Search Results (142)

High-fidelity motion cueing in driving simulators is essential for delivering a realistic and immersive user experience. However, the trade-off between motion accuracy and computational efficiency often hinders achieving this. Fixed-horizon Model Predictive Control (MPC)-based Motion Cueing Algorithm (MCA) frameworks frequently struggle to adapt to rapid dynamic changes in vehicle behaviour, resulting in suboptimal simulator responses. Their reliance on worst-case horizon tuning can result in inefficient platform usage and increased computational load, limiting computational efficiency and practical deployment. This study presents an adaptive MPC-based MCA designed to enhance the fidelity of motion platforms used in vehicle dynamic simulations. The proposed method dynamically adjusts the MPC prediction horizon to improve overall simulation performance while minimising motion sensation error. Within the simulation environment, the prediction horizon is adaptively updated at each simulated control step according to recent tracking-performance metrics, enabling responsiveness to varying vehicle dynamic models and driving scenarios. The system was developed and implemented using Python and MATLAB environments, with Long Short-Term Memory (LSTM) networks employed to enhance the adaptability and precision of prediction horizon adjustments. Due to safety constraints, the proposed framework was evaluated exclusively within a simulation environment and compared against both classical MPC-based MCA and RL MPC-based MCA. Experimental results demonstrate that the proposed adaptive framework improves workspace utilisation and substantially reduces computational load compared with the classical and RL-based MPC-based MCA approaches, while maintaining competitive motion cueing tracking performance. The adaptive system effectively enhances linear displacement (LD), ensuring better alignment of motion cues with platform constraints. While minor trade-offs were observed in root mean square error (RMSE) and correlation coefficients (CCs) for sensed angular velocity (SAV) and sensed specific force (SSF), the framework improves workspace utilisation and computational efficiency while maintaining competitive motion cueing performance. Furthermore, the adaptive LSTM-MPC framework substantially reduces computational load, achieving approximately 44.26 times faster execution compared with the classical MPC-based MCA and approximately 30.03 times faster execution compared with the RL MPC-based MCA. These findings highlight the potential of integrating deep learning (DL) with MPC to optimise the trade-off between motion cueing performance, platform utilisation, and computational efficiency in driving simulators. Full article

Autonomous navigation of a vehicle in an environment where there are obstacles is difficult due to low onboard sensing technology, high measuring noise, and external interference, which collectively result in poor tracking performance of the vehicle’s trajectory and compromise safety. In this paper, a UAV-assisted Distributed Sliding Mode Control (DSMC) is proposed to robustly and safely implement path tracking for autonomous ground vehicles (AGVs). The proposed system utilizes an aero-sensor layer for enhanced perception, such as obstacle sensing, reference path preview, and look-ahead trajectory information, and it shares this information with the vehicle via wireless communication. The fundamental scheme, called DSMC, is based on a conventional Sliding Mode Control (SMC) technique and uses UAV preview-based feedback. This allows anticipation of control actions to enhance tracking performance and achieve more timely, smoother obstacle avoidance than baseline SMC. The proposed method is designed to overcome the limitations of traditional SMC strategies, such as chattering and poor responsiveness. The proposed method features continuous nonlinear approximation and damping mechanisms to reduce chattering and improve response characteristics, thereby enhancing stability and reducing oscillations. Strict safety enforcement through constraint is always achieved by keeping the vehicle and obstacles separated by a minimum distance only; that is, a minimum distance is always guaranteed: a Constraint Barrier Function (CBF)-based constraint is used. By combining UAV-assisted perception with DSMC and CBF the system can guarantee its formal safety in the presence of disturbances and sensing uncertainties while maintaining accurate trajectory tracking. Based on our simulation results, the proposed UAV-assisted DSMC method is shown to be significantly superior to conventional SMC and Model Predictive Controller (MPC) in terms of tracking accuracy, control smoothness, and adherence to the safety margin. Our simulation results demonstrate that the proposed method significantly outperforms conventional SMC and MPC control. Specifically, it achieves a 22.9% reduction in RMSE (0.135 m vs. 0.175 m) and 63% lower mean control effort, and it strictly maintains the minimum safety distance under both static and dynamic obstacles. The algorithm runs in real-time with an average execution time of 1.85 ms (>200 Hz), making it highly suitable for embedded deployment. These results highlight the effectiveness of combining UAV-assisted preview, adaptive robust control, and formal safety constraints for reliable autonomous navigation in complex environments. Full article

