Current research predominantly focuses on enhancing vehicle driving stability when tires operate in the linear region. Thus, the effect of cornering stiffness on vehicle driving stability under nonlinear region remains an unresolved challenge. To address this issue, this paper proposes a novel nonlinear model predictive control (NMPC)-based four-wheel steering trajectory tracking controller that incorporates time-varying cornering stiffness. First, the longitudinal and lateral axle forces are estimated using a strong tracking unscented Kalman filter (STUKF); these estimated forces are then applied to a cornering stiffness estimator based on the unscented Kalman filter (UKF). Simulation tests under various scenarios are conducted using CarSim and Simulink, with comparisons made against the linear quadratic regulator (LQR) and conventional model predictive control (MPC) strategies. Finally, hardware-in-the-loop (HIL) experimental results verify that the proposed NMPC controller exhibits satisfactory trajectory tracking performance, stability, and real-time performance.

An application-oriented model predictive control strategy for optimal active power regulation of integrated large-scale wind–photovoltaic–storage power plant with hardware-in-the-loop test

Structural improvement and segmented model predictive control strategy for engine–battery coupled thermal management system under low-temperature conditions

Towards intelligent safety-aware control systems – A stochastic risk-based explicit model predictive control strategy

This paper proposes a continuous-time dynamic clearing model on a multilayer financial network to study systemic risk propagation and optimal intervention. The model incorporates interbank credit, equity crossholdings, and overlapping portfolios, and models bankruptcy as a jump event triggered by insolvency or illiquidity. Based on the system’s dynamic structure, we develop a model predictive control (MPC) framework that enables forward-looking and flexible allocation of limited bailout resources between debt relief and capital injection. Numerical results show that the proposed MPC strategy substantially outperforms both no-intervention and rule-based policies in terms of financial stability and resource efficiency. Compared with no intervention, the MPC strategy reduces the number of defaulting banks by approximately 56%. In contrast, the simple rule-based intervention achieves a reduction of about 48.83%, while improving rescue efficiency by approximately 28.57%. Overall, the framework provides a unified and effective approach to systemic risk control in financial networks.

The existence of stochastic sampling phenomena in wastewater treatment processes (WWTPs) breaks the assumption that the existing control strategies use periodic data, and the operational constraints of equipment and the requirements for effluent water quality impose constraints on the system's input and output. These factors collectively increase the difficulty of achieving stable control of dissolved oxygen concentration (DOC). To solve these problems, a data-driven model predictive control (DDMPC) strategy is proposed to achieve stable control of constrained WWTPs with stochastic sampling intervals. First, a DDMPC framework is designed, which involves designing the objective function based on the mathematical expectation of the predicted output and considering system input and output constraints. In this framework, the problem of stochastic data acquisition caused by stochastic sampling can be solved, and the stable operation of the system can be ensured under constraints. Second, a data-driven multimodel prediction structure is constructed based on the stochastic characteristics of the sampling intervals. Specifically, fuzzy neural networks (FNNs) that match possible sampling intervals are established, thereby providing predictive outputs for the control process at the corresponding sampling instants. Third, a controller solving algorithm based on the generalized multiplier method is proposed, in which the constrained optimization problem within the model-predictive control (MPC) framework is reformulated by incorporating system constraints into the objective function as penalty functions to obtain the optimal control input that satisfies the constraints. Finally, the stability of the proposed DDMPC strategy is demonstrated, and its effectiveness is verified through the simulations on the benchmark simulation model No. 1 (BSM1). The results show that the proposed DDMPC strategy can achieve stable control of DOC in constrained WWTPs with stochastic sampling intervals.

Autonomous electric vehicles require precise coordination between motor torque control and vehicle trajectory tracking. However, permanent magnet synchronous motors (PMSMs) exhibit nonlinear behavior-particularly inductance variation and flux weakening-that conventional model predictive control (MPC) methods with fixed parameters cannot adequately capture, leading to degraded tracking performance during dynamic transitions. To address these challenges, this paper proposes an adaptive linear MPC (AL-MPC) strategy that integrates three key components. First, a moving-average-filtered third-order generalized integrator flux observer provides real-time estimation of electromagnetic torque and d-q axis stator reactance, enabling the predictive model to adapt to operating-point-dependent PMSM nonlinearities. Second, a Taylor-series-based linearization formulates a unified nine-state predictive model coupling motor currents, wheel velocity, yaw angle, and lateral position, which is updated at each sampling instant to reflect current operating conditions. Third, an active-set quadratic programming optimizer efficiently computes optimal d-q voltages and steering angle while enforcing current, voltage, and state constraints. The AL-MPC is validated through MATLAB/Simulink simulations and hardware-in-the-loop (HIL) testing on a TI C2000 embedded controller. Compared with classical seven-dimensional linear MPC, the proposed method achieves 99.9% reduction in yaw mean absolute error (MAE), 65% reduction in lateral position root mean square error, and 93% reduction in steering signal variation under varying velocity conditions. Against adaptive nonlinear MPC, it attains 77.7% lower yaw MAE, 94.6% lower lateral MAE, 95.4% reduction in velocity ripple, and 67% lower voltage ripple during rapid acceleration with torque disturbances, while requiring 3.7% less computation time. The HIL results confirm real-time feasibility with a total execution time of 9.65 ms per control cycle.

