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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]

This paper provides a thorough evaluation of well-known control algorithms, including proportional–integral (PI), model predictive control (MPC), and H-infinity (H ∞) controllers, by implementing them in a full nonlinear wind turbine model under normal wind conditions in below and above-rated wind speeds. The simulation results show that all the controllers perform satisfactorily. This study extends MPC to include a feedforward (FF) loop (FF-MPC) that uses the wind speed information provided by a light detection and ranging (LiDAR) sensor, which measures the upcoming wind (in advance), to improve the overall control performance. The FF-MPC was tested under both normal and anomalous (i.e. gusty) wind conditions. The results were compared with those of the standard feedback MPC (FB-MPC). The results show that the incorporation of the FF loop into the standard FB controller can improve the control performance, which can result in improved reliability and lifespan of the turbine. Furthermore, MPC was augmented with an FF loop over PI and H ∞ controllers owing to its versatility in handling constraints, nonlinearities, and multiple objectives, along with its inherent capability to incorporate preview wind data. All the controllers are tested using a high-fidelity aeroelastic model (i.e. Bladed by DNV). The use of a Bladed model is common in wind turbine controller design before the application to the real-life wind turbine, and Bladed also allows more realistic simulation when incorporating a LiDAR. • PI, MPC and H ∞ are evaluated and compared under realistic turbulent wind conditions. • A feedforward (FF) loop is added to MPC that uses the wind measured by LiDAR. • FF-MPC is compared to the standard MPC under realistic (inc. gusts) wind conditions. • All the simulations are performed using a high-fidelity aeroelastic model. [ABSTRACT FROM AUTHOR]

Model Predictive Control algorithms have been recently developed for controlling grid-tied converters. However, the inclusion of the converter switching constraints in the optimization problem and the high computational burden are some of the main challenges of these algorithms. In this way, this paper proposes a Fast Finite Control Set Model Predictive Control algorithm with a low computational burden for a three-phase Neutral Point Clamped inverter considering its switching constraints. Initially, the vector with the unconstrained solution in the line-to-line voltage coordinates is obtained to minimize the current tracking error. Then, it is limited to ellipses as an intermediate step to ensure that the selected voltage vector is feasible and to restrict the switching transitions. The constrained vector is rounded to the nearest inverter line-to-line voltage vector to be implemented in the next sampling period. The NPC redundant phase-voltage vectors are generated online to avoid the potentially destructive switching transitions. The neutral point is balanced by minimizing a cost function, considering the obtained redundant phase voltage vectors, and is evaluated at most twice in each sampling period. As both control objectives are treated in a cascaded sequence, the proposed Fast FCS-MPC avoids the design of weighting factors and has the advantages of low computational burden, fast transient response, and good steady-state performance. Finally, Hardware-in-the-Loop results are presented to compare the proposed Fast FCS-MPC to other strategies presented in the literature, and the effectiveness of the proposed algorithm is also demonstrated by means of an experimental prototype. • System model in the converter output line-to-line voltage space with integer vectors. • Inverter constraints included in the formulation of the optimization problem. • Fast way to obtain the switching voltage vector by rounding the constrained solution. • Comparison results indicating equivalent performance with optimal algorithms. • Lowest computational burden when compared to similar algorithms. [ABSTRACT FROM AUTHOR]

The automatic tuning of the weighting parameters in model predictive control (MPC) requires a systematic strategy to shape the state and input responses to become close to the user's specifications. In this paper, based on the system-level parameterization of controllers, the system response under MPC is considered as the optimized response matrix under the tuning parameters, and hence an inverse optimization formulation is proposed to seek the tuning under which the desired response is close to being optimal. This results in a two-phase procedure, both formulated as quadratic programming (QP) or linear programming (LP) problems. First, the user specifications are interpreted as "reference" responses or hard constraints, under which the closest realizable response is found. Then, by fitting the realizable response to optimality conditions, the inversely optimal MPC parameters are determined with minimum residuals. The proposed automatic MPC tuning approach is generic and efficient, whose practical performance is demonstrated by applications on single-loop and process unit-level models. [ABSTRACT FROM AUTHOR]

Interconnected structures are commonly found in process networks. In this paper, an output consensus framework is proposed for a class of continuous interconnected linear heterogeneous systems subject to disturbances and constraints. The distributed output consensus control strategy is developed by combining integral sliding mode control with model predictive control. The integral sliding mode control is designed to eliminate a class of matched disturbances. The model predictive control plays two main roles: On the one hand, it drives the system states to track the steady state values so as to achieve output consensus; on the other hand, it helps to deal with interconnections and constraints existing in systems. In the meantime, a distributed iterative algorithm is designed to acquire the system steady states. A simulation example and an experiment relating to control of systems of interconnected CSTRs are presented to validate the effectiveness and superiority of the proposed method. [ABSTRACT FROM AUTHOR]

In the field of heavy vehicle stability research, traditional safety requirements are often based on static scenario settings. However, the complexity and variability of actual road environments require safety control strategies that can be adapted to different driving conditions and environmental changes in real-time. To address this challenge, the paper proposes a coordinated control strategy for yaw and roll stability that considers the dynamic safety requirements. First, a quantitative analysis method for vehicle stability is proposed based on the dissipated energy theory, taking into account the lateral-vertical dynamics coupling characteristics. Additionally, a dynamic safety requirements identification model is developed by integrating the vehicle's future driving risk prediction algorithm. To meet dynamic safety requirements, a dynamic weight model predictive control method based on randomized ensembled double Q-learning reinforcement learning is designed. This method adjusts the control weights of yaw and roll stability online to flexibly address various destabilization risks, aiming to achieve more precise and efficient stability control. Through simulation and experimental verification, the results demonstrate that the proposed coordinated control strategy can effectively enhance the stability and safety of heavy vehicles in complex and dynamic driving environments. [ABSTRACT FROM AUTHOR]