Background/Objective: Autonomous vehicles (AVs) require control algorithms capable of handling complex and dynamic environments while satisfying multiple conflicting objectives such as safety, comfort, energy efficiency, and trajectory accuracy. Model predictive control (MPC) offers a principled framework for multi-constraint optimization, yet its real-time feasibility remains challenging for nonlinear vehicle dynamics. Methods: This paper presents a feedforward neural network (FNN)-based MPC framework for autonomous vehicle trajectory tracking. The FNN approximates the coupled vehicle dynamics and visual preview error model using an algebraic sum of log-sigmoid functions. Three adaptive FNN parameter sets, namely, the scaling factor, convergence parameter, and time-shifting parameter, are jointly optimized using a hybrid algorithm that combines the global search capability of fractional particle swarm optimization (FPSO) with the local refinement of sequential quadratic programming (SQP). Results: Comprehensive scenario-based simulations are performed to evaluate trajectory tracking dynamics under dry conditions with an adhesion coefficient of 0.8 and a vehicle mass of 1723 kg moving at a speed of 80 km/h. The results are quantitatively compared with a traditional PID controller and a structurally comparable MPC framework from the literature under identical simulation conditions; related DRL- and RL-based methods are discussed qualitatively for contextual orientation only. The stability, reliability, and computational complexity of the proposed framework are examined based on the mean square error, fitness value, and computational budget in GFLOPs for 100 independent runs. Conclusions: The proposed FNN-based MPC framework demonstrates improved tracking accuracy and optimizer reliability in simulation. While the present results indicate promising computational behavior, real-time deployment will require further validation on embedded automotive hardware and under closed-loop real-time constraints. Full article

This paper presents a primary side model predictive control (MPC) strategy for wireless power transfer (WPT) based charging of light electric vehicle (LEVs). A battery simulator develops a model to accurately reproduce constant-current (CC) charging profile from Open Ciruit Voltage (OCV) and State of Charge (SoC) parameters of the battery. This model forms the foundation of the predictive control design, allowing accurate prediction of the charging trajectory while avoiding reliance on secondary-side feedback signals. The WPT system employs a phase-shifted full-bridge (PSFB) inverter with S-S compensation, where the primary-side controller regulates the secondary-side charging current using only primary-side current measurements. In contrast to conventional secondary side control, which is tuned around nominal coupling, requires explicit feedback, and degrades under coil misalignment and parameter variations, the proposed MPC leverages integrated system and battery models to predict future states and optimally adjust the phase shift for robust charging operation. Simulation and experimental validation on a real-time LEV charging prototype under aligned, lateral, and angular misalignment conditions demonstrate significant reduction in current-settling time compared to fixed-gain proportional-integral (PI) and known adaptive feedback controllers for same system, with lower RMS current and reduced current spikes at the battery. On the embedded controller, the proposed MPC executes within approximately 1 µs per 85 kHz PWM cycle, corresponding to less than 10% CPU utilization, confirming its practical real-time feasibility. Full article

Conventional model predictive control (MPC) is highly vulnerable to motor parameter variations. Meanwhile, existing parameter-based MPC schemes are often constrained by the accuracy of model reconstruction. To overcome these limitations, this article proposes a model-free predictive control (MFPC) strategy based on a fuzzy approximation method for a permanent magnet synchronous motor (PMSM). Leveraging the exceptional nonlinear mapping capability of fuzzy approximation, the proposed strategy approximates the autoregressive term within a structurally simple first-order autoregressive model with exogenous input (ARX). This significantly enhances model reconstruction accuracy. Furthermore, discrete-time Lyapunov stability analysis rigorously demonstrates that the estimation errors of the internal states under the proposed control scheme are uniformly ultimately bounded (UUB). Finally, experimental results reveal that the proposed MFPC strategy achieves superior steady-state current quality while ensuring excellent dynamic performance, effectively validating the advantages of the proposed method. Full article