To meet the requirements for the automatic alignment, insertion, and inspection of guide-tube opening pins on the upper core plate in a component pool during refueling outages of nuclear power units, this paper proposes a cognition-enhanced visual-servoing framework that integrates geometric cognition-based compensation, observation-confidence modeling, and constraint-aware optimal control. The framework addresses the key challenge posed by the coexistence of long-term geometric drift and underwater observation uncertainty. Specifically, historical closed-loop data are leveraged to learn and compensate for systematic geometric errors online, substantially improving coarse-positioning accuracy. In addition, an explicit confidence model is introduced to quantitatively assess the reliability of visual measurements. Building on these components, a confidence-driven, finite-horizon, constrained model predictive control strategy is designed to achieve safe and efficient finite-step convergence while strictly respecting actuator physical constraints. Ground experiments and deep-water component-pool validations demonstrate that the proposed method reduces coarse-positioning error by approximately 75%, achieves stable sub-millimeter alignment with an ample engineering safety margin, and effectively decreases erroneous insertions and the need for manual intervention. These results confirm the engineering applicability and safety advantages of the proposed cognition-enhanced visual-servoing framework for underwater alignment tasks in nuclear component pools.

In this paper, frequency control of shipboard microgrids is achieved in the presence of measurement noise, dynamic uncertainty, and actuator faults. Measurement noise arises from incorrect signal processing, electromagnetic interference, converter switching dynamics, mechanical vibrations from propulsion and generators, and transients caused by sudden changes in load or generation. Actuator faults are caused by intense mechanical vibrations, temperature-induced stress, degradation of power electronic devices, communication latency, and wear or saturation in fuel injection and governor components. To regulate the frequency deviation under these challenges, a cross-entropy-based fault-tolerant model predictive control method, utilizing a discrete-time linear Kalman filter, is developed. Firstly, the discrete-time linear Kalman filter ensures that uncertain states of the shipboard microgrids are measurable in a noisy environment. Afterward, the model predictive control scheme is employed to obtain an optimal control input based on the measurable states. This controller ensures the frequency regulation of shipboard microgrids in the presence of measurement noise. Furthermore, a fault-tolerant control technique that utilizes the concept of cross-entropy is extended to provide a robust controller that verifies the frequency regulation of shipboard microgrids with actuator faults. To demonstrate the stability of the closed-loop system of the shipboard microgrids based on the proposed controller, considering the effects of measurement noise, state uncertainty, and actuator faults, the Lyapunov stability concept is employed. Finally, simulation results in MATLAB/Simulink R2025b are provided to show that the proposed control method for frequency regulation in renewable shipboard microgrids is both effective and practicable.

The integration of inverter-based generation units, such as photovoltaic systems, wind turbines, and vehicle-to-grid (V2G) technologies, has introduced new challenges in maintaining power and frequency stability in modern power systems. Virtual Synchronous Generators (VSGs) have emerged as a promising solution to enhance system stability; however, existing control methods often lack the robustness and flexibility needed to address deliberate and unplanned outages effectively. This paper presents a novel approach for optimizing power control in generation units using a Long Short-Term Memory (LSTM)-based machine learning method. The proposed LSTM-based controller provides a fast and real-time response, ensuring robust and flexible performance under varying operational conditions. Unlike traditional controllers, the proposed method effectively handles nonlinearities and uncertainties associated with inverter-based units. Additionally, it effectively balances technical and economic aspects of power system operation by minimizing oscillations and optimizing resource utilization. The proposed approach is benchmarked against conventional control methods through a detailed simulation-based comparative analysis against a linear Model Predictive Control strategy under identical operating conditions. Simulation results indicate that the proposed controller reduces frequency deviations by up to 66.7%, voltage deviations by 62.5%, and total operational cost by approximately 11.3%, while achieving nearly 90% faster dynamic response, validating its effectiveness for modern power systems.