This paper considers the problem of optimal coordination of trajectory tracking performance and handling stability for autonomous equipped with distributed drive electric vehicle. Therefore, a hierarchical frame for multi-mode chassis dynamics torque vector allocation strategy is proposed, which aimed to solve the contradictory issues between vehicles' trajectory tracking accuracy and handling stability under extreme working conditions. Firstly, in a hierarchical architecture, the upper-level trajectory tracking controller is designed by using model predictive control theory, which is used to solve the front wheel angle and the additional yaw moment of the vehicle. Secondly, the lower-level multimode torque distribution controller severs the sum of tire force utilization in every wheel as the objective function, and designs three distribution modes of chassis dynamic torque vectors based on the response of the longitudinal force and yaw moment obtained from the upper-level controller. Thirdly, the switching mechanism between the three chassis torque vector distribution modes is set according to the road adhesion condition and the requirements of the upper-level controller. Then, an analysis is conducted on the computational time complexity and robustness of the algorithm, confirming the potential for real-world application of the algorithm. Finally, Simulink/CarSim co-simulation test and hardware-in-the-loop test platform are carried out. And a vehicle trajectory tracking controller with single-mode torque vectors distribution by MPC is used as the baseline algorithm. The test results show that the proposed method show better trajectory tracking performance and handling stability than the baseline algorithm under the conditions of low adhesion surfaces and split-friction surfaces. Therefore, this study provides a solution for the safe driving of autonomous vehicles under extreme working conditions. • A hierarchical architecture for autonomous driving trajectory tracking is proposed. • Multi-mode torque vector allocation is appropriate for extreme operating conditions. • HiL test verify the performance of handling stability and trajectory tracking. [ABSTRACT FROM AUTHOR]

In this paper, a control solution to reduce congestion in highway traffic systems is presented. The aim is to produce a control strategy characterized by low computational cost, so that real-time implementation can be attained. The adopted model to describe traffic dynamics is the METANET model. A particular spatio-temporal derivative relationship, describing how control signals (ramp metering and variable speed limits) and disturbances effects propagate along the highway system, is highlighted in the paper. This relationship is the basis of a proposition providing the essential tool for relative degree calculation in generic highway systems. Utilizing this proposition, a feedback linearization-based control law is developed. The control design is completed by employing a linear MPC, which allows for complying with the physical constraints. The performance of the proposed method is evaluated by conducting comprehensive simulation studies, also considering a real-world traffic system. The computational costs are analyzed by comparing the developed methodology with a nonlinear MPC-based approach. Simulation evidence confirms that the proposed method can provide satisfactory solutions for coordinating RM and VSL in highway systems. Such solutions are compatible with real-time implementation. [ABSTRACT FROM AUTHOR]

This paper addresses the design of a decision making and motion planning system for lane change maneuvers considering energy efficiency. A novel decision making algorithm is proposed to check the desirability of performing the lane change. The algorithm is based on a utility function that consists of different performance criteria, including energy consumption. The execution of the decided maneuver involves a lower-level motion planning and control system for the longitudinal and lateral directions. For the longitudinal direction, an energy-efficient Model Predictive Controller (MPC) is designed, which considers the safety boundaries as well as other constraints, such as comfort, traffic laws, and physical limitations of the system. For the lateral direction, the desired trajectory is planned based on a parameterized sigmoid function. The lateral tracking is then realized by a PID controller. Finally, to evaluate the performance of the designed algorithms, a fuel consumption map of an internal combustion engine (ICE) is approximated by a second-order multivariate polynomial. Simulation results demonstrate the capability of the proposed algorithm to safely perform the lane change maneuver in different scenarios and for two vehicle models, including a simplified vehicle dynamic model and a high-fidelity IPG CarMaker model. [Display omitted] • A new lane change decision making is proposed considering energy consumption. • An integrated energy-efficient planning and control framework is designed using MPC. • The framework considers safety, comfort, energy consumption, and actuator limitation. • The fuel map of an internal combustion engine is fitted to second-order polynomial. • The obtained polynomial is used in the optimization problem of energy-efficient MPC. • Simulation study for a simple mathematical model and a high-fidelity CarMaker model. [ABSTRACT FROM AUTHOR]

The paper presents the comparison of two different continuous-time adaptive control strategies applied to the temperature stabilization of molten glass during conditioning. Both control methods include on-line linear continuous-time model parameter identification using a nonstandard procedure based on the modulating functions method. The related control task is of great practical importance because it directly affects the quality of manufactured glass containers. The molten glass temperature must be stabilized with accuracy of about 1 C ∘ which can be very difficult. At the core of this work, the synthesis of a nonstandard adaptive control procedure is described that consists of a linear quadratic regulator (LQR) being fed with process parameters and state estimates. These new state estimates are generated with a special transform and reconstructed by a special type of modulating function state observer consisting of two modulating function based soft-sensors which rely on a continuous-time model. However, an equally important issue of this investigation is the efficiency and accuracy of the algorithm. To this end, the described stabilization method will be compared with a standard continuous-time model predictive control (MPC) approach that was used in the authors' previous research on the continuous molten glass temperature stabilization in a single glass forehearth zone. Simulation results based on experimental calibration data are presented and compared for these two approaches. It turns out that the first method with LQR is simpler than the MPC approach while maintaining the same level of accuracy and quality of control. • Performed glass conditioning process experiments and related model calibration. • Established an adaptive control procedure using the Modulating Function Method. • Carried out a comparison study evaluating LQR and continuous-time MPC strategies. • Derivation of a delay-based stability criterion for modulated feedback control. [ABSTRACT FROM AUTHOR]

This paper presents the design of a database-driven model predictive control (DD-MPC) system for the online adaptation of autonomous excavators to environmental conditions. Control systems for autonomous excavators should consider environmental conditions as these affect their performance for a given excavation operation. Moreover, these conditions may change during operation. MPC was performed using an excavator-environment interaction model, which was estimated online using DD-Modeling to represent changes in environmental conditions. The target excavation trajectory was modified by predicting excavation motion using MPC and decision based on the prediction to complete a given excavation operation regardless of the environmental conditions. The proposed system was experimentally verified using a radio-controlled excavator, and it was confirmed that a given operation could be completed by adapting to environmental conditions. • The control system enables autonomous excavators to adapt to environmental conditions. • The controlled object is modeled considering the environmental conditions. • Database-driven modeling provides model estimation for a controlled object. • The control input is calculated using database-driven model predictive control. • The target excavation trajectory is modified based on prediction and decision. [ABSTRACT FROM AUTHOR]

