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The application of finite control set model predictive control (FCS-MPC) in six-phase permanent magnet synchronous motors (PMSMs) often faces a trade-off between computational burden and accurate voltage vector selection, as well as challenges related to harmonic components and torque generation. This paper introduces an improved model predictive current control (MPCC) method to address these problems. Firstly, 12 virtual voltage vectors are synthesized to improve torque output performance while suppressing harmonic currents. Then, to generate symmetrical switching signals and reduce switching loss, the largest basic vector used to synthesize the virtual vector is replaced by two medium vectors. Secondly, to solve the problem of the increased computational burden caused by the increase in discrete virtual vectors, a two-step vector selection method is proposed. In this method, each part is divided into several parts according to N, and the traditional cost function is also replaced by two-step functions. Different control performances can be achieved according to different values of N. Experimental results show that the proposed control scheme not only achieves stable current quality but also significantly improves steady-state performance throughout the entire speed range. [ABSTRACT FROM AUTHOR]

This paper presents an optimization method for hybrid energy systems based on Model Predictive Control (MPC), Long Short-Term Memory (LSTM) networks, and Kolmogorov–Arnold Networks (KANs). The proposed method is applied to a high-altitude wind energy work umbrella control system, where it aims to enhance the stability and efficiency of energy utilization. The work umbrella system integrates wind and solar energy sources, with energy stored in a battery and used to control the umbrella's operations. The MPC framework is employed to optimize control actions by solving a finite-horizon optimization problem, ensuring the battery State of Charge (SOC) remains within an optimal range. The LSTM network provides accurate predictions of environmental conditions, including wind speed and solar irradiance, which are essential for MPC's decision-making process. To address complex nonlinearities in the system, the KAN is utilized to model and approximate these dynamics, refining the LSTM predictions. The integration of these advanced control strategies enables the system to handle varying operational conditions and maintain optimal performance. The case study demonstrates the effectiveness of the MPC-LSTM-KAN approach, revealing improvements in the SOC stability, energy efficiency, and operational endurance of the high-altitude wind energy work umbrella system. The results indicate that this hybrid optimization method offers a robust solution for managing hybrid energy systems in dynamic environments. [ABSTRACT FROM AUTHOR]

In this study, a data-driven model predictive control (MPC) method is proposed for the optimal control of a doubly-fed variable-speed pumped storage unit. This method combines modern control theory with the dynamic characteristics of the pumped storage unit to establish an accurate dynamic model based on actual operating data. In each control cycle, the MPC uses the system model to predict future system behavior and determines the optimal control input sequence by solving the constrained optimization problem, thereby effectively dealing with the nonlinearity, time-varying characteristics, and multivariable coupling problems of the system. When compared with a traditional PID control, this method significantly improves control accuracy, response speed, and system stability. The simulation results show that the proposed MPC method exhibits better steady-state error, overshoot, adjustment time, and control energy under various operating conditions, demonstrating its advantages in complex multivariable systems. This study provides an innovative solution for the efficient control of doubly-fed variable-speed pumped storage units and lays a solid foundation for the efficient utilization of new energy sources. [ABSTRACT FROM AUTHOR]

Copyright of Przegląd Elektrotechniczny is the property of Przeglad Elektrotechniczny and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Conical tanks have a high application rate in industrial processes, especially in colloidal mills, chemical processes, and food processing. The use of conical tanks presents significant benefits because they contribute to sedimentation and reduce the accumulation of impurities compared to conventional cylindrical tanks. However, level control of a conical tank due to its shape requires advanced strategies to guarantee efficient control. In this research, a model predictive control (MPC) method was designed and implemented for the level control of a conical tank on a laboratory scale. To evaluate the performance of the controller, it was compared with a traditional proportional–integral (PI) controller, and the rise time, settling time, overshoot, and error in the steady state were analyzed when different set point changes were tested. In addition, the system was subjected to disturbances, and the MPC demonstrated better performance in a transient state, as well as smooth and stable action controls that allowed for an increase in the useful life of the actuator. In addition, an interactive graphical interface was developed that allowed a dynamic response in a real plant to be experienced; this provides an academic tool for designing control strategies before implementation in a real process. [ABSTRACT FROM AUTHOR]

Ensuring efficiency and safety is critical when developing charging strategies for lithium-ion batteries. This paper introduces a novel method to optimize fast charging for cylindrical Li-ion NMC 3Ah cells, enhancing both their charging efficiency and thermal safety. Using Model Predictive Control (MPC), this study presents a cost function that estimates the thermal safety boundary of Li-ion batteries, emphasizing the relationship between the temperature gradient and the state of charge (SoC) at different temperatures. The charging control framework combines an equivalent circuit model (ECM) with minimal electro-thermal equations to estimate battery state and temperature. Optimization results indicate that at ambient temperatures, the optimal charging allows the cell's temperature to self-regulate within a safe operating range, requiring only one additional minute to reach 80% SoC compared to a typical fast-charging protocol (high current profile). Validation through numerical simulations and real experimental data from an NMC 3Ah cylindrical cell demonstrates that the simple approach adheres to the battery's electrical and thermal limitations during the charging process. [ABSTRACT FROM AUTHOR]

This paper studies the consensus problem of Multi-Agent Systems (MASs) with input-delay using the Model Predictive Control (MPC) approach. Due to challenges such as input delay and communication graph, the traditional MPC is not efficient for the considered systems. In this regard, a novel MPC is developed for MASs with input delay. The main advantage of this paper is to design distributed controllers based on minimizing given cost functions which result in the improvement of the transient responses. For this purpose, first, distributed observers are derived to estimate the leader's states, perfectly. Then, distributed controllers are achieved through the MPC approach. Finally, numerical and practical examples are simulated to affirm the efficiency and applicability of the presented scheme. [ABSTRACT FROM AUTHOR]

