Optimal load-sharing in isolated DC microgrids using an adaptive model predictive control

Trajectory planning and tracking for UAVs with deep reinforcement learning and adaptive nonlinear MPC

Hierarchical cooperative adaptive model predictive control for swarm self-assembly of large-scale spacecraft

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.

With the rapid development of autonomous driving, path tracking has emerged as a pivotal research direction. Model predictive control (MPC) has become one of the most prevailing approaches for path tracking, owing to its superior capacity in dealing with multi-constrained control problems and compatibility with the symmetry of vehicle dynamic systems. Nevertheless, conventional MPC suffers from performance degradation in path tracking when vehicle localization drift occurs, referring to the noticeable deviation between sensor-measured position and actual physical position over time, which is mainly induced by sensor noise and outliers. To overcome these limitations and enhance the accuracy and stability of path tracking, this paper presents a location-adaptive model predictive control framework. Specifically, a supervisor is designed to detect localization drift, and a Runge–Kutta-based location estimator is activated to predict the current vehicle state once drift is identified. Furthermore, a linear time-varying MPC is utilized to compute the desired control input for real-time multi-objective optimization. A set of co-simulations based on Simulink and CarSim are conducted to validate the effectiveness of the proposed strategy. Numerical results demonstrate that the presented method outperforms traditional MPC in terms of tracking accuracy and stability under localization drift conditions.

Adaptive dual-layer MPC for real-time energy management of an inland diesel–LNG–battery vessel on the Yangtze River

Physics-aware adaptive model predictive control guided by reinforcement learning for enhanced cyber-resilience of building energy systems

Distributed-drive commercial vehicles are prone to skidding or rolling over when operating on low-friction roads or negotiating tight curves. To address this issue, this paper proposes a control strategy based on Adaptive Model Predictive Control (AMPC) to coordinate yaw and roll stability of distributed-drive commercial vehicles. By analyzing the improved β−β˙ phase-plane boundary and the roll stability threshold, this study identifies the yaw rate, sideslip angle, and predicted lateral load transfer rate (PLTR) as key indicators for vehicle stability assessment. The AMPC controller employs these metrics to dynamically adjust the control weights associated with yaw and roll stability in real time, thereby calculating the required additional yaw moment, which is applied through optimal torque distribution among all four wheels to achieve coordinated control. Finally, experiments are conducted on a Simulink-TruckSim co-simulation platform to assess the performance of AMPC. Compared with the conventional MPC method, the proposed approach achieves obvious improvements in both roll and yaw stability under sinusoidal and fishhook operating conditions.

This paper presents a vibration suppression method for two-inertia mechanical transmission systems, combining adaptive horizon model predictive control (AHMPC) with the equivalent-input-disturbance (EID) approach. First, conventional model predictive control (MPC) is extended to an adaptive horizon formulation to reduce computational cost while effectively attenuating vibrations. Second, a quadratic-programming-based method is designed to efficiently solve the AHMPC optimization problem. Third, the EID approach is integrated to compensate for external disturbances. Finally, simulation studies on a two-inertia system demonstrate the effectiveness of the proposed method in achieving significant vibration suppression.

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.

This paper presents an Adaptive Model Predictive Control (AMPC) strategy for robust load-frequency control (LFC) in single-area and double-area power systems under load variations, parameter uncertainty, and renewable energy disturbances. The controller integrates online system identification using Recursive Least Squares (RLS) with a receding-horizon optimization framework to ensure real-time model adaptation and constraint-aware predictive regulation. Simulation results demonstrate that the proposed AMPC significantly improves transient and steady-state performance compared with conventional PI/PID controllers. In single-area systems, the AMPC achieves settling times of 0.5-1s, compared with 30s for PI, and eliminates overshoot while reducing undershoot from 4.5 × 10⁻³ to 1 × 10⁻³. Under dynamic and wind disturbances, peak-to-peak deviations are reduced to ≈ 0, whereas PI exhibits deviations up to 26.5 × 10⁻³. In double-area systems, the AMPC reduces settling time from 20 to 40s (PID) to 1-2s and minimizes undershoot by up to an order of magnitude. Comparative studies further confirm the proposed AMPC's superiority over Harmony Search (HS), Sine-Cosine Algorithm (SCA), Teaching-Learning-Based Optimization (TLBO)-optimized PID/PIDA controllers and the Marine Predator Algorithm (MPA)-based cascaded PIDA, establishing AMPC as an effective and scalable solution for low-inertia grids with high renewable penetration.