In this paper, we present experimental results from the application of model predictive control (MPC) to inventory management in a real hospital. In particular, the stock levels of ten different drugs that belong to the same laboratory have been controlled by using an MPC policy. The results obtained after four months show that the adopted approach outperforms the method employed by the hospital and reduces both the average stock levels and the work burden of the pharmacy department. This paper also paper presents some practical insights regarding the application of advanced control methods in this context. [ABSTRACT FROM AUTHOR]

This paper addresses the design and implementation of a model predictive control framework for temperature control in buildings zones via direct control of their thermal energy inputs. Comfort-centric approach in ensured by selecting building thermal zones to be equal to the physical building rooms. The framework integrates different identification and estimation technologies, machine learning and model predictive control to assure systematic handling of non-modelled disturbances and offset-free control. It is envisioned as the lowest level in the hierarchical decomposition of building subsystems responsible for comfort and shaping the overall thermal energy consumption in building zones. The paper shows how it is deployed on a full scale occupied skyscraper building. To enable optimization of the whole building behaviour a special focus is put on developing the possibility for interaction and coordination with other building subsystems or energy distribution grids. This ensures the scalability of the approach, computational relaxation, technology independency, cost-effective implementation and enables upscaling towards the smart grid and smart city concepts where buildings play decisive roles. [Display omitted] • Direct control of thermal energy per zone. • Enabled interaction with other building subsystems. • Integral part for upscaling towards smart grid and smart city concepts. • Deployment and verification on a scale of the whole skyscraper building. • Modular service built on top of the existing building automation infrastructure. [ABSTRACT FROM AUTHOR]

In this paper, we focus on the challenging problem of model predictive control (MPC) for dynamics systems with high-level tasks formulated as signal temporal logic (STL). The state-of-art for STL synthesis mainly suffers from limited scalability with respect to the complexity of the task and the planning horizon, hindering the real-time implementation of MPC. This work tackles this issue by STL formula reformulation and input blocking. Specifically, simplifications are applied on disjunctive STL (sub)formulae recursively in the framework of MPC to limit formula size. We show that the simplified STL can be reformulated into mixed integer linear programming (MILP) constraints with a modifiable number of binary variables being required. The move blocking scheme is then employed to further reduce problem complexity by fixing input variables to be constant over several time intervals. In order to trade off the control performance and computational load, a blocking structure design with on-line correction is proposed. The extension of the proposed STL-MPC algorithm to uncertain systems is achieved through STL constraint tightening. Simulations and experiments show the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

This paper presents the development of a data-based model predictive control method for a semi-active suspension system with air springs and continuous damping. A continuous damping controller (CDC) has been devised for the system to alter its damping coefficient in real-time, capable of reducing the impact of external road disturbances. In this research, the damping force has been split up into a nominal force and a controllable additional force, allowing the system to be modelled as a linear time-invariant system, despite the inherent nonlinearity of air spring suspension systems. In consideration of such constraints given by the damper, a model predictive controller (MPC) has been devised with the goal of improving ride comfort. Additionally, Gaussian process regression (GPR) has been used to compensate for output estimation errors arising from model parameter uncertainties. The semi-active suspension system also featured a multichamber air spring with three available modes of stiffness. Hence, a stiffness controller has been designed to select an appropriate mode based on the predicted vehicle states given by the MPC using road preview information and a reduced full-car model. The proposed algorithm has been verified using computer simulations. The results showed that compensations for model errors made with GPR significantly improves ride comfort even in the presence of parameter uncertainties. Additionally, both the damping controller and stiffness mode selector were successfully implemented into an actual test vehicle. Vehicle test results showed the proposed algorithm to be robust and effective in enhancing ride comfort and reducing vehicle pitch motion. [ABSTRACT FROM AUTHOR]

Offshore boom cranes are complex nonlinear underactuated systems, whose control problems, when considering the 3-dimensional (3-D) model affected by ship-induced disturbances, are full of various challenges. In fact, it still remains an open problem to efficiently control the 3-D offshore boom cranes. Moreover, most existing works on offshore cranes concentrate upon designing the force/torque controller, whose performance degrades badly in the presence of friction. In this paper, considering the above issues, a novel model predictive control (MPC) method, which successfully considers the constraints of both input and output of the system, is proposed to achieve satisfactory control performance even under the effect of persistent ship roll and heave perturbations. Specifically, a discrete model is first obtained by some careful transformation and discretization on the unactuated dynamics equations, based on which a novel model predictive controller is constructed. To reduce the complexity of the system, the unactuated states and the accelerations of actuated states are considered as system states and control inputs respectively. After that, the requirements for the payload position accuracy and swing suppression as well as other system constraints, are taken into full account by converting them into input constraints to facilitate subsequent handling. At last, hardware experiments are implemented on a self-built testbed, with the obtained results clearly illustrating the effectiveness and robustness of the proposed method. • Proposing a model predictive control method for the three-dimensional offshore boom crane disturbed by ship motions. • Regarding only unactuated states as system states, with the unactuated dynamics model considered to develop controller. • Introducing various practical constraints to guarantee convenient applications. • Experiment results present satisfactory control performance. [ABSTRACT FROM AUTHOR]

This paper investigates the event-triggered model predictive control (MPC) for the series–series (SS) resonant inductive coupling power transfer (ICPT) system in electric vehicles (EVs). Different from most existing literature in ICPT systems, a data-driven modeling approach based on input–output data is proposed to describe the system dynamics and achieve constant voltage output in the presence of load variations. In the traditional MPC control strategies, the optimal control input should be calculated at each time instant to achieve the desired output voltage, which causes great computational burden. To tackle this issue, an event-triggered MPC mechanism is therefore developed to effectively alleviate the computational burden, which will generate the optimal control input only when the norm of the state error exceeds a predefined threshold. The effectiveness and reliability of the proposed event-triggered MPC control strategy are successfully verified by the experimental results. [ABSTRACT FROM AUTHOR]