The magnetic levitation (maglev) ball system is a prototypical Single-Input-Single-Output (SISO) system, characterized by its pronounced nonlinearity, rapid response, and open-loop instability. It serves as the basis for many industrial devices. For describing the dynamics of the maglev ball system precisely in the pseudo linear model, the long short-term memory (LSTM) based auto-regressive model with exogenous input variables (LSTM-ARX) is proposed. Firstly, the LSTM network is modified by incorporating the auto-regressive structure with respect to sequence input, allowing it to deduce a locally linearized model without the need for Taylor expansion. Then, the LSTM-ARX model is transformed into a linear parameter varying (LPV) state space model, and upon this foundation, a model predictive controller (MPC) is proposed. Specifically, when deducing the MPC, the deep learning-based model is linearized by fixing its state input at the current state, so that the nonlinear, non-convex optimization problem can be converted to a finite-horizon quadratic programming problem, thereby deriving the explicit form of MPC. To further enhance the efficiency of the controller in real-time control tasks, a predictive functional controller (PFC) is proposed. It employs multiple nonlinear functions to fit the control sequence, thereby reducing the number of decision variables of the on-line optimization problem in MPC. The proposed controller was successfully applied to the real-time control of the maglev ball system. Simulation and real-time control experiments have validated the improvement in transient performance and efficiency of the LSTM-ARX model-based PFC (LSTM-ARX-PFC). • The LSTM-ARX model is proposed, it describes global nonlinearity dynamics and bears pseudo linear structure. • The explicit form of a deep learning based computational efficient MPC is proposed and its stability is proved. • A predictive functional control method is proposed to furtherly facilitate computational efficiency in real-time control. [ABSTRACT FROM AUTHOR]

In recent years, the pulse-width-modulation (PWM) converter has been found to have extensive applications in renewable energy, industrial fields, and others. The high efficiency requirement is crucial to operating a PWM rectifier in various applications, in addition to the fundamental control objectives of sinusoidal grid currents and the correct DC bus voltage. Additionally, in practical application, another issue arises when the grid voltage frequently experiences distortion, leading to a distorted grid current and a significant rise in total harmonic distortion (THD). To resolve these problems, a model predictive virtual flux-based direct power control (MPVFDPC) with improved power loss performance is proposed based on an integrated switching state predetermination strategy. The proposed MPVFDPC for PWM rectifier inherits the merits of both virtual flux control and direct power control, which have fast dynamic performance and the grid current THD is considerably decreased under distorted grid voltage states. The proposed technique aims to minimize switching loss under ideal and distorted grid voltage states by exploiting the discontinuous modulation concept by using a switching state predetermination strategy. The MPVFDPC with switching state predetermination strategy is proven by employing it in experiments as well as simulations in comparison with previous models: predictive direct power control (Conv. MPDPC) and conventional MPVFDPC (Conv. MPVFDPC). The acquired waveforms and quantitative data are employed to prove the effectiveness of the developed algorithm. [ABSTRACT FROM AUTHOR]

Bipedal walking robots are advancing research by performing challenging human-like movements in complex environments. Particularly, wheeled-bipedal robots are used in many indoor environments by overcoming the speed and maneuverability limitations of bipedal walking robots without wheels. However, when both wheels lose contact with the ground, maintaining lateral balance becomes challenging, and there is an increased risk of toppling over. Furthermore, utilizing robotic arms similar to human arms, in addition to wheel-based balance, could enable more precise and stable control. In this paper, we develop a wheeled-bipedal robot that is able to jump and drive while also being able to cross its legs and balance on one leg (the OLEBOT). The OLEBOT is designed with a hand-fan-shaped end-effector capable of generating compensatory torque. By tilting the hand-fan-shaped end-effector in the opposite direction, OLEBOT achieves pitch control and single-leg balance. In jumping scenario, it imitates the arm movements of a person performing stationary high jumps, while utilizing a cam-based leg joint system to boost jump height. In addition, this paper develops a control architecture based on model predictive control (MPC) to ensure stable posture in driving, jumping, and one-legged balancing scenarios for OLEBOT. Finally, the experimental results demonstrate that OLEBOT is capable of maintaining a stable posture using a wheeled-bipedal system and achieving balance in a one-legged stance. [ABSTRACT FROM AUTHOR]

We present a straightforward way to solve a model predictive control problem for a power network system given as a nonlinear differential‐algebraic equation (DAE) in a distributed way using the consensus alternating directions method of multipliers (consensus ADMM) algorithm. While no convergence‐ or stability results are available for fully nonlinear DAE models, this gives unprecedented experimental evidence that power network systems of the presented structure allow to be controlled in this way, unlocking the numerous combined advantages of distributed and predictive control schemes in the context of energy distribution networks, as well as broadening the field of use for the consensus ADMM algorithm to nonlinear DAE models. [ABSTRACT FROM AUTHOR]

This paper studies the trajectory tracking of a constrained mobile robot under slippery conditions. The goal is to propose a controller for real‐time operations of time‐varying dynamics with insignificant execution time. Therefore, a Laguerre‐based model predictive control (LMPC) is designed, and robustness is provided with an linear matrix inequality (LMI)‐feedback controller. Moreover, a recursive least square (RLS) algorithm with a forgetting factor is utilized to identify the required parameters of the LMI‐based controller. LMPC and LMI‐based controller stability is achieved with suboptimal theory and Lyapunov function, respectively. This algorithm separates the nominal system and introduces new dynamics containing uncertainties. Furthermore, LMPC is removed from the online calculation, which dramatically reduces computation burden and time; consequently, online computation is dedicated to determining the LMI feedback. Simulations are provided to compare the proposed robust controller to its not robust counterpart. Finally, it is demonstrated that this controller diminishes the execution time considerably, making it incomparable to previous robust MPC strategies. [ABSTRACT FROM AUTHOR]

The prediction horizon is a key parameter in model predictive control (MPC), which is related to the effectiveness and stability of model predictive control. In vehicle control, the selection of a prediction horizon is influenced by factors such as speed, path curvature, and target point density. To accommodate varying conditions such as road curvature and vehicle speed, we proposed a control strategy using the proximal policy optimization (PPO) algorithm to adjust the prediction horizon, enabling MPC to achieve optimal performance, and called it PPO-MPC. We established a state space related to the path information and vehicle state, regarded the prediction horizon as actions, and designed a reward function to optimize the policy and value function. We conducted simulation verifications at various speeds and compared them with an MPC with fixed prediction horizons. The simulation demonstrates that the PPO-MPC proposed in this article exhibits strong adaptability and trajectory tracking capability. [ABSTRACT FROM AUTHOR]