The Sparse Identification of Nonlinear Dynamics (SINDy) method yields compact and interpretable models that preserve physical system properties, offering a superior alternative to black-box models. This work proposes a computationally efficient Model Predictive Control (MPC) algorithm for SINDy models. The algorithm employs a successively obtained online linear Taylor approximation of the model for future prediction, while the full SINDy model captures past dynamics. As a result, the nonlinear MPC problem is reformulated as a tractable quadratic program. The implementation covers three discretization schemes: the first-order Euler and the simplified and full fourth-order Runge–Kutta. Simulation benchmarks for population dynamics and aircraft models show that the algorithm achieves performance comparable to nonlinear MPC with significantly lower complexity, enabling real-time use. Full article

Dynamic wireless charging (DWC) of electric vehicles (EVs) offers a promising solution to mitigate range anxiety and enhance the feasibility of electrified transportation; however, achieving optimal power transfer requires precise alignment between the primary coil embedded in the roadway and the secondary coil mounted on the vehicle. In practice, lateral misalignment (LTM) frequently occurs, leading to reduced efficiency. Although conventional controllers can partially compensate for these losses, their performance degrades under significant misalignment, resulting in overshoot and steady-state error (SSE). To overcome these limitations, this paper proposes a model predictive control (MPC)-based approach to mitigate the effects of LTM and restore efficient power transfer. A comparative study between the proposed MPC and a conventional proportional–integral (PI) controller is conducted to assess performance and suitability. The MPC utilizes an optimization framework to determine optimal control actions over a prediction horizon, thereby minimizing SSE and reducing overshoot under varying misalignment conditions. The effectiveness of the proposed method is validated through MATLAB/Simulink simulations and experimental testing. The results demonstrate that the MPC maintains stable operation over a wide LTM range, achieving a maximum power transfer efficiency of 93% at zero misalignment, which decreases to 83% at severe misalignment (LTM = 0.5). Compared to the PI controller, the MPC improves average efficiency by approximately 8–12%, leading to improved robustness and smoother dynamic response. These results confirm the effectiveness of the proposed MPC approach in maintaining high efficiency and stable operation in misaligned DWC systems. Full article

This study presents a novel hierarchical hybrid control architecture for the attitude stabilization of a 3-Degree-of-Freedom (3-DOF) hover system. Operating on a linearized state-space model, a Linear Quadratic Regulator (LQR) is deployed as the optimal inner-loop core to guarantee baseline multi-variable stability. To dramatically improve transient performance and suppress high-frequency oscillations, Sliding Mode Control (SMC) and Super-Twisting Sliding Mode Control (STSMC) are incorporated not as conventional additive inputs, but as dynamic reference-reshaping supervisory mechanisms in the outer loop. This structural decoupling preserves the optimal characteristics of the LQR while effectively attenuating chattering, thereby preventing physical actuator fatigue. Furthermore, the Big Bang–Big Crunch (BB-BC) metaheuristic algorithm is employed to systematically optimize the design parameters of the supervisory layers, enabling effective steady-state error reduction with a remarkably low computational cost. Comparative evaluations demonstrate that the proposed LQR-STSMC framework significantly accelerates system responsiveness, reducing rise times by approximately 80% to 90% and consistently lowering settling times across all operational axes while achieving a reduction of up to two orders of magnitude in overall tracking errors (ITAE) relative to the baseline LQR. Although evaluations involving Model Predictive Control (MPC) demonstrate improvements in transient response and a reduction in total error compared to the standard LQR, the proposed LQR-STSMC architecture exhibits significantly better overall performance and superior disturbance rejection capabilities. Simulation results under continuous aerodynamic perturbations (wind disturbances) confirm that the proposed hierarchical methodology effectively eliminates steady-state offsets, fundamentally outperforming both classical LQR and MPC in terms of robustness, precision, and ultra-fast transient performance. Full article

Autonomous UAV landing on dynamic unmanned surface vessel platforms is affected by deck motion and degraded visual observations, which may lead to unsafe final descent decisions. This paper proposes a fully decentralized reliability-enhanced predictive landing method that combines probabilistic perception, visual quality assessment, and model predictive control. Target posterior probability, perception uncertainty, and task-oriented image quality are fused into an online observation reliability index, which is used to adapt observation noise, constrain phase switching, and penalize unreliable descent opportunities. FFT-based dominant-mode identification and Kalman correction are also used to predict deck roll and pitch for landing-window selection. Simulation results show that the proposed method achieves a 90% small-angle landing success rate and keeps the touchdown attitude angle within 5°. Compared with standard MPC, landings within a 15° deck inclination increase from 24% to 82%, and the 80th-percentile touchdown inclination decreases by 9°. Compared with SHMPC, the average solution time decreases from 913 ms to approximately 104 ms per iteration. These results indicate that the proposed reliability-aware framework can reduce unsafe descent decisions and improve landing robustness while maintaining real-time feasibility under degraded maritime visual conditions. Full article