Magnetic levitated planar motors are widely employed in flexible manufacturing and photolithography due to their characteristics of high precision, high acceleration, and low maintenance. In this paper, a novel predictor-based model predictive control is proposed to improve the tracking performance of a maglev planar motor which consists of a 2D Halbach array mover and stationary square coils. A Kalman filter and finite spectrum assignment based on an augmented system model are employed to forecast mover's location by compensating for the system delay due to data transmission and processing. The predicted location is fed back to model predictive control for control optimization under the limit of coil currents. Hildreth's quadratic algorithm is then applied to solve for the optimal control effort within the constraints. Comparative experiments demonstrate the effectiveness of the proposed control strategy, the total tracking error of which is much smaller than the result of a proportional–integral–derivative controller and standard model predictive control in all tests. Additionally, experimental results show that the proposed predictor-based model predictive control can tolerate higher speeds of the mover while retaining mover's steadiness. The experimental results suggest the proposed predictor-based model predictive control is an effective path-tracking strategy for magnetic levitated planar motors subject to delays. [ABSTRACT FROM AUTHOR]

Model-based design is proven to be essential for the development of control systems. This paper presents a real-time predictive control-orientated model (COM) for low-temperature combustion (LTC), dual-fuel, reactivity-controlled compression ignition (RCCI) engines. A comprehensive model-based design methodology must be capable of constructing an RCCI control-orientated model with high accuracy, high noise immunity, good response, predictivity in governing mechanisms, and low computation time. This work attains all of these for the first time for a cutting-edge RCCI marine engine. The real-time model (RTM) captures the key sensitivities of RCCI by controlling the total fuel energy and the blend ratio (BR) of two fuels, while also considering uncertainties arising from variations of inlet temperature and the gas exchange process. It provides not only the cycle-wise combustion indicators but also the crank-angle-based cylinder pressure trend. The RTM is derived by direct linearisation of a physics-based model and is successfully validated against experimental results from a large-bore, RCCI engine and the previously acknowledged UVATZ (University of Vaasa Advanced Thermo-kinetic Multi-zone) model. Validation covers both steady-state and transient modes. With high accuracy in several case studies representing typical load transients and air-path disturbance rejection tests, the model predicts maximum cylinder pressure (P max), crank-angle of 5 % burnt (CA5), crank-angle of 50 % burnt (CA50) and indicated mean effective pressure (IMEP) with root means square (RMS) errors of 8.6 %, 0.3 %, 0.6 %, and 0.6 % respectively. The average simulation time without any code optimisation is around 5 ms/cycle, offering sufficient real-time surplus to incorporate a semi-predictive emission submodel within the current approach. [ABSTRACT FROM AUTHOR]

This paper proposes an optimized finite control set model predictive current control (FCS-MPCC) strategy based on disturbance feedback rejection control (DFRC) method for interior permanent magnet synchronous motors (IPMSM) employed in high-speed trains. The strategy aims to mitigate the impact of parameter mismatch caused by permanent magnet demagnetization faults. Firstly, a discrete predicting model is established for IPMSMs, considering the mismatch in flux linkage, resistance, and inductance. The analysis is conducted to evaluate the impact of disturbances caused by demagnetization faults on the traditional FCS-MPCC. Secondly, second-order sliding mode disturbances observers (SMDOs) are utilized to detect real-time disturbances under demagnetization faults. The stability analysis of the second-order SMDOs is conducted using a Lyapunov function. Furthermore, disturbance controllers are developed to regulate the disturbances and generate compensating values for the FCS-MPCC. Lastly, experimental validation is performed on two IPMSMs to demonstrate the effectiveness of the proposed DFRC-based FCS-MPCC strategy against parameter mismatch issues caused by demagnetization faults. [ABSTRACT FROM AUTHOR]

This paper focuses on developing a control-oriented coal-fired utility boiler model for advanced economical Low-NO x combustion (ELNC) controller design. Two boiler combustion models are proposed in this paper: one is a mathematical model describing the key dynamics of the real-time boiler thermal efficiency and the furnace one-dimensional NO x concentration distribution under conventional fuel and overfire air operations; the other recast from the first model is a control-oriented grey-box model with a data-driven furnace combustion submodel. Simulation studies on static and dynamic properties of the first mathematical model indicate that the model can function as a real-time simulator for both advanced boiler combustion control laws testing and generating training and validation data for the control-oriented grey-box model. At the end of this paper, the control-oriented grey-box modelling procedure as well as an optional discrete time linear state-space model are summarised to facilitate model-based advanced combustion controllers design. [ABSTRACT FROM AUTHOR]

This paper focuses on the design of advanced core control systems for future generations of pressurized water reactors. The objective is to improve the flexibility of nuclear power plants to cope with the rapid growth of renewable energies. In practice, this means that the average coolant temperature, the axial power distribution of the reactor core and the position of the control rods have to be properly regulated during power variations. In previous work, conducted by the same authors, two promising approaches were investigated: 1) fixed-structure gain-scheduled control and 2) nonlinear model predictive control. Here, both methods are tested according to industry standards in an attempt to determine the best one for our problem. To achieve this, two different controllers are designed using a new multipoint kinetic model of the reactor core, which provides an accurate representation of the axial power distribution. The advantages and drawbacks of both design methodologies are discussed and then compared on PWRSimu, an intermediate complexity pressurized water reactor simulator developed by Framatome. • Current nuclear reactor core control systems still rely on SISO PID technology. • A new multipoint reactor core model is presented and used for controller design. • Two advanced MIMO controllers are designed and compared against industry standards. • Several useful methodological guidelines and practical insights are given in detail. • Both controllers are able to meet the real technical specifications of Framatome. [ABSTRACT FROM AUTHOR]

With the advent of on-board Direct Current (DC) power and propulsion systems, the transmission and delivery of energy on board of ships can be carried out more efficiently as it is being done using conventional direct-diesel or Alternative Current (AC) power and propulsion systems. However, the stability of DC voltage on-board of all-electric ships with a DC power and propulsion architecture is a critical issue that has drawn attention over the last few years. In this paper, a novel Model Predictive Control (MPC) approach is proposed for the diesel-generator shaft speed control and DC voltage regulation on-board of all-electric ships, focusing on the uncontrolled rectification at the voltage conversion stage. This work considers the prime mover as a Diesel–Generator–Rectifier (DGR) set which feeds propulsive asynchronous motors through a DC-link. First, a state space model dynamic model is developed for the DGR set and the DC-link. Then, the MPC-based approach is presented. The approach is based on Input–Output Feedback Linearization (IOFL) which is used for the linearization of the highly non-linear dynamics of the system. To increase the robustness of the algorithm, a tube-based technique is adopted which is implemented through a linear auxiliary control law. Different analyses are carried out to show that the proposed control strategy is capable of handling sudden changes in load conditions as well as adverse effects of Constant Power Loads (CPL). • The stability problem of ship power systems with uncontrollable rectifier is considered. • A MPC approach is proposed for the control of Diesel–Generator–Rectifier set. • The approach is capable of handling CPLs and fast changes in the loading conditions. • The approach is integrated with autopilot control mode concepts. • The controller performance is evaluated using high fidelity simulation models. [ABSTRACT FROM AUTHOR]

Electric Power Assisted Steering (EPAS) enables better driver's steering and user experience. It plays an important role for ADAS and automated driving (AD) features. EPAS control strategies are still in infancy entailing continuous monitoring and driver intervention. In fact, EPAS solutions have required more fail-safe strategies and optimal control algorithms for dynamics and road conditions. This paper presents a novel EPAS control strategy based on the parameterized Model Predictive Control (MPC) technique. The parameterized MPC scheme provides low computation effort enabling real-time implementation of the proposed control strategy. Experimental results highlighted success on tracking desired assistant torque without violating predefined operating constraints. • Control strategy with constraints handling is needed for Electric Power Steering. • MPC gathers optimal control profile with command variables and state constraints. • Explicit MPC demands high computational resource not available in embedded systems. • Exponential MPC reduces the computational effort to meet real-time requirements. • Validation in a HIL platform shows the effectiveness of the EPAS control strategy. [ABSTRACT FROM AUTHOR]

An autonomous hierarchical distributed control (AHDC) strategy is proposed for a building multi-evaporator air conditioning (ME A/C) system in this paper. The objectives are to minimize peak demand and energy costs, and to reduce communication resources, computational complexity and conservativeness while maintaining both thermal comfort and indoor air quality (IAQ) in acceptable ranges. The building consists of multiple connected rooms and zones. The proposed control strategy consists of two layers. The upper layer is an open loop optimizer, which only collects local measurement information and solves a distributed steady state resource allocation problem to autonomously and adaptively generate reference points, for low layer controllers. This is achieved by optimizing the demand and energy costs of a multi-zone building ME A/C system under a time-of-use (TOU) rate structure, while meeting the requirements of each zone's thermal comfort and IAQ within comfortable ranges. The lower layer also uses local information to track the trajectory references, which are calculated by the upper layer, via a distributed model predictive control (DMPC) algorithm. The control strategy is distributed at both layers because they use only local information from the working zone and its neighbors. Simulation results are provided to illustrate the advantages of the designed control schemes. [ABSTRACT FROM AUTHOR]

This paper develops a control strategy for a life-support device to be attached to an orbiting satellite to extend its operational life. The objective is met in such a way that the original satellite keeps operating without communications between the two systems (also valuable for energy efficiency). The case in which the original satellite is equipped with a feedback-linearization based controller is considered and the control law for the life-support is developed with the same methodology, obtaining a compensating control which recovers the performance of the original control strategy. Simulations validate the approach considering a real case study in various scenarios. • Control logic for a life-support device to extend satellites operational life. • No communication needed between the satellite and the life-support system. • Feedback linearization of the two-body spacecraft. • LQR and MPC mission controllers for attitude tracking. [ABSTRACT FROM AUTHOR]

This paper presents an innovative power hardware-in-the-loop (PHiL) development platform for railway pantograph testing. A novel real-time-capable finite-element catenary model is proposed in a train-fixed moving-coordinate formulation combined with an efficient absorbing boundary layer to accurately depict railway catenary dynamics in the region around the pantograph contact point. The complex catenary dynamics is accurately and efficiently modeled, including nonlinear effects like dropper slackening, and a model-predictive impedance controller realizes the task of accurately emulating the virtual catenary dynamics on a real-world pantograph test rig. • High-fidelity real-time-capable nonlinear finite element catenary model • Absorbing boundary control on a moving Euler-Bernoulli beam under axial pretension • Model-predictive impedance control for catenary emulation on a test rig • Validation of the proposed methods on a full-scale pantograph test rig [ABSTRACT FROM AUTHOR]

Abstract Poor model quality is one of the most frequent causes of performance deterioration in Model Predictive Controllers. As such, frequent model evaluation and correction is fundamental. Some assessment methods are reported in the literature, but most cannot deal with Model Predictive Controllers (MPCs) without fixed setpoints for controlled variables. Botelho et al. (2015, 2016a, 2016b) proposed a series of methods that include the controller tuning and the applied MPC implementation in the assessment procedure. Their main advantage is setpoint independence. This paper analyzes the application of these methods in an industrial MPC with control ranges. The system studied is an MPC of a fractionating column in a delayed coker unit of a refinery in Brazil. The results demonstrate that the method is capable of correctly quantifying the effect of modeling problems and identifying whether they are related to a model-plant mismatch or unmeasured disturbance. Highlights • The model assessment of a real industrial application of MPC in a delayed coker unit is shown. • The evaluated MPC is characterized by operating in control ranges. • The methods proposed by Botelho et al. (2015, 2016a, 2016b) are used for the modeling errors quantification. • The most important modeling problems are pointed by the method. [ABSTRACT FROM AUTHOR]