This paper investigates optimizing the power exchange between electric vehicles (EVs) and the grid, with a specific focus on the DC-DC converters utilized in vehicle-to-grid (V2G) systems. It specifically explores using model predictive control (MPC) in synchronous boost converters to enhance efficiency and performance. Through experiments and simulations, this paper shows that replacing diodes with SIC MOSFETs in boost converters significantly improves efficiency, particularly in synchronous mode, by minimizing the deadtime of SIC MOSFETs during switching. Additionally, this study evaluates MPC's effectiveness in controlling boost converters, highlighting its advantages over traditional control methods. Real-world validations further validate the robustness and applicability of MPC in V2G systems. This study utilizes TMS320F28379D, one of Texas Instruments' leading digital signal processors, enabling the implementation of MPC with a high PWM frequency of up to 200 MHz. This processor features dual 32-bit CPUs and a 16-bit ADC, allowing for high-resolution readings from sensors. Leveraging digital signal processing technologies and advanced electronic circuits, this study advances the development of high-performance boost converters, achieving power outputs of up to 48 watts and output voltages of 24 volts. Electronic circuits (PCB boards) have been devised, implemented, and evaluated to showcase their significance in advancing efficient V2G integration. [ABSTRACT FROM AUTHOR]

The interstage subcooling vapor-injection technique has been introduced to transcritical CO 2 heat pumps to alleviate performance degradation under conditions of low ambient temperature and high water-outlet temperature. This type of solution holds considerable appeal and finds utility across a wide range of applications. However, the efficacy and efficiency of such systems are contingent upon their management, which frequently requires the implementation of suitable control systems. The primary objective is to enhance the overall system operational performance, resulting in tangible advantages in terms of economic viability, energy conservation, and environmental sustainability. To achieve these objectives, one can utilize both explicit and implicit optimization methods ranging from Model Predictive Control (MPC) to Extremum Seeking Control (ESC), each with its strengths and weaknesses. This paper compares the performance of an ad hoc integrated MPC strategy and a multi-variable ESC one. Based on the results of the system operation employing the latter strategy, a significant linear correlation was discovered between the optimal medium pressure and the optimal discharge and suction pressure. Consequently, an intrinsic self-optimization strategy for the medium pressure was developed. Subsequently, a dynamic system identification was performed on the new system, incorporating this proposed strategy. To optimize the system Coefficient of Performance (COP) while maintaining the water outlet temperature, ensuring the thermal load constraint, the MPC strategy was employed. The main focus of the MPC was to determine the optimal Electronic Expansion Valve (EEV) opening degree and water pump settings. The integrated MPC strategy was evaluated and compared to the multi-variable ESC strategy under various conditions, including fixed design conditions, changing ambient conditions, and step-change water outlet temperature. The results clearly demonstrated the effectiveness and superiority of the integrated MPC. [ABSTRACT FROM AUTHOR]

The increase of popularity of reactivity-controlled compression ignition (RCCI) is attributed to its capability of achieving ultra-low nitrogen oxides (NOx) and soot emissions with high brake thermal efficiency (BTE). The complex and nonlinear nature of the RCCI combustion makes it challenging for model-based control design. In this work, a model-based control system is developed to control the combustion phasing and the indicated mean effective pressure (IMEP) of RCCI combustion through the adjustments of total fuel energy and blend ratio (BR) in fuel injection. A physics-based nonlinear control-oriented model (COM) is developed to predict the main combustion performance indicators of an RCCI marine engine. The model is validated against a detailed thermo-kinetic multizone model. A novel linear parameter-varying (LPV) model coupled with a model predictive controller (MPC) is utilized to control the aforementioned parameters of the developed COM. The developed system is able to control combustion phasing and IMEP with a tracking error that is within a 5% error margin for nominal and transient engine operating conditions. The developed control system promotes the adoption of RCCI combustion in commercial marine engines. [ABSTRACT FROM AUTHOR]

Doubly fed induction generators (DFIG) find extensive application in variable-speed wind power plants, providing notable advantages such as cost-effectiveness, operational flexibility across varying speeds, and enhanced power quality. This research focuses on the control of DFIGs employed in variable-speed wind turbine configurations. A suitable mathematical model is chosen for representative systems following a comprehensive review of contemporary research. Subsequent analysis reveals the instability of the open-loop time response of the system. To address this instability, the initial approach involves the implementation of the conventional model predictive controller (MPC). However, the outcomes indicate that this controller falls short of delivering satisfactory performance despite the enhanced stability. In the subsequent phase, efforts are made to mitigate the impact of wind input variability by utilizing the Kalman filter, given its effectiveness in handling high variability. Following this, a novel methodology is introduced, which combines nonlinear MPC with the Lyapunov function. This method is based on the nonlinear model of the system. By using the Lyapunov function in the nonlinear MPC structure, the stability of the designed controller is guaranteed. To validate the proposed control approach, the results are compared with PID based controller in MATLAB/Simulink. The simulation results showed that the output variables of the modeled DFIG system achieve stability within a reasonable timeframe applying the input. [ABSTRACT FROM AUTHOR]

The study focuses on improving drone landing gear dynamics through an innovative auxetic foot design, leveraging Spider Monkey Optimization for Model Predictive Control adjustment, facilitated by an Arduino-MATLAB interface. The auxetic foot design incorporates materials with a negative Poisson ratio, which allows the foot to expand and enhance energy absorption during landings. This design improves stability and safety during the perched landing process. The SMO-MPC approach is used to optimise the control of the perched landing gear. SMO, inspired by spider monkey search behaviour, optimises auxetic foot control input sequences with the limits of rotational displacement (theta = 30 deg to -30 deg) on the prediction horizon to improve landing gear performance. The real-time implementation of SMO-MPC is achieved through an Arduino-MATLAB interface on quadcopter drone. A comparative analysis is conducted to evaluate the benefits of SMO-MPC compared to conventional MPC methods. The results show that the SMO-MPC approach with auxetic foot design surpasses conventional MPC methods in terms of landing performance with 14.6 % improvement in damping force control and control of aerodynamic stability with pitch of 34.16 %, yaw of 16.87 %, and roll of 31.74 %. [ABSTRACT FROM AUTHOR]

Permanent magnet synchronous linear motor (PMSLM) is widely used to meet the requirement of high dynamic accuracy positioning, such as in machine tools and devices of semiconductor manufacturing. A new 2-DOF control structure is proposed in this paper to improve the dynamic performance of the positioning servo system with PMSLM. Aiming at the position tracking performance, a control algorithm based on the model predictive control (MPC) is developed with position and speed as the feedback state variables. In addition, an extended state observer (ESO) is designed for the rejection of various disturbances, which are not involved in the control model and are regarded as the lumped disturbance to be estimated and compensated by the ESO. The experimental results show that, compared with the commonly used PPI controller (proportional position controller and proportional–integral speed controller), the proposed method enhances the position bandwidth and servo stiffness effectively. [ABSTRACT FROM AUTHOR]

This paper proposes a control strategy for active lateral secondary suspension that uses preview data. Based on a derived analytical model, a model predictive controller (MPC) is implemented. The influence of the track irregularities upon carbody lateral dynamics is considered explicitly. The controller developed is applied to a full-scale rail vehicle model. Ride comfort is evaluated according to EN 12299. Multibody simulations show that there is a significant increase in continuous ride comfort on poor-quality tracks. [ABSTRACT FROM AUTHOR]

Observer‐based model predictive control (MPC) for the discrete‐time switched systems suffered by event‐triggered mechanism and denial‐of‐service (DoS) attacks is discussed in this paper. We assume that the switch is slow enough and the attacker's energy is limited. To save network resources, an event‐triggered mechanism is designed based on dwell time and triggered error. Under the coupled influence of attack and trigger, a complex mismatch of system mode and controller mode occurs, which brings difficulties to the transformation of MPC optimization problem. To address this problem, a new performance index coefficient is designed by using the increasing/decreasing law of Lyapunov function. On this basis, the transformation of the optimization problem is realized. Then, the controller gain and observer gain for the attack‐free case are designed to guarantee the exponential convergence of the closed‐loop system. In the presence of attacks, we obtain the upper bound of attack duty cycle, below which the exponential convergence of the system can still be archived. An example is illustrated at last to verify the validity of the main results. [ABSTRACT FROM AUTHOR]

Maritime Autonomous Surface Ships (MASS) perfectly fit into the future vision of merchant fleet. MASS autonomous navigation system combines automatic trajectory tracking and supervisor safe trajectory generation subsystems. Automatic trajectory tracking method, using line-of-sight (LOS) reference course generation algorithm, is combined with model predictive control (MPC). Algorithm for MASS trajectory tracking, including cooperation with the dynamic system of safe trajectory generation is described. It allows for better ship control with steadystate cross-track error limitation to the ship hull breadth and limited overshoot after turns. In real MASS ships path is defined as set of straight line segments, so transition between trajectory sections when passing waypoint is unavoidable. In the proposed control algorithm LOS trajectory reference course is mapped to the rotational speed reference value, which is dynamically constrained in MPC controller due to dynamically changing reference trajectory in real MASS system. Also maneuver path advance dependent on the path tangential angle difference, to ensure trajectory tracking for turns from 0 to 90 degrees, without overshoot is used. All results were obtained with the use of training ship in real-time conditions. [ABSTRACT FROM AUTHOR]

Copyright of Ordnance Industry Automation is the property of Editorial Board for Ordnance Industry Automation and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In this paper, multiple autonomous underwater vehicle (multi-AUV) formation control with obstacle avoidance ability in 3D complex underwater environments based on an event-triggered model predictive control (EMPC) is proposed. Firstly, multi-AUV motion model systems are developed. The navigation reference trajectory of the follower AUVs can be obtained using a multi-AUV relative motion model. Secondly, in order to overcome the speed jump and obstacle avoidance problem in multi-AUV systems, compatibility constraints are presented in MPC that limit the uncertainty deviation of each AUV. The event-triggered mechanism (ET) is designed to decrease the computational load, which is based on the error between the optimal predicted and current state of the AUV. Finally, the effectiveness and superiority of the proposed algorithm are confirmed via simulation and compared with those of other algorithms. [ABSTRACT FROM AUTHOR]

Wastewater treatment plants (WWTPs) are large-scale and nonlinear processes with tightly integrated operating units. The application of online optimization-based control strategies, such as model predictive control (MPC), to WWTPs generally faces high computational complexity. This paper proposes an event-triggered approach to address this issue. The model predictive controller updates information and solves the optimization problem only when the corresponding triggered logic is satisfied. The triggered logic sets the maximum allowable deviation for the tracking variables. Moreover, to ensure system performance, the design of the event-triggered logic incorporates the effluent quality. By obtaining the optimal sequence for the effluent quality within the receding horizon of the MPC, the cumulative deviation between the predicted and desired effluent quality is analyzed to evaluate the performance within that horizon. Based on these two conditions, the need for adjusting control actions is determined. Even if the maximum allowable range for the tracking variables in the triggered logic design is set unreasonably, the consideration of effluent quality factors in the triggered conditions ensures good performance. Simulation results demonstrate an average reduction in computational effort of 25.49% under different weather conditions while simultaneously ensuring minimal impact on the effluent quality and total cost index and compliance with effluent discharge regulations. Furthermore, this method can be combined with other approaches to guarantee effluent quality while further reducing computation time and complexity. [ABSTRACT FROM AUTHOR]