To address the conflict between real-time performance and global optimality in the energy management of dual-motor plug-in hybrid electric vehicles (PHEVs), this paper proposes a model predictive control (MPC) strategy based on dynamic programming (DP). Firstly, a radial basis function (RBF) neural network is employed to predict future driving conditions, providing preview information for the MPC. Subsequently, a DP-MPC cooperative architecture is constructed, which invokes DP to solve for local optimal solutions during the receding horizon optimization process and incorporates linear reference SOC trajectory planning to approximate the global optimum. Simulation results under the WLTC driving cycle demonstrate that the fuel consumption of the proposed strategy is 2.311 L/100 km, representing a 33.2% reduction in pure fuel consumption compared to the rule-based (RB) strategy, and a 16.3% reduction in equivalent fuel consumption (including electricity converted to fuel based on the engine’s generation efficiency), while achieving 96.31% of the fuel economy of the global optimal DP strategy. The study validates that this method significantly improves fuel economy while guaranteeing real-time performance. Full article

Buildings account for nearly half of global energy consumption, with HVAC systems contributing approximately 40%. Fan coil units (FCUs) and fresh-air systems are widely adopted in commercial buildings for their flexibility. However, this system faces numerous critical challenges in tropical maritime climates, including low temperature control accuracy, high energy consumption, and inadequate coordination between thermal comfort and indoor air quality. This study aimed to optimize the indoor thermal environment and reduce HVAC energy consumption. It compared and analyzed the operational performance of traditional PID control and MPC. Additionally, dynamic CO₂ concentration modeling was performed to evaluate the impact of different outdoor air strategies on indoor air quality. A building simulation model was developed in TRNSYS 18. Based on the simulation data, a multi-objective model predictive control (MPC) model was created in MATLAB/Simulink. Results indicate that MPC significantly outperforms PID control in both temperature stability and energy efficiency across all outdoor air strategies, with the occupancy-based demand-controlled outdoor air strategy achieving the greatest energy savings (16.89%) while maintaining favorable indoor air quality. This study provides a theoretical foundation and practical control guidelines for the coordinated optimization of fan coil units and outdoor air systems in tropical maritime climates, facilitating the development of energy-efficient and comfortable HVAC solutions for commercial buildings. Full article

This paper establishes a theoretical framework for treating communication quality as a navigable resource in long-endurance unmanned aerial vehicle (UAV) control under stochastic degradation. We prove that a hierarchical architecture integrating communication-aware model predictive control (MPC) achieves $\epsilon$-optimality with respect to the intractable stochastic dynamic programming formulation while maintaining exponential stability guarantees under switched system dynamics governed by continuous-time Markov chains. Three primary theoretical contributions were made: (1) A stochastic optimality theorem is given showing that sigmoid penalty function approximation yields bounded suboptimality of $\eta \le 0.12$ under mild ergodicity conditions; (2) a formal stability result for mode switching based on hysteresis was established using multiple Lyapunov functions, and it showed exponentially fast convergence with a decay rate of $\lambda \ge 0.23$; and (3) bifurcation analysis showed that there is a critical time threshold of 72 h at which thermal-induced gyro-drift in the GPS sensor causes a transition in navigation error dynamics from linear to catastrophic nonlinear growth. The validation through 2430 Monte Carlo missions over 54,686 flight hours resulted in an average increase in endurance by 243% (18.2 days versus 5.3 days), while keeping CEP at approximately 8.7 m and achieving 82% mission success under extreme communication degradation ($q_{comm}<0.3$). The statistical results confirm a very strong positive relationship between the Resilience Quotient (RQ) and the length of successful missions ($R^2=0.89$, $p<0.001$), supporting the theoretical model with empirical evidence. Full article