Abstract Model predictive control strategies have been applied successfully when controlling solar plants. If the control algorithm uses a linear model associated only to an operating point, when the plant is working far from the design conditions, the performance of the controller may deteriorate. In this paper, a gain scheduling model predictive control strategy is designed for the Fresnel collector field located at the Escuela Superior de Ingenieros de Sevilla. Simulation results are provided comparing the proposed strategy with another linear MPC controller showing a better performance. Furthermore, two real tests are presented showing the effectiveness of the proposed strategy. Highlights • Advances control techniques play an important role in operating solar plants. • Simple linear control strategies do not perform well in the entire operation range. • Thus, adaptative or nonlinear control strategies are needed. • A gain scheduling GPC is developed. • Simulations and real tests show the effectiveness of the proposed strategy. [ABSTRACT FROM AUTHOR]

Abstract This paper deals with the modeling and control of water distribution networks consisting of multiple pressure zones that are interconnected by fixed-speed pumps. A simplified control-oriented model with linear dynamics and non-convex constraints which considers the pressures and losses in the zones is derived. Based on the model, a periodic distributed model predictive scheme which is tailored to the non-convex constraints and minimizes an economic objective is introduced. The scheme uses event-triggered communication between the subsystems to reduce the communicational load. The proposed model and controller are evaluated using a model of the water distribution network of the city of Kaiserslautern, Germany. [ABSTRACT FROM AUTHOR]

Abstract This paper addresses the design of time-varying model predictive control of an autonomous vehicle in the presence of input rate constraints such that closed-loop stability is guaranteed. Stability is proved via Lyapunov techniques by adding a terminal state constraint and a terminal cost to the controller formulation. The terminal set is the maximum positive invariant set of a multi-plant description of the vehicle linear time-varying model. The terminal cost is an upper-bound on the infinite cost-to-go incurred by applying a linear–quadratic regulator control law. The proposed control design is experimentally tested and successfully stabilizes an autonomous Scania construction truck in an obstacle avoidance scenario. Graphical abstract Highlights • Guaranteeing stability is essential for autonomous vehicles. • The stability is guaranteed by terminal cost and constraints in the MPC. • The stability is further improved by considering the steering rate limitation. • Evaluation is performed in an autonomous Scania truck. • Experimental tests demonstrate the improved stability of the truck. [ABSTRACT FROM AUTHOR]

Abstract This paper proposes a model predictive control strategy for an Organic Rankine Cycle based waste heat recovery system. The control strategy uses a prediction model based on gain scheduling of local models, which results in a quadratic program to efficiently calculate the optimal control inputs. To ensure an optimal system operation, the reference values are obtained from a steady-state optimization. To capture a model-plant mismatch, the control concept features an EKF-based estimator of the model uncertainties. Simulations on a validated simulation model show that this control strategy can track the optimal reference very well, even for a large model-plant mismatch. [ABSTRACT FROM AUTHOR]

The paper concerns fault-tolerant control of a real battery assembly system which is under a pilot implementation at RAFI GmbH Company (one of the leading electronic manufacturing service providers in Germany). The proposed framework is based on an interval analysis approach, which along with max-plus algebra, allows describing uncertain discrete event system such as the production one being considered in this paper. Having a mathematical system description, a model predictive control-based fault tolerant strategy is developed which can cope with both processing, transportation and mobile robot faults. In particular, it enables tolerating (up to some degree) the influence of these faults on the overall system performance. As a result, a novel robust predictive fault-tolerant strategy is developed that is applied to the advanced battery assembly system. The final part of the paper shows the implementation and experimental validation of the proposed strategy. The proposed approach is tested against single as well as simultaneous faults concerning processing, transportation and mobile robots. [ABSTRACT FROM AUTHOR]

Increasingly in power systems, there is a trend towards the sharing of reserves and integration of markets over wide areas in order to enable increased penetration of renewable sources in interconnected power systems. In this paper, a number of simple PI and gain based Model Predictive Control algorithms are proposed for Automatic Generation Control in AC areas connected to Multi-Terminal Direct Current grids. The paper discusses how this approach improves the sharing of secondary reserves and could assist in achieving EU energy targets for 2030 and beyond. [ABSTRACT FROM AUTHOR]

This paper considers a Model Predictive Control (MPC) approach to micro-slip clutch control for driveline shuffle attenuation and longitudinal acceleration oscillation reduction in vehicles with an Automated Manual Transmission system. We introduce an original MPC formulation to consider the performance tradeoff between the clutch slip regulation, the torsional oscillation attenuation and the jerking reduction. A particular problem to be considered is handling saturation constraints on the transmitted torque. We employ a piecewise-affine linear model of the actuator-driveline system to take care of special manoeuvres that cause inversions in the transmission motion. On the methodological side, we present a switching MPC approach where the underlying optimization problem is a quadratic programme for a fast online controller implementation on real-world transmission control unit platforms. To show the effectiveness of the proposed approach, we present extensive simulation results and experimental tests performed on a prototype vehicle. [ABSTRACT FROM AUTHOR]

This paper studies the optimal dynamic operation of pumped storage power plants with variable and fixed speed generators. A control strategy for the dynamic operation is proposed based on a detailed physics-based plant model. It combines a stationary optimizer with a nonlinear model predictive control strategy and a nonlinear state observer for non-measurable system quantities. Contrary to earlier works on this topic, all system constraints are systematically taken into account, e.g., the pressure limits over the whole hydraulic system. The physics-based model allows for an easy model and controller parametrization and application to different plant topologies. This work studies the optimal combined operation of multiple (different) generators in the turbine and pump mode. The results show that a synchronous generator with a full-rated converter is the optimal choice for single-unit operation and that a synchronous generator with a converter-fed synchronous generator is the optimal combination of two units. A clear advantage of variable speed units is identified in the dynamic operation. The performance of the combined operation of variable and fixed speed generators is only slightly lower than for two variable speed units since the proposed control strategy allows to partially mitigate the shortcomings of the fixed speed unit by the variable speed unit. • Model predictive control allows for fast and safe pumped storage power plant operation. • Only one variable speed unit is needed to increase efficiency and flexibility. • Variable speed units allow for pumping power control. [ABSTRACT FROM AUTHOR]

