulated and analyzed. The results show that the developed control strategy is more flexible than a single control scheme. [ABSTRACT FROM AUTHOR]

Spray drying is a widely used unit operation in synthesis of particles for production of chemicals, food, and pharmaceuticals. However, accurate control and development of this unit remains an elusive task because of the complex interactions of variables and phenomena. In this paper, we adopt and present a dynamic model of the complete drying process for a laboratory spray dryer. The dynamic mathematical model, which integrates atomization, evaporation, and particle formation models, is described by mass and energy balances. The model can predict temperature, residual moisture, and particle size of the produced powder. Model predictions are verified through datasets collected from a lab-scale spray dryer. A data-driven model based on the dynamic model is produced to interface with the control strategy. A model predictive control (MPC) strategy is then adopted to address the highly cross-coupled effects among different components of the dryer and guarantee desired product quality measures. Successful MPC implementation on the drying system is addressed, including trajectory tracking and disturbance rejection scenarios. [ABSTRACT FROM AUTHOR]

This paper proposes a novel modulation algorithm based on vector decomposition and model predictive control for 5-level active neutral-point-clamped (5 L-ANPC) inverter. Firstly, the control of 5-level inverter is transformed into that of 3-level inverter by vector decomposition, which reduces the control complexity. Secondly, based on the framework of 3-level inverter, the optimal zero sequence voltage and switch state sequence are selected by multi-objective optimisation of model predictive control to balance the neutral point voltage and floating capacitors voltages simultaneously. Finally, according to the optimal choice, single-carrier comparison is used to generate the drive signal, which does not require traditional dual-carrier or vector synthesis, and is relatively easy to implement. Experimental results verify the validity of the method. [ABSTRACT FROM AUTHOR]