Aiming at the characteristics of multivariable coupling, pronounced nonlinearity, time-varying behavior, and time delay effects in clean coal ash content during dense medium cyclone separation, which make it difficult for traditional control methods to achieve high-precision and stable regulation, this paper proposes an intelligent control method based on the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm. First, a data-driven environment model based on Long Short-Term Memory (LSTM) is constructed using historical operational data from a coal preparation plant to enable offline training of the reinforcement learning policy. Second, the state space, action space, and multi-objective reward function are designed for the ash content control task. On this basis, the standard TD3 algorithm is improved by introducing a hierarchical experience replay mechanism to enhance the utilization efficiency of critical samples, and a gated feature attention enhancement network to strengthen state representation under complex operating conditions. Experimental results demonstrate that the proposed method achieves the best overall performance among the compared approaches, with a mean absolute error (MAE) of 0.1190 and a root mean square error (RMSE) of 0.1938. The compliance rates within the target intervals of ±0.2 and ±0.5 reach 83.29% and 97.14%, respectively. Compared with Model Predictive Control (MPC), the proposed method improves the compliance rate under strict constraints by approximately 9.58 percentage points, indicating superior fine control capability. In addition, the proposed method outperforms the benchmarks in terms of error distribution, fluctuation suppression, and steady-state maintenance. These results verify that the improved TD3 method can effectively enhance the accuracy and stability of clean coal ash content control, providing a feasible solution for intelligent optimization control of quality indicators in complex industrial processes. Full article

This study investigates the safety limitations of autonomous vehicles operating under dense fog conditions, where sensor performance is severely degraded, and explores the potential of cooperative control for collision avoidance. A comparative framework is developed using the CARLA simulator to analyze four driving configurations: no perception and no communication, degraded LiDAR–radar sensing, V2V-assisted Model Predictive Control (MPC), and V2V-assisted MPC enhanced with predictive buffering. The methodology integrates fog-dependent perception modeling, cooperative hazard messaging, and real-time MPC-based longitudinal control, and evaluates system performance through multiple simulation trials under urban and highway conditions. Key performance indicators include time-to-collision, reaction time, maximum deceleration, jerk, and collision occurrence. The results demonstrate that perception-only strategies lead to late reactions and unsafe emergency braking, with minimum TTC values as low as 0.29 s and frequent collision events. In contrast, V2V-assisted MPC significantly improves anticipation and driving comfort, while the proposed predictive buffering approach achieves a 0% collision rate and increases the minimum TTC to approximately 1.93 s. The inclusion of predictive buffering further enhances robustness against communication losses, enabling smoother deceleration and consistently safe inter-vehicle spacing. Overall, the findings confirm that cooperative V2V communication combined with predictive control effectively compensates for fog-induced perception degradation and represents a viable solution for improving safety and reliability in low-visibility autonomous driving scenarios. Full article

Unsignalized intersections pose a representative challenge for autonomous-driving decision-making because online planning must satisfy tightly coupled requirements for safety, task completion, traffic efficiency, and control smoothness under a limited computation budget. Existing continuous-action MCTS planners often suffer from sparse candidate-action coverage and from the absence of an internal safety filter before node expansion. To address these issues, this paper proposes CP-LDS-MCTS, a decision-making framework that coordinates Sobol low-discrepancy sampling, truncated Taylor control barrier function (TTCBF)-based safety pruning, and policy-value composite scoring within the expansion stage of Monte Carlo tree search. Sobol sampling improves candidate representativeness under a fixed sampling budget; TTCBF provides a local one-step screening rule that removes actions inconsistent with safety constraints before search resources are consumed; and composite scoring prioritizes safe actions that are simultaneously policy-consistent and value-promising. To clarify the methodological contribution, CP-LDS-MCTS is formulated as a unified expansion-stage design rather than a loose combination of independent modules. The revised manuscript further adds a local approximation-error discussion for the TTCBF truncation, a computational-complexity analysis, a real-time latency evaluation, statistical significance tests, and two stronger baselines, namely PPO and MPC-CBF. Experiments in CARLA Town03 under low-, medium-, and high-density traffic show that the proposed method achieves the best overall balance among safety, success rate, travel time, and control smoothness while maintaining a mean planning latency below 25 ms per step on the test platform. The resulting safety assurance is local rather than global, as TTCBF pruning performs a one-step approximation-based feasibility check within the expansion stage and is validated in simulation. These results suggest that candidate coverage, internal safety screening, and value-aware expansion should be designed jointly for real-time continuous-action planning at unsignalized intersections. Full article