Coal Mine Mobile Robots (CMRRs) are generally large in size and inertia, while narrow laneway space and bumpy terrain pose great challenges to CMRR's planning and control. Aiming at the trajectory planning and tracking problems of CMRR, a new trajectory class MINCO is derived in detail based on the properties of differential flat systems. A trajectory planning method based on MINCO trajectory and safety corridor constraints constructed with underground environmental constraints is further proposed. A trajectory tracking method based on model predictive control (MPC) is further proposed. The prediction model of MPC is constructed by a kinematics model and transformed into a standard quadratic programming problem according to the cost function of a trajectory tracking target. Finally, large quantities of field tests were carried out for the proposed approaches. The results show that the proposed planning algorithm based on MINCO trajectory can achieve good avoidance effects within 10 planning attempts in different obstacle scenarios, and the trajectory is smoother compared to the Fast-Planner algorithm, with shorter trajectory length and less planning time. The tracking error of MPC is always less than 0.05 m in different underground scenarios, having a more adaptable trajectory tracking effect than PID. [ABSTRACT FROM AUTHOR]

This paper evaluates experimentally a novel strategy for solving a variant of the differential game of target defense in presence of obstacles. The game is widely applied in the areas of military defense for protecting important equipment such as a ship, an aircraft, a moving vehicle, or a sensitive installation from a malicious attacker. The state‐of‐the‐art approaches mostly employ an offline optimization strategy that is only applicable to holonomic robots. Moreover, most of the approaches could not autonomously avoid obstacles or take into account uncertainties. As a consequence, this paper presents an online optimization technique, by designing a trade‐off parameter that integrates game theory with the model predictive control, which allows a nonholonomic defender to intercept the attacker while simultaneously defending the target. Simulations under different conditions as well as several indoor laboratory experiments validate the proposed approach. Moreover, performance is compared with a standard model predictive control approach. [ABSTRACT FROM AUTHOR]

On-orbit servicing for the failed spacecraft involves two necessary stages: on-orbit observing and approaching. For these two stages, a novel approaching and pointing tracking control scheme under motion constraints and input saturation is proposed in this paper. For the security of approaching, a relative position guidance law for specific surface hovering is proposed. Then, a model predictive controller based on quadratic programming algorithm is designed. For the continuous observation task, the optimal maneuver angular velocity under the motion constraint is planned through model predictive control. In order to reduce the dimensionality of the optimization problem to reduce the computational effort, only the attitude kinematics equation is used as the process model. Thereafter, the adaptive anti-saturation attitude controller is designed to track the optimal maneuver angular velocity. The anti-saturation auxiliary system and the adaptive update law are used to cope with the control torque saturation and the integrated uncertainties, respectively. Finally, the stability of the closed-loop system is demonstrated by Lyapunov method. Simulation results demonstrate that the proposed control scheme accomplishes the approaching and pointing tracking task, and finally service spacecraft maintains hovering above the tumbling target and observing its specific surface. • The problem of approaching and pointing tracking for tumbling target is studied. • A relative pose guidance law for specific surface hovering is proposed. • A novel control scheme under motion constraints and input saturation is proposed. •.The control scheme accomplishes the tracking task with optimal angular velocity. [ABSTRACT FROM AUTHOR]

In this paper, we investigate trajectory tracking and obstacle avoidance for a nonholonomic system subject to external disturbances by a nonlinear switched model predictive control (MPC) strategy. In the nonlinear switched MPC strategy, a potential field is introduced in a cost function to guarantee a smooth path for the nonholonomic system. A switched mechanism is designed to ensure switching stability by setting multiple Lyapunov functions in different areas. Different from traditional switched mechanisms, an average dwell time closely related to stability conditions is proposed to balance safety and stability in the whole process. Recursive feasibility is presented for the nonlinear switched MPC strategy in trajectory tracking and obstacle avoidance. Simulation results are provided to show effectiveness and superiority of the nonlinear switched MPC strategy by a two‐wheeled mobile vehicle. [ABSTRACT FROM AUTHOR]

The problem of mixed static and dynamic obstacle avoidance is essential for path planning in highly dynamic environments. However, the paths formed by grid edges can be longer than the actual shortest paths in the terrain since their headings are artificially constrained. Existing methods can hardly deal with dynamic obstacles. To address this problem, we propose a new algorithm combining Model Predictive Control (MPC) with Deep Deterministic Policy Gradient (DDPG). Firstly, we apply the MPC algorithm to predict the trajectory of dynamic obstacles. Secondly, the DDPG with continuous action space is designed to provide learning and autonomous decision-making capability for robots. Finally, we introduce the idea of the Artificial Potential Field to set the reward function to improve convergence speed and accuracy. In this paper, Matplotlib is used for simulation experiments. The results show that our method has improved considerably in accuracy by 8.11% -43.20% compared with theother methods, and on the length of the path and turning angle by reducing about 50 units and 340 degrees compared with DeepQ Network (DQN), respectively. We also employ Unity 3D to perform simulation experiments in highly uncertain environments, such as squares. [ABSTRACT FROM AUTHOR]

Industrial process control systems commonly exhibit features of time-varying behavior, strong coupling, and strong nonlinearity. Obtaining accurate mathematical models of these nonlinear systems and achieving satisfactory control performance is still a challenging task. In this paper, data-driven modeling techniques and deep learning methods are used to accurately capture a category of a smooth nonlinear system's spatiotemporal features. The operating point of these systems may change over time, and their nonlinear characteristics can be locally linearized. We use a fusion of the long short-term memory (LSTM) network and convolutional neural network (CNN) to fit the coefficients of the state-dependent AutoRegressive with the eXogenous variable (ARX) model to establish the LSTM-CNN-ARX model. Compared to other models, the hybrid LSTM-CNN-ARX model is more effective in capturing the nonlinear system's spatiotemporal characteristics due to its incorporation of the strengths of LSTM for learning temporal characteristics and CNN for capturing spatial characteristics. The model-based predictive control (MPC) strategy, namely LSTM-CNN-ARX-MPC, is developed by utilizing the model's local linear and global nonlinear features. The control comparison experiments conducted on a water tank system show the effectiveness of the developed models and MPC methods. [ABSTRACT FROM AUTHOR]