Automated insulin delivery (AID) systems depend on their actuators’ behavior since saturation limits, rate constraints, and hardware degradation directly affect the stability and safety of glycemic regulation. In this paper, we conducted an actuator-centric evaluation of five control strategies: Nonlinear Model Predictive Control (NMPC), Linear MPC (LMPC), Adaptive MPC (AMPC), Proportional-Integral-Derivative (PID), and Linear Quadratic Regulator (LQR) in three physiologically realistic scenarios: the first combines exercise and sensor noise to test for stress robustness; the second tightens the actuation constraints to provoke saturation; and the third models partial degradation of an insulin actuator in order to quantify fault tolerance. We have simulated a full virtual cohort under the two-actuator configurations, DG3.2 and DG4.0, in an effort to investigate generation-to-generation consistency. The results detail differences in the way controllers distribute insulin and glucagon effort, manage rate limits, and handle saturation: NMPC shows persistently tighter control with fewer rate-limit violations in both DG3.2 and DG4.0, whereas the classical controllers are prone to sustained saturation episodes and delayed settling under hard disturbances. In response to actuator degradation, NMPC suffers smaller losses in insulin effort with limited TIR losses, whereas both PID and LQR show increased variability and overshoot. This comparative analysis yields fundamental insights into important trade-offs between robustness, efficiency, and hardware stress and demonstrates that actuator-aware control design is essential for next-generation AID systems. Such findings position MPC-based algorithms as leading candidates for future development of actuator-limited medical devices and deliver important actionable insights into actuator modeling, calibration, and controller tuning during clinical development.

ABSTRACT As the need to reduce use in the building sector increases, thermally activated building systems (TABS) have gained attention for providing both comfort and energy efficiency. Their large thermal mass enables peak load shifting, making them suitable for demand response (DR). Effective DR control requires methods that can flexibly handle dynamic building behavior, disturbances, and varying thermal characteristics. While model predictive control (MPC) is capable of predictive optimization, conventional MPC relies on fixed models and lacks adaptability to time‐varying system conditions. This study introduces an adaptive MPC (AMPC) method, which incorporates online estimation and sequential model updating, to realize a DR‐based control strategy for TABS. The method was evaluated through a co‐simulation framework using Dymola and MATLAB/Simulink. Results show that AMPC can perform effective precooling and stably respond to DR requests. Through multiple case studies, the method was found to leverage the thermal storage capacity of TABS to flexibly shift cooling loads. Under the examined conditions, approximately 90%–100% of peak cooling energy was shifted to off‐peak periods, while ceiling surface temperature errors were maintained within about 0.3°C. Furthermore, PMV remained within ±0.5 in all cases, demonstrating that thermal comfort can be preserved even under restricted cooling operation.

Adaptive model predictive control with extended state observer for unmanned aerial vehicle trajectory tracking

A resilient stochastic model predictive control (MPC) method based on an adaptive input reconstruction mechanism is proposed for networked stochastic systems under false data injection (FDI) attacks. To the best of our knowledge, this is the first stochastic MPC framework designed to address FDI attacks; it not only mitigates the conservatism of existing methods but also reduces system resource consumption. Particularly, an adaptive input reconstruction mechanism is introduced to relax the assumptions on FDI attack energy in existing resilient MPC methods by reconstructing feasible control inputs. In addition, the adaptive prediction horizon and terminal constraint are co-designed to reduce the computational complexity. Furthermore, the conservatism inherent in existing resilient MPC methods due to hard constraints is alleviated by transforming fixed hard constraints into stochastic constraints. Based on these designs, sufficient conditions are derived to guarantee the proposed method's recursive feasibility and the closed-loop system stability. Finally, the effectiveness of the proposed method is validated through simulations on a DC-DC converter system.

Robust Adaptive Control Barrier Function-Based Model Predictive Speed Control for PMSM Drives With Current Safety Guarantees

Adaptive boundary prescribed-performance MPC for 3-D UAV formation with obstacle/collision avoidance

A mechanism-data-driven hybrid model-based adaptive model predictive control method for alumina evaporation process

Multiple High-Speed Trains Cooperative Tracking Control Based on Distributed Adaptive Model Predictive Control

Neural-Network-Enhanced Adaptive MPC for Constant-Voltage Output in Dynamic Wireless Power Transfer Systems