This paper focuses on the problem of decision-making and control in an autonomous driving application for highways. By considering the decision-making and control problem as an obstacle avoidance path planning problem, the paper proposes a novel approach to path planning, which exploits the structured environment of one-way roads. As such, the obstacle avoidance path planning problem is formulated as a convex optimization problem within a receding horizon control framework, as the minimization of the deviation from a desired velocity and lane, subject to a set of constraints introduced to avoid collision with surrounding vehicles, stay within the road boundaries, and abide the physical limitations of the vehicle dynamics. The ability of the proposed approach to generate appropriate traffic dependent maneuvers is demonstrated in simulations concerning traffic scenarios on a two-lane, one-way road with one and two surrounding vehicles. [ABSTRACT FROM AUTHOR]

Abstract This paper aims to extend the operation time/range of an electric-powered multi-agent system (MAS) in leader-followertracking tasks, through integrating battery-based energy awareness with distributed tracking control synthesis. While MASs have gained much popularity nowadays, their use and deployment are often restricted by the operation time/range, due to the limited battery capacity. In an effort to overcome such a barrier, this work proposes to leverage a battery’s rate capacity effect to extend its runtime, which states that more energy can be drawn from the battery on less aggressive discharging rates. The battery-aware leader-follower tracking control design is then established in a model predictive control (MPC) framework, which strikes a tradeoff between tracking performance and energy consumption rates, accounts for the battery’s rate capacity dynamics, and incorporates the energy and power constraints. A distributed optimization method is used to distribute the MPC across the agents of the MAS. leader-follower tracking based on the proposed distributed MPC algorithm is then evaluated through a case study and compared with an existing algorithm in the literature. The simulation results show its effectiveness in extending the operation. Highlights • This is the first study of battery-aware leader-follower tracking control. • It uniquely leverages a battery’s dynamics to extend the tracking time/range. • A distributed model predictive control approach is designed. • Simulations validate the effectiveness and potential of the proposed results. [ABSTRACT FROM AUTHOR]

In this paper a linear MPC control scheme is proposed to address the motion problems of an autonomous wheelchair in a realistic environment. Thanks to an inner feedback-linearizing loop, the formulation of the model predictive control problem is simplified, allowing for a real-time computationally-efficient implementation. Thanks to the MPC framework, constraints like obstacle avoidance, actuator limitations, and passenger comfort have been included in the optimization problem. Experimental results show the effectiveness of the proposed scheme. [ABSTRACT FROM AUTHOR]

This paper presents a real-time advanced process control and monitoring scheme for a continuous flow micro-reactor producing an ether compound. A PLS calibration model is designed to predict the ether product yield using inline spectral data. This yield is then controlled to the desired setpoint by the proposed MPC scheme. Through real-time results, it is shown that the MPC controller is able to deliver accurate setpoint tracking even in the face of substantial plant-model mismatch caused by diluted catalyst. Furthermore, it is shown that the designed PCA monitor can effectively detect process/reaction faults, such as irregular reaction chemistry. [ABSTRACT FROM AUTHOR]

Given the importance of wave excitation force prediction in most advanced control schemes for wave energy converters, where every new wave force estimation becomes available every fraction of second, the main objective of this paper is to perform a short-term wave prediction that can meet a trade-off between low computational complexity, limited memory usage and accuracy. To this aim, two prediction algorithms are proposed using Kalman filtering theory. The proposed prediction methods are evaluated by using real measurements. [ABSTRACT FROM AUTHOR]

Three-phase rectifier is typically realized by six power switches. However, this rectifier is fault sensitive in power switches. To enable continued controllable operation, the grid phase with fault rectifier leg can be connected to center tap of the dc-link capacitors, known as the four-switch three-phase rectifier (FSTPR), using hardware reconfiguration. However, the symmetry of three-phase currents and reliable operation of the FSTPR cannot be retained due to the offset of the two-capacitor voltages. This paper proposes a finite control set model predictive control (FCS-MPC) to obtain the balanced three-phase current with the offset of two-capacitor voltages suppressed. The PI-Controller-free FCS-MPC with a second-order Luenberger observer is adopted to improve the dynamic performance of FSTPR. The performance of the proposed control scheme is illustrated by extensive simulation and experimental results. The comparison with the conventional voltage-oriented-control, which is based on PI controller and pulse width modulation (PWM), is also presented to show the superiority of the proposed FCS-MPC. [ABSTRACT FROM AUTHOR]

Model predictive controllers incorporate step response models for pairings of independent and dependent variables. Motivated by the fact that it may be time-consuming to conduct open-loop experiments to identify the step response models, the paper assesses the performance of closed-loop system identification on MPC-equipped plants, using both simulated and actual plant data. Pure feedback closed-loop system identification is shown to be effective for an identifiable simulated system and an industrial hydrogen production plant. The use of closed-loop system identification as a mechanism for monitoring model quality in MPC implementations may enhance the long-term sustainability of the implementation. [ABSTRACT FROM AUTHOR]

In this paper a novel approach for energy-optimal adaptive cruise control (ACC) combining model predictive control (MPC) and dynamic programming (DP) is presented. The approach uses knowledge about a given route to precalculate a position-dependent energy-optimal speed trajectory using DP while taking information like speed limits, road slope, and travel time into account during the optimization. A simple MPC framework is used to control the traction force of the host vehicle such that the vehicle speed follows the energy-optimal speed trajectory as good as possible while ensuring safety-related constraints like distance to a preceding vehicle or speed limits. To show the benefits of the approach, a comparison of the energy consumption between the host vehicle and the preceding vehicle on the same route is performed. For the speed profile of the preceding vehicle, data from real test drives is used. Simulations show that the approach leads to a significant reduction of the energy consumption compared to the preceding vehicle on the same route. Furthermore, the simulations indicate that the approach achieves high energy savings even with a poor prediction model for the preceding car. Moreover, the approach has shown to run very fast, indicating its real-time capability. [ABSTRACT FROM AUTHOR]