Real-time optimization (RTO) is a valuable tool for economic optimization of chemical process systems. Incorporating plant dynamics and model predictive control (MPC) behavior into the RTO problem can improve its performance by accounting for plant transitions under the action of its control system, resulting in a closed-loop dynamic RTO (CL-DRTO) formulation. This paper extends the formulation for direct inclusion of uncertainty handling. A robust multi-scenario CL-DRTO scheme which models the dynamic behavior of the plant and its MPC system under uncertainty is introduced. The method is applied and its performance evaluated in two nonlinear case studies, where an input clipping approximation scheme is used to reduce the computation time. The effects of number of scenarios and multiple sources of uncertainty are also investigated. • Scenario based stochastic formulation for robust dynamic RTO (DRTO). • Dynamic RTO uses prediction of plant response under action of constrained MPC. • Input clipping approximation used to reduce computation time. • SISO and MIMO case studies, with single and multiple sources of uncertainty. • Robust DRTO shown to outperform DRTO that is based on a nominal model. [ABSTRACT FROM AUTHOR]

A novel neural network (NN)-based parallel model predictive control (PMPC) method is proposed to deal with the tracking problem of the quadrotor unmanned aerial vehicles (Q-UAVs) system in this article. It is well known that the dynamics of Q-UAVs are changeable while the system is operating in some specific environments. In this case, traditional NN-based MPC methods are not applicable because their model networks are pre-trained and kept constant throughout the process. To solve this problem, we propose the PMPC algorithm, which introduces parallel control structure and experience pool replay technology into the MPC method. In this algorithm, an NN-based artificial system runs in parallel with the UAV system to reconstruct its dynamics model. Furthermore, the experience replay technology is used to maintain the accuracy of the reconstructed model, so as to ensure the effectiveness of the model prediction algorithm. Furthermore, a convergence proof of the artificial system is also given in this paper. Finally, numerical results and analysis are given to demonstrate the effectiveness of the PMPC algorithm. [ABSTRACT FROM AUTHOR]

This paper proposes a model predictive controller (MPC) design based on the optimal tip-speed ratio method for maximum power point tracking (MPPT) of a direct-driven permanent magnet synchronous generator (D-PMSG)-based wind energy conversion system (WECS). To eliminate system nonlinearity and time-varying characteristics, a control variable was added at the wind turbine and the system model was feedback-linearized to create a linear time-invariant system, reducing the computational burden of the MPC and improving system performance. MATLAB/Simulink simulations were performed and the results show that the linearized system has high fidelity. Compared to traditional MPC that use an operating point to linearize the system, it has better adaptability to turbulent wind speeds, improving the stability and rapidity of the system. [ABSTRACT FROM AUTHOR]

Autonomous outdoor operations of Unmanned Aerial Vehicles (UAVs), such as quadrotors, expose the aircraft to wind gusts causing a significant reduction in their position-holding performance. This vulnerability becomes more critical during the automated docking of these vehicles to outdoor charging stations. Utilising real-time wind preview information for the gust rejection control of UAVs has become more feasible due to the advancement of remote wind sensing technology such as LiDAR. This work proposes the use of a wind-preview-based Model Predictive Controller (MPC) to utilise remote wind measurements from a LiDAR for disturbance rejection. Here a ground-based LiDAR unit is used to predict the incoming wind disturbance at the takeoff and landing site of an autonomous quadrotor UAV. This preview information is then utilised by an MPC to provide the optimal compensation over the defined horizon. Simulations were conducted with LiDAR data gathered from field tests to verify the efficacy of the proposed system and to test the robustness of the wind-preview-based control. The results show a favourable improvement in the aircraft response to wind gusts with the addition of wind preview to the MPC; An 80% improvement in its position-holding performance combined with reduced rotational rates and peak rotational angles signifying a less aggressive approach to increased performance when compared with only feedback based MPC disturbance rejection. System robustness tests demonstrated a 1.75 s or 120% margin in the gust preview's timing or strength respectively before adverse performance impact. The addition of wind-preview to an MPC has been shown to increase the gust rejection of UAVs over standard feedback-based MPC thus enabling their precision landing onto docking stations in the presence of wind gusts. [ABSTRACT FROM AUTHOR]

The stability of formation flight is not only sensitive to external disturbances but also to data observed and transferred between sensors and unmanned aerial vehicles (UAVs). A multi-constrained model predictive control (MPC) strategy, combined with Kalman-consensus filter (KCF) and fixed-time disturbance observer (FTDOB) is developed for the formation control of multiple quadrotors here Firstly, KCF is used to effectively fuse the data shared in the formation with noise and uncertainty, which improves the applicability and robustness of the formation in complex environments. Secondly, FTDOB is able to estimate the external disturbances suffered by the quadrotor in a fixed time and provides real-time compensation for the controller. On this basis, an improved MPC (IMPC) is designed for each UAV of the formation, which improves the computational efficiency while ensuring the asymptotic stability of the system. Eventually, the capability and effectiveness of the proposed strategy are verified by simulation in terms of disturbance rejection and noise suppression, as well as good trajectory tracking of the formation. • A modified MPC strategy is proposed for the formation control. • The use of an FTDOB ensures that the estimation error converges to zero within a fixed time. • KCF is used to fuse the data shared in the formation with noise and uncertainty. [ABSTRACT FROM AUTHOR]

Focusing on autonomous driving algorithm development, this paper proposes a novel real-time trajectory planner formulated as a Nonlinear Model Predictive Control (NMPC) algorithm. The mathematical formulation of the problem is deeply reported and discussed. The numerical solution of the NMPC problem is the result of a novel genetic algorithm strategy that represents the innovative aspect of the work proposed. The aim of this paper is also to show how genetic algorithm can be a valid approach for motion planning strategies. Numerical results are discussed through simulations that show a reasonable behaviour of the proposed strategy in the presence of moving obstacles as well as in a wide range of road friction conditions. Moreover, a real-time implementation for research purposes is assumed as possible by considering computational time analysis reported. [ABSTRACT FROM AUTHOR]