To meet the miniaturization and lightweight requirements of inter-satellite laser communication, this study investigates the servo control system of a silicon-based optical phased array (OPA). Based on the far-field radiation model for beam steering of the silicon-based OPA, combined with thermo-optic phase modulation technology and time domain response, the transfer function of the silicon-based OPA is established. To address noise and disturbances encountered during actual tracking, a silicon-based OPA beam tracking method for satellite platform vibration is proposed. The control algorithm employs a Kalman filter-based Model Predictive Control (KF-MPC) strategy. The advantages of the designed control algorithm were verified through simulations and experiments. Step response simulation results show that compared with the traditional PID control algorithm, the proposed algorithm reduces overshoot by 15.1% and shortens the response time by 76.4%. Sinusoidal tracking simulation results indicate a 27.15% improvement in tracking accuracy over the traditional PID algorithm. Experimental results demonstrate that the tracking accuracy of the servo control system with the proposed algorithm is 155.45 μrad, while that using the PID algorithm is 210.97 μrad, representing a 26.31% improvement in tracking accuracy. This research provides a valuable reference for the application of silicon-based OPA in inter-satellite laser communication.

Abstract The tuned viscous mass damper (TVMD), composed of an inerter, a tuning spring, and a damping component, has emerged in recent years as one of the most successful structural control devices. The TVMD efficiently absorbs vibration energy through the damping component by resonating the inerter with the tuning spring. However, concerns have been raised regarding the excessive reaction forces that are exerted on the primary structure by the TVMD, potentially leading to structural damage. To address this issue, this study proposes an active control strategy that employs a tuned inertial mass electromagnetic transducer (TIMET). The TIMET has the same configuration as the TVMD but replaces the damping component with an electromagnetic motor, which is controlled by using a model predictive control (MPC) algorithm with acceleration feedback. To evaluate the structural control performance and the control force generated by the proposed strategy, a numerical example involving a five-story shear building model with TIMETs installed between floors is presented. Comparative analyses are conducted using models lacking control devices but including TVMDs, subjected to four earthquake records. The results demonstrate that the force-constrained MPC strategy effectively limits the control forces of the TIMETs, while simultaneously reducing the response displacements and accelerations of the primary structure.

Pursuit–evasion involves coupled, antagonistic decision-making and is prone to local-optimal behaviors when solved online under nonlinear dynamics and constraints. This study investigates a dual-AUV pursuit–evasion problem in ocean-current environments by integrating game theory with model predictive control (MPC). We formulated a game-theoretic MPC scheme that optimizes pursuit and evasion actions over a finite receding horizon, producing Nash-like responses. To solve the resulting nonconvex and multi-modal optimization problems reliably, we developed an Enhanced Adaptive Quantum Particle Swarm Optimization (EA-QPSO) method that incorporates chaos-based initialization and adaptive diversity-aware exploration with stagnation-escape perturbations. EA-QPSO is benchmarked against representative solvers, including fmincon, Differential Evolution (DE), and the Marine Predator Algorithm (MPA). Extensive 2D and 3D simulations demonstrate that EA-QPSO mitigates local-optimum trapping and yields more effective closed-loop behaviors, achieving longer escaping trajectories and more persistent pursuit until capture under the game formulation. In 3D scenarios, EA-QPSO better preserves high-speed motion while coordinating agile angular-rate adjustments, outperforming competing methods that exhibit premature deceleration or degraded maneuvering. These results validate the proposed framework for computing reliable competitive strategies in constrained underwater pursuit–evasion games.

ABSTRACT We propose a data‐driven reinforcement learning model predictive control (DD‐RLMPC) scheme for linear time‐invariant (LTI) systems. The scheme integrates reinforcement learning (RL) and data‐driven model predictive control (DD‐MPC) through value iteration. Also, a value function approximation technique is applied to approximate the terminal cost, thereby providing a direct method based on behavioral systems theory, thus using historical operation data to bypass the system identification step. The scheme first operates offline to derive the optimal approximated value function, and then operates online for controller design. Furthermore, the proposed DD‐RLMPC scheme offers flexibility in selecting the prediction horizon, thus provides a potential to significantly reduce the computational burden compared to the terminal equality‐constrained DD‐MPC methods. We demonstrate the convergence, stability, and feasibility of the proposed DD‐RLMPC scheme, with properties verified by simulation results.

The Doubly fed induction generator (DFIG) occupies an important position in current wind turbines. With the continuous increase in wind power penetration, its Low-voltage ride-through (LVRT) capability becomes more important. When facing dynamic faults, traditional control strategies have the problems of slow response speed and insufficient control accuracy, which makes it difficult to meet the ride-through requirements. This article proposes a coordinated control strategy for LVRT of the DFIG based on Model predictive current control (MP-CC) combined with a crowbar circuit. This method first establishes a mathematical model of the doubly fed wind turbine and then uses the rolling optimization and constraint-handling capabilities of model predictive control to achieve rapid dynamic adjustment of the rotor current and grid-side power. The simulation results show that the optimized model predictive control strategy can more effectively control the rotor over current and make the system have better stability, which can effectively improve the LVRT capability of the system and is of great significance to the current rapidly developing wind power generation technology.

Data-driven distributed EMPC for economic optimization of interconnected systems: A Hankel matrix approach.

A model predictive control strategy for three-phase interleaved parallel boost converter based on super-twisting sliding mode observer

Abstract This paper presents an optimal trajectory planning and tracking control framework for a tilt-rotor Vertical Take-Off and Landing (VTOL) aircraft during longitudinal transition flight. Specifically, the Multiple Shooting Method (MSM) is employed to generate a flight trajectory that consists of takeoff, transition, and level flight. Unlike prior works, MSM yields a dynamic flight trajectory rather than a quasi-equilibrium trajectory. After that, a Linear Parameter-Varying (LPV) Model Predictive Control (MPC) scheme is developed to track the dynamic trajectory. The linear parameter-varying-model predictive control (LPV-MPC) scheme efficiently accounts for varying nonlinearities by previewing the scheduling parameters (velocity, pitch rate, and rotor tilting angle) along the dynamic flight trajectory. The LPV-MPC formulates a convex optimization problem that minimizes the weighted tracking error and control inputs while satisfying constraints of states and control inputs. The proofs of stability and recursive feasibility are also presented. The tracking control is evaluated in simulation scenarios of initial state error and measurement noises. Furthermore, the LPV-MPC is compared with nonlinear MPC and further validated in hardware-in-the-loop (HIL) experiment with excellent tracking performance and computational efficiency for real-time implementation.

The integration of renewable energy sources (RESs) introduces significant challenges related to uncertainty and intermittency in power grids. While Artificial Intelligence (AI) offers promising solutions for Virtual Power Plants (VPP) optimization, existing approaches often treat load forecasting, system dispatch, and demand response as loosely coupled components, limiting their ability to holistically manage these deep uncertainties. To address this, we propose a novel AI-enhanced multi-timescale optimization strategy that creates a synergistic, integrated framework. Methodologically, the approach begins with an attention-augmented Bidirectional Long Short-Term Memory (BiLSTM) model that generates high-fidelity spatiotemporal load forecasts, providing crucial spatial-aware inputs often overlooked by traditional models. These enhanced forecasts are then leveraged by a Model Predictive Control (MPC) strategy for more robust and proactive day-ahead and intraday dispatch. Crucially, the framework integrates a dynamic demand response (DDR) mechanism that is directly coupled with real-time MPC outputs, ensuring that load flexibility is mobilized based on immediate system needs rather than static signals alone. Simulations, driven by real-world operational data, confirm that this integrated strategy not only reduces operational costs and improves forecasting accuracy but also establishes a more resilient and adaptive VPP operational paradigm compared to prior AI-based methods.

To enhance the path-tracking performance and stability of autonomous vehicles (AVs), this paper proposes an adaptive robust model predictive control (ARMPC) strategy. First, a path-tracking model is established by integrating a two-degree-of-freedom (2-DoF) vehicle dynamics model with a preview error model. Then, to address model uncertainties caused by variations in tire cornering stiffness, a linear parameter varying (LPV) model with four polytopic vertices is constructed. Subsequently, a stability envelope for vehicle dynamics is defined, specifying the operational boundaries for the sideslip angle and yaw rate. Based on this envelope, a stability index is proposed to quantitatively evaluate vehicle stability, and a weight adaptive mechanism is designed to coordinate the objectives of path tracking and stability control. The min-max optimization problem with adaptive weights and multiple constraints is solved using a robust model predictive control (RMPC) framework based on linear matrix inequality (LMI) to determine the optimal front steering angle. Finally, co-simulation results from Carsim and MATLAB demonstrate that the proposed strategy significantly improves both path-tracking accuracy and vehicle stability under various velocities and road adhesion conditions.