In this paper an offset-free model predictive control scheme is presented for fractional-order systems using the Grünwald–Letnikov derivative. The infinite-history fractional-order system is approximated by a finite-dimensional state-space system and the modeling error is cast as a bounded disturbance term. Using a state observer, it is shown that the unknown disturbance at steady state can be reconstructed and modeling errors and other persistent disturbances can be attenuated. The effectiveness of the proposed controller–observer ensemble is demonstrated in the optimal administration of an anti-arrhythmic medicine with fractional-order pharmacokinetics. [ABSTRACT FROM AUTHOR]

This paper presents a model predictive control of a semi-active suspension with a shift delay compensation using preview road information. A Model Predictive Control (MPC) methodology has been developed to optimize both ride comfort and road handling performance, where suspension states have been estimated through a model-based Kalman filter. The use of MPC in semi-active suspension control has proven to be suitable due to its ability to consider constraints when optimizing the objective function. A feasible region for the control inputs into the semi-active suspension has been determined from its mechanical limitations. However, the proposed Model Predictive Controller carries a sizeable computational load. Occurrences of "shift time delays" have been observed from the vehicle's Electronic Control Unit processing the algorithm. To combat this, a model prediction system, in which a full-car dynamic model and road preview information are utilized to predict vehicle suspension states, has been further proposed to compensate for the shift time delay. Preview road information has been obtain through the computation of relative vehicle motion and a temporal–spatial conversion. The overall algorithm has been evaluated via computer simulation studies. Simulation results have shown that the shift compensation algorithm provides considerable improvements with regards to ride comfort. A noteworthy section of this study is the successful implementation of the proposed algorithms into an actual vehicle, where the real world performance of the proposed algorithms have been assessed. It has been shown via vehicle tests that significant improvement in ride comfort can be obtained by the proposed MPC damping control. [ABSTRACT FROM AUTHOR]

In the heating, ventilation, and air-conditioning (HVAC) system of electric vehicles (EVs), an electric heater is often used for a reheating process that warms up the chilled evaporator outlet air for thermal comfort before it is supplied to the cabin. The usage of the electric heater can significantly reduce the driving range of an EV. Therefore, optimal control of the HVAC system with consideration of the reheating process becomes essential for an increased driving range. In addition, considering the primary role of cabin climate control, it is desirable to intelligently consider the passengers' thermal comfort in climate control design. In this paper, thermal comfort-conscious eco-climate control (TCC-ECC) based on model predictive control (MPC) is proposed to enhance the energy efficiency of the HVAC operation while ensuring passengers' thermal comfort. The MPC design uses a reduced-order HVAC system model based on an ideal vapor-compression cycle. For the integration of thermal comfort into the MPC, a new approach is proposed to obtain an approximate solution of a predictive mean vote (PMV)-based thermal comfort model, which aims to balance computational efficiency and prediction accuracy. With the proposed TCC-ECC, a parametric study is conducted to analyze the impact of weighting factors on energy consumption and thermal comfort under two different thermal load conditions. Then, the performance of the tuned TCC-ECC is evaluated in comparison with a rule-based (RB) controller and the baseline eco-climate control (ECC) without considering thermal comfort. In the performance evaluation, the proposed TCC-ECC demonstrates that with the inclusion of thermal comfort it performs better in terms of energy efficiency and thermal comfort than manually adjusting a target cabin temperature depending on the environmental thermal load. The energy consumption of the proposed TCC-ECC is 22.5% and 35.24% less than that of the RB controller at an ambient temperature of 24 °C and 35 °C, respectively, and 14.5% and 18.5% less than the baseline ECC at the same conditions, respectively. • Eco friendly climate control in an EV should consider both cooling and reheating. • Reheat process in an EV may consume significant energy. • Approximated solution for a PMV-based modified thermal comfort model. • Better energy efficiency by including thermal comfort in MPC-based climate control. [ABSTRACT FROM AUTHOR]

In seagoing ships in waves, the torque and thrust of the propeller may vary with the submergence depth of the propeller. A large ship motion can cause ventilation and loss of effective disc area, which degrade the ship's propulsion efficiency. Electric or electrified ships powered by electric motors can respond quickly to such load variations, unlike conventional ships powered by internal combustion engines. This paper proposes a model predictive anti-spin thruster control algorithm that can improve the propulsion efficiency of electric ships by controlling the rotational speed of the propeller with consideration of time-varying load conditions. The weight of the cost function for optimizing the propulsion efficiency is adjusted by using the propeller's submergence predicted by an autoregressive model. The feasibility of the proposed algorithm is shown through numerical simulations of ship motions and propeller depth variation in irregular waves. The performance of the proposed algorithm is validated and compared with that of shaft speed and anti-spin thruster controllers, and the results are discussed. [ABSTRACT FROM AUTHOR]

Abstract: This paper describes a new simplified model for controller design of open water channels that are relatively short, flat and deep: the integrator resonance model (IR model). The model contains an integrator and the first resonance mode of a long reflecting wave. The paper compares the integrator resonance model to the simplified models: integrator delay, integrator delay zero and filtered integrator delay and to the high-order linearized Saint-Venant equations model. Results of using the integrator resonance model in a model predictive controller applied in closed loop on a high-order non-linear Saint-Venant model of the first pool of the laboratory canal at Technical University of Catalonia, Barcelona are compared to the results of using the other simplified models in MPC. This comparison shows that the IR model has less model mismatch with the high order model regarding the relevant dynamics of these typical channels compared to the other simplified models. It is demonstrated that not considering the resonance behavior in the controller design may result in poor performance of the closed loop behavior. In order to demonstrate the validity of the simulation model used in this study, the controller using the IR model is also tested on the actual open water channel and compared to the results of the high-order non-linear Saint-Venant simulation model. The results of this comparison show a close resemblance between simulation model and real world system. [Copyright &y& Elsevier]