This paper applied an artificial intelligence technique to control Variable Speed in a wind generator system. One of these techniques is an offline Artificial Neural Network (ANN-based system identification methodology, and applied conventional proportional-integral-derivative (PID) controller). ANN-based model predictive (MPC) and remarks linearization (NARMA-L2) controllers are designed, and employed to manipulate Variable Speed in the wind technological knowledge system. All parameters of controllers are set up by the necessities of the controller's design. The effects show a neural local (NARMA-L2) can attribute even higher than PID. The settling time, upward jab time, and most overshoot of the response of NARMA-L2 is a notable deal an awful lot less than the corresponding factors for the accepted PID controller. The conclusion from this paper can be to utilize synthetic neural networks of industrial elements and sturdy manageable to be viewed as a dependable desire to normal modeling, simulation, and manipulation methodologies. The model developed in this paper can be used offline to structure and manufacturing points of conditions monitoring, faults detection, and troubles shooting for wind generation systems. [ABSTRACT FROM AUTHOR]

This paper presents a torque controller for the energy optimization of the powertrain of an electric Formula Student race car. Limited battery capacity within electric race car designs requires energy management solutions to minimize lap time while simultaneously controlling and managing the overall energy consumption to finish the race. The energy-managing torque control algorithm developed in this work optimizes the finite onboard energy from the battery pack to reduce lap time and energy consumption when energy deficits occur. The longitudinal dynamics of the vehicle were represented by a linearized first-principles model and validated against a parameterized electric Formula Student race car model in commercial lap time simulation software. A Simulink-based model predictive controller (MPC) architecture was created to balance energy use requirements with optimum lap time. This controller was tested against a hardware-limited and torque-limited system in a constant torque request and a varying torque request scenario. The controller decreased the elapsed time to complete a 150 m straight-line acceleration by 11.4 % over the torque-limited solution and 13.5 % in a 150 m Formula Student manoeuvre. [ABSTRACT FROM AUTHOR]

The marine autonomous surface ship (MASS) has gained significant attention because of its wide application in waterborne transportation in recent years. The trajectory control of the MASS is crucial in ensuring safety, particularly in narrow navigation areas and urgent encounter situations. Model predictive control (MPC) is well known as a sufficient method to solve the tracking problem considering the actuator saturation with model uncertainties and environmental disturbances. However, due to the constraints on computing resources and hardware limits, the controller may fail to find a feasible solution for MPC within a reasonable amount of time, especially when the system models are coupled and complex. To improve the solution efficiency, this paper introduces adaptive constraints to reduce search space for optimization, i.e., the current tracking point's speed is transformed into input constraints in the MPC formulation to decrease the computation burden, as well as reduce mechanical wear on the thrusters with smaller amplitudes of control actions. Simulations are carried out to evaluate the effectiveness of the proposed method with different MPC forms. • A constraint-adaptive MPC strategy is proposed for nonlinear systems, which reduces the computational burden and improves tracking efficiency compared to traditional NMPC. • The ranges of adaptive constraints are constructed from the dynamic trajectories, which serves to further reduce the fluctuations of the ship and improve control smoothness. • The safety of MPC is guaranteed by considering the feasible region of control inputs, particularly when computation time is limited. [ABSTRACT FROM AUTHOR]

This paper presents an optimal control architecture for Pegasus, a novel quadruped wheel-legged robot with hybrid locomotion capabilities. The proposed control architecture comprises of a hierarchical motion planner and a model predictive controller (MPC) that optimizes motion planning and control in various stages. A command-based motion planner is implemented to map desired robot states to optimal joint positions and velocities. This enables the MPC to seamlessly integrate legged and wheeled locomotion as a single task. The legs are modeled as N-link manipulators, and parallel tracking MPC controllers are implemented to optimize torques. This approach results in improved motion control and comprehensive four-wheel independent steering mechanism maneuvers. The experiments and results demonstrate the practical feasibility and robustness of the proposed control approach, with Pegasus exhibiting stable balancing, precise motion control, and the ability to navigate through challenging paths. Overall, the proposed control architecture provides a promising solution for achieving hybrid locomotion capabilities in quadruped wheel-legged robots. • Control scheme for a novel Bio-Inspired Quadruped robot. • Centroidal optimum controller system design of a wheel-legged robot. • Optimum tracking torque controller for each leg deployed in embedded controllers. • Command-based optimized motion planner. [ABSTRACT FROM AUTHOR]

The constraints, input and output, are both important for path following in practical applications; unfortunately, few works have reported the results for path following with both the input and output constraints. A feasible solution for path following with the input and output constraints is presented in this paper, that is the constrained line of sight (LOS) guidance-based model predictive control (MPC) methodology. It contains two layers: the guidance layer and the control layer. The guidance layer is the barrier Lyapunov function-based LOS guidance for handling output constraints. The control layer is the MPC for handling input constraints. The design intent is that the output constraints are handled in the guidance layer, and the input constraints are handled in the control layer, relaxing the complexity of controller. Furthermore, the extended state observer (ESO) is also incorporated into MPC for approximation, minimizing the discrepancy between predicted model and plant. The effectiveness and superiority of the constrained LOS guidance-based MPC methodology are demonstrated by the simulation experiments. • The constrained LOS is realized based on barrier Lyapunov method. • Both the input and output constraints are addressed simultaneously. • Constrained LOS is presented for path following with input saturation. [ABSTRACT FROM AUTHOR]

Vibration control for high‐rise structures has become more important as buildings become more lightweight. Tuned liquid column dampers are an established approach for this problem, as they do not consume high amounts of energy while achieving high damping forces. The natural frequency of tuned liquid column dampers (TLCDs) can be changed semi‐actively by modifying their column geometry. As shown by previous studies, this approach increases the control performance of the damper and allows a larger application field. However, this modification simultaneously provokes strong nonlinearities, which need to be addressed for controller design. In this contribution a model‐based controller for such TLCDs is proposed, which explicitly accounts for the nonlinear dynamics of the coupled system consisting of a multi‐storey structure with a semi‐active TLCD. To account for various system dynamics of the structure, a hierarchical nonlinear model predictive controller (NMPC) in combination with a multi‐start strategy is formulated. In addition, an extrapolation algorithm is developed to determine an approximation of the future trajectory of the structure's induced excitation load. The excitation trajectory is then incorporated in the NMPC framework. For validation, a frequency response analysis and a case study are conducted on a 20‐storey benchmark structure, which is equipped with a semi‐active TLCD. The study is carried out in simulation, and the results are compared with passive vibration control. With the proposed hierarchical control, a significant improvement is shown in the reduction of structural vibrations over the relevant spectrum of excitation frequencies up to a mean of 50%. [ABSTRACT FROM AUTHOR]

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This paper proposes a model-based predictive control strategy based on mixed-integer linear programming for a photovoltaic power plant with battery energy storage. The control objective is to maximize the revenues from energy delivered from both photovoltaic panels and batteries to the grid in a deregulated electricity market. For each control interval, the proposed algorithm incorporates information on solar radiation, market prices, and the state of charge of the batteries to determine the intervals of energy injection into the grid to maximize the economic benefits. The proposed strategy considers the rate-based variable efficiency in the battery model and time-varying energies prices, thus providing a more general implementation than previous schemes proposed in the literature for the same purpose. Simulations considering the operational procedures of the Spanish market as a case study show that, by integrating the battery efficiency in the model, the proposed control strategy increments the economic benefits in 21% compared to previous results reported in the literature for the same operational conditions. Additionally, the proposed approach reduces the number of charge and discharge cycles, potentially extending the lifespan of batteries. [ABSTRACT FROM AUTHOR]

This paper proposes performance improvement of hydraulic turbine generation system (HTGS) using a subdivided finite set model predictive current control (SFS-MPCC). Recently, the differential pressure control valve (DPCV) is replaced by HTGS because of the frequent breakdown in DPCV caused by cavitation in the district heating systems. The HTGS consists of the hydraulic turbine, permanent magnet synchronous generator (PMSG), back-to-back (BTB) converter, and three-phase grid. Especially, BTB converter is composed of a generator-side inverter, DC-link capacitor, and grid-side inverter. In the generator-side inverter, a model predictive current control (MPCC) has been widely used because of its many advantages, such as robustness from parameter variation, fast dynamic response, and unnecessariness of gain tuning. The conventional MPCC uses only eight voltage vectors; thus, it makes a large current ripple, which is directly related to torque ripple. Therefore, to reduce the current and torque ripple, SFS-MPCC, which uses subdivided voltage vectors is proposed. An additional algorithm to reduce the calculation time is proposed because the subdivided voltage vectors increase the calculation time excessively. The effectiveness of the proposed SFS-MPCC is demonstrated by simulation and experimental results. [ABSTRACT FROM AUTHOR]

Ocean thermal energy conversion (OTEC) provides a feasible solution for sustainable and stable power supply in remote islands. In this paper, a thermodynamic model of the OTEC system is developed to study the dynamic response of power output and superheating degree to manipulated variables. Afterward, a control strategy of load following for the island OTEC system is proposed. Finally, two controllers, MPC and PI, are designed and compared under fast and slow load changes as well as disturbance rejection test. The results indicate that the power output of OTEC system is sensitive to the speed of working-fluid pump, reaching the adjustment range of 14.7 kW when the nominal speed decreases by 30%. In addition, model predictive control (MPC) shows better performance in both rapid and slow load-change modes. Especially, in the rapid load-change mode, the MPC controller could always keep the evaporator outlet superheating in the range of 2–4.5 °C, while the PI controller may introduce no superheat that may cause liquid impact. Both MPC and PI controller can stabilize the power output at the set point under one day's measured disturbance, while MPC controller makes the superheating degree of evaporator outlet within smaller temperature fluctuations (<0.2 °C). [ABSTRACT FROM AUTHOR]

In this paper, a comparative review for maximum power point tracking (MPPT) techniques based on model predictive control (MPC) is presented in the first part. Generally, the implementation methods of MPPT-based MPC can be categorized into the fixed switching technique and the variable switching one. On one side, the fixed switching method uses a digital observer for the photovoltaic (PV) model to predict the optimal control parameter (voltage or current). Later, this parameter is compared with the measured value, and a proportional–integral (PI) controller is employed to get the duty cycle command. On the other side, the variable switching algorithm relies on the discrete-time model of the utilized converter to generate the switching signal without the need for modulators. In this regard, new perspectives are inspired by the MPC technique to implement both methods (fixed and variable switching), where a simple procedure is used to eliminate the PI controller in the fixed switching method. Furthermore, a direct realization technique for the variable switching method is suggested, in which the discretization of the converter's model is not required. This, in turn, simplifies the application of MPPT-based MPC to other converters. Furthermore, a reduced sensor count is accomplished. All conventional and proposed methods are compared using experimental results under different static and dynamic operating conditions. [ABSTRACT FROM AUTHOR]

This paper presents a Model Predictive Control (MPC) design for the test mass release phase of the LISA space mission. LISA is a gravitational wave observatory consisting of a triangular constellation of three spacecraft. The gravitational waves are detected by measuring the relative distance between free falling test masses by means of a laser interferometer. Each test mass is a cubic body located inside an electrostatic suspension that is initially locked by a clamp mechanism. Once the plungers are retracted, the test masses are released with high initial offsets and velocities. To detect the gravitational waves, each test mass must be accurately positioned at the cage centre and its attitude must be aligned with the local cage frame. However, the low actuation authority of the electrostatic suspension along with the critical initial conditions, make the attitude and translation control a difficult task. MPC is a suitable technique for this application because it can systematically account for command saturations, state constraints and can provide optimal (or sub-optimal) control inputs by solving an optimization problem online. In this paper, an MPC controller is designed and validated by means of Monte Carlo simulations, achieving satisfactory results. • Model Predictive Control is a suitable method for the test mass release control problem of the LISA mission. • Model Predictive Control can deal with the low actuation authority of the electrostatic suspensions. • Model Predictive Control can deal with the state constraints of the test mass inside the cage. • Model Predictive Control can manage the operating mode transitions from Wide Range to High Resolution. [ABSTRACT FROM AUTHOR]