Search Results (177)

Sustained uphill motion of two-wheeled self-balancing wheelchairs is challenging. Slope-induced gravity changes the equilibrium condition, driving-torque demand, and velocity response. This paper proposes a smoothly weighted hybrid control method. The method combines nonlinear model predictive control and a linear quadratic regulator for pitch stabilization and uphill velocity tracking. The control design accounts for the slope-dependent equilibrium condition and steady-state torque demand. NMPC handles large-deviation recovery, velocity regulation, and actuator constraints. LQR improves local stabilization near the equilibrium point. The two controller outputs are coordinated by a continuously varying weight, which provides a smooth transfer of control authority across the transition region. MATLAB/Simulink simulations compare the proposed method with standalone NMPC, LQR, and SMC under several slope angles. Disturbance-recovery tests are also conducted under external torque disturbances. The results show stable uphill motion under the tested slope conditions. After finite-duration disturbances, the controller recovers both pitch posture and uphill velocity. Under a sustained torque disturbance, pitch stability is retained, but velocity regulation degrades. The proposed method improves pitch stabilization and velocity maintenance under the tested conditions. The recorded wheel-end torque remains bounded without sustained saturation in the three hybrid-controller cases. These results demonstrate numerical feasibility under the specified nominal simulation conditions, while uncertainty-robust and real-time performance remain to be validated. Full article

Commercial truck path tracking is strongly affected by large mass and yaw moment of inertia, steering-rate constraints, and signal delays, whereas optimization-based predictive controllers can impose high online-computational costs. This study proposes a real-time path-tracking method based on Constraint-Handling Trajectory Prediction (CHTP). A dynamic model predicts the vehicle’s future pose, and a Stanley-based tracking law computes the desired steering angle from the predicted state. A constraint-handling module then explicitly limits the steering angle and its rate of change. The proposed CHTP method requires no online optimization. The method was evaluated through MATLAB/Simulink-TruckSim co-simulations and hardware-in-the-loop (HIL) tests. Under the low-speed unladen condition, CHTP reduced the maximum absolute-displacement error by 74.60% compared with the conventional Stanley controller. Under the high-speed unladen, low-speed heavy-load, and high-speed heavy-load conditions, CHTP completed the lane-change maneuver with bounded tracking errors, whereas the conventional Stanley controller failed to maintain convergent tracking. Across the four basic path-tracking conditions, the maximum absolute displacement and heading errors of CHTP did not exceed 0.3930 m and 0.1133 rad, respectively. Although nonlinear model predictive control (NMPC) generally achieved higher tracking accuracy, CHTP reduced the mean solution time by 94.17–95.96% relative to NMPC, with a maximum solution time of 1.1416 ms. Additional robustness tests showed that CHTP maintained bounded tracking errors and stable lateral dynamic responses under positioning errors, low road adhesion, and random response delays, while preserving its real-time computational performance. In the HIL test with a total loop delay of approximately 0.22 s, extending the prediction time from 0.20 s to 0.42 s limited the maximum displacement and heading errors to 0.2290 m and 0.1046 rad, respectively, with a maximum solution time of only 0.9895 ms. Additional prediction-time tests showed a trend consistent with the simulation results, further supporting the proposed delay-compensation mechanism. These results demonstrate that CHTP provides a favorable balance among tracking accuracy, robustness, and real-time performance. Full article

Ultra-wideband (UWB) positioning provides cost-effective localization for quadrotors in global navigation satellite system (GNSS)-denied environments, but geometry-dependent, heavy-tailed errors challenge state estimation and safety-critical control. This paper presents a risk-aware framework linking geometry-aware error quantification with belief-space collision avoidance under a fixed four-anchor UWB configuration. Specifically, horizontal dilution of precision (HDOP) and nearest-anchor distance are used as spatial features in a Student’s-t process regression (STPR) model to predict UWB positioning errors and quantify the associated uncertainty. The compensated UWB measurements are then fused with inertial data through a Kalman filter to obtain a Gaussian belief state. A belief control barrier function (BCBF) maps ellipsoidal collision regions to a unit sphere, approximates them using tangent half-spaces, and is embedded in nonlinear model predictive control (NMPC). In outdoor flight experiments, the positioning RMSE is reduced to 0.071 m by the proposed STPR-KF method, compared with 0.299 m for raw UWB and 0.292 m for conventional KF. Feasible risk-aware obstacle avoidance and adjustable safety clearance are further demonstrated through numerical simulations. The feasibility of linking geometry-aware UWB error characterization with belief-space safety constraints for UWB-localized quadrotor navigation is therefore indicated. Full article

Accurate prediction and energy-efficient control of pig-house environments remain challenging because temperature, relative humidity, NH_3, and CO₂ are nonlinear, strongly coupled, and affected by ventilation and seasonal conditions. This study proposes an intelligent prediction-control framework integrating hybrid deep learning prediction with nonlinear model predictive control for multivariable pig-house environmental regulation. Several hybrid deep learning models were compared, including CNN-LSTM-standard transformer, CNN-LSTM-lightweight transformer, and the proposed CNN-LSTM-linformer-style model. The CNN-LSTM-lightweight transformer achieved the highest overall prediction accuracy, whereas the proposed CNN-LSTM-linformer-style model provided the most compact structure by using separable convolution, global average pooling, and linformer-style attention, reducing training time and memory usage by approximately 53% compared with the CNN-LSTM-standard transformer. The prediction model was integrated with FLC, NMPC, and ANMPC for closed-loop ventilation control. ANMPC adjusts control weights online according to environmental deviations to balance environmental regulation and energy use under disturbances. In the nominal closed-loop simulation, NMPC and ANMPC reduced ventilation energy consumption by approximately 44% compared with FLC, while ANMPC achieved a 4.35% lower NH₃ steady-state error and a 3.5% faster NH₃ recovery response than NMPC under disturbance conditions. In 24-h pre-field verification, NMPC and ANMPC reduced energy consumption by 35.8% and 27.4%, respectively, while maintaining pollutant safety. Full article

Cabin heating significantly affects the driving range of electric vehicles (EVs) under cold weather conditions, making energy-efficient thermal management essential. Conventional control strategies typically regulate a fixed cabin temperature setpoint, which may lead to suboptimal energy consumption and does not directly account for passenger thermal comfort. This study proposes a real-time thermal comfort-oriented nonlinear model predictive control (NMPC) framework for EV heat pump systems. To enable real-time implementation, a control-oriented model is developed by combining a data-driven model for heat pump performance with a linearized Predicted Mean Vote (PMV) model. The proposed NMPC optimizes the trade-off between passenger thermal comfort and compressor energy consumption while satisfying actuator constraints. A high-fidelity physics-based virtual plant is employed to evaluate the proposed strategy. Simulation results show that the proposed NMPC maintains thermal comfort within the recommended PMV range while reducing total energy consumption by 8.3% compared with a conventional rule-based controller. Furthermore, a parametric study involving 1210 gain-tuning cases of a rule-based controller and 100 NMPC weighting scenarios demonstrates a consistently superior comfort–energy trade-off. The average computation time remains below 0.15 s on the simulation platform, indicating that the proposed NMPC formulation can be solved within the selected sampling interval. These results highlight the potential of thermal comfort-oriented NMPC for improving both passenger comfort and energy efficiency in EV heat pump systems. Full article

Trajectory planning is a key enabling technology for UAV swarms operating in complex and obstacle-rich environments. This paper proposes a distributed nonlinear model predictive control (NMPC)-based trajectory planning method for UAV swarms, where terminal target reaching, prescribed formation maintenance, obstacle avoidance, and inter-UAV collision avoidance are incorporated into a unified predictive optimization framework. To reduce the online computational burden, a control parameterization strategy is introduced to describe the control input using M control segments, thereby reducing the dimension of the online decision variables. Furthermore, an exact-penalty-based constraint transcription method is developed to transform the original constrained optimal control problem into a lower-complexity finite-dimensional nonlinear programming problem, while efficiently handling velocity constraints, obstacle avoidance constraints, and inter-UAV collision avoidance constraints. Simulation results for a three-UAV swarm in a cluttered environment demonstrate that the proposed method can generate dynamically feasible and collision-free trajectories, while enabling the swarm to reach the assigned target positions and preserve the desired formation within a certain formation error. Furthermore, real UAV swarm flight experiments were conducted to further validate the practical feasibility and online applicability of the proposed distributed NMPC framework for UAV swarm trajectory planning. Full article

The application and performance of Nonlinear Model Predictive Control (NMPC) are critically limited by the computational efficiency of solving nonlinear combinatorial optimization problems. To address this challenge, this study proposes an efficient optimization strategy that employs a neural network to solve the constrained L-1 norm minimization problem within the NMPC framework, thereby enhancing motion control performance. Inspired by the flexible representational capacity and powerful optimization capabilities of neural networks, we explicitly encode the NMPC objective function into a network architecture. The optimal control solution is then obtained efficiently through network training. We validate the proposed strategy in a tractor path-tracking control task, detailing the processes of network construction and optimization. Benefiting from the inherent parallelism and computational efficiency of neural networks, the resulting controller demonstrates excellent real-time performance. Specifically, with prediction horizons set to 5, 10, and 20 steps, the solution times are reduced to less than 0.12, 0.28, and 0.84 s, respectively, under typical operating constraints. Full article

In light of the advancement of vehicle electrification and intelligence, four-wheel independent drive (4WID) electric vehicles (EVs) have garnered significant attention as a promising platform. Integrating advanced torque-vectoring (TV) strategies into 4WID EVs can effectively optimize the synergistic performance between handling stability and energy efficiency of the over-actuated system across various driving conditions. In this paper, a hierarchical Combined Sliding Mode Control–Adaptive Nonlinear Model Predictive Control (cSMC-ANMPC) TV strategy is proposed to enhance the comprehensive performance of 4WID EVs and ensure adaptive control across diverse driving conditions. Firstly, a hierarchical control architecture is developed to decouple the complex multi-objective problem. The upper layer performs robust stability decision-making by observing the vehicle’s state errors. The lower layer determines the optimal torque distribution throughout the powertrain. Secondly, a Combined Sliding Mode Controller (cSMC) is developed for the upper layer to promptly generate a robust stability command. By co-regulating both yaw rate and sideslip angle into a single command, it simplifies the lower layer’s task and enhances overall stability. Thirdly, a Soft Actor-Critic (SAC) intelligent tuner is integrated into the lower-layer NMPC to mitigate the effects of varying conditions on the stability–economy trade-off and strengthen the adaptability of the controller. Finally, co-simulation evaluations on the MATLAB R2023b/CarSim 2020.0platform demonstrate that the proposed cSMC-ANMPC strategy can improve comprehensive performance for the studied 4WID EV. Compared with other baselines, the stability enhancement in extreme maneuvers and the long-term energy-saving capability are remarkable, showcasing its promising performance. Full article

This paper presents a battery-aware control framework for a single-phase integrated battery charger (IBC) for electric vehicles, in which the traction system is reused as part of the charging hardware. The proposed charger consists of a stator-assisted bridgeless totem-pole power-factor-correction AC–DC stage and a bidirectional buck–boost DC–DC stage connected to a 48 kWh, 400 V lithium-ion battery pack. The battery pack is modeled using a lookup-table-based equivalent circuit model with state-of-charge- and temperature-dependent open-circuit voltage and impedance parameters. A conventional double-loop PI controller is used as the baseline, while the proposed strategy combines nonlinear model predictive control, an extended Kalman filter, and lookup-table-based battery parameterization to regulate charging current under electrical and thermal constraints. The system is evaluated under 7 kW, 230 V/32 A and 22 kW, 230 V/96 A charging cases using average-model simulations, switching-model transient simulations, and finite element thermal assessment of the induction motor stator. The average-model results show stable charging from 20% to 80% SOC, with charging times of approximately 275 min at 7 kW and 90 min at 22 kW. The EKF provides bounded battery state estimation, with maximum SOC estimation errors of approximately 1.3% and 2.0% for the 7 kW and 22 kW cases, respectively, while the core-temperature estimation error converges close to zero. The switching-model results confirm feasible duty-command behavior, bounded battery-current tracking error, and a representative DC-link ripple of approximately 8 Vpp. During grid-voltage reduction, the charging current is reduced to keep the grid-current envelope within the intended limit. FEM results show that charging-only motor temperatures remain low, reaching approximately 27.39 °C at 7 kW and 38.82–38.85 °C at 22 kW. The most critical charging-related thermal case occurs at 22 kW after one hour of full-load motor operation with a 40 °C initial condition, reaching approximately 92.32 °C. Overall, these simulation-based findings support the feasibility of the proposed NMPC–EKF–LUT framework as a battery-aware supervisory control strategy for single-phase IBC operation. The proposed controller improves constraint-aware, battery state-based decision-making, while switching ripple and motor thermal response are mainly governed by the power stage, feasible current trajectory, and initial thermal condition. Full article

Various methodologies have been developed over the years to assess model predictive control (MPC) performance. However, few have been applied in industry, and they remain limited in terms of providing a rapid indication of the root causes of deteriorated control performance. This article aims to review the existing methodologies in the literature that address these challenges. Additionally, it implements a structure in which MPC performance assessment is integrated within a fault detection and diagnosis (FDD) framework. The integrated approach employs cascaded modules of machine learning (ML) binary classifiers arranged in a sequence that mimics the decision-making logic of an operator. To illustrate the integrated strategy both conceptually and operationally, a van de Vusse reactor, controlled by a nonlinear model predictive controller (NMPC), is used as a case study. The ML models evaluated include XGBoost, Random Forest, Multilayer Perceptron, Long Short-Term Memory, and Gated Recurrent Unit. The results show that these models can correctly distinguish the cause of abnormalities, even in the presence of measurement noise, with a detection accuracy of 99% and an abnormality classification accuracy above 85% for the best-performing models. Different ML models performed best for distinct diagnostic tasks, highlighting the flexibility of arranging models according to their most suitable application. The investigation indicates that the proposed ML-based FDD framework, which embeds control performance assessment, is competitive for control-aware diagnosis of MPC-controlled processes. Full article

This study proposes a reduced-complexity nonlinear model predictive control (NMPC) framework for high-performance path tracking of a four-wheel-drive (4WD) autonomous vehicle. A 4WD sports car equipped with four independent wheel motors is used as the test vehicle. Although the vehicle has four motors, the proposed NMPC directly optimizes the front-wheel steering command and the rear-left and rear-right wheel torque commands, while the front-wheel torques are generated using a gain-based virtual 4WD distribution law. Trajectory optimization (TRO) is performed offline to generate the reference racing line and velocity profile, while the online NMPC controller tracks the optimized reference trajectory using the front-wheel steering command and the rear-left and rear-right wheel torque commands as control inputs. This structure reduces the control complexity while maintaining the ability to improve traction utilization and yaw response. Under the investigated simulation conditions on the Shanghai International Circuit, the proposed reduced-dimensional NMPC with rear-dominant virtual 4WD torque distribution reduces the simulated lap time while maintaining bounded path-tracking errors and satisfying the track-boundary constraints. As the torque distribution gain $K_r$ increases from 0 to 0.5, the lap time is reduced by approximately 10.3% (from 182.08 s to 163.30 s), while the maximum lateral tracking error remains below 0.33 m and the maximum heading-angle error remains below 2.95 deg for all stable cases. However, further increasing $K_r$ beyond 0.5 leads to degraded tracking performance or loss of stable path following because excessive front-wheel longitudinal force reduces the available lateral tire force for steering. These results indicate that an appropriate torque distribution gain can improve corner-exit acceleration and overall lap-time performance, whereas excessive front torque assistance may degrade tracking accuracy and vehicle stability. Full article

This paper presents a dual-layered computational framework for the robust trajectory planning and active stabilization of a robotic manipulator transporting a non-fixed payload. The primary challenge addresses the transport of a tray containing multiple objects prone to sliding, exacerbated by significant uncertainties in the system’s dynamic parameters, such as objects’ mass and inertia. The first contribution is an optimal closed-loop sensitivity-based trajectory planning algorithm that generates energy-efficient paths while minimizing the possibility of object sliding. The second contribution is an active sliding control strategy based on Nonlinear Model Predictive Control (NMPC). This algorithm dynamically adjusts the orientation of the tray, mounted to the robot’s end effector, usefully exploiting a dynamic model including inertial forces and gravity to move the object to given positions. Simulation and experimental results demonstrate that the integrated approach allows the robot to set the objects in desired positions with an average steady-state error of $6.3\times {10}^{-3}$ m across a set of ten experiments, while limiting the sliding to less than 3% of the tray dimensions during successive transportation in face of a 10% uncertainty about objects’ masses. The synergy between the uncertainty-aware planner and the NMPC controller supports robust tray-transport tasks in unstructured environments where precise dynamic modeling is unavailable. Full article

A methodology for the development of nonlinear model predictive control for thermal management of a 40-ton fuel-cell heavy-duty truck is presented, using the medium-temperature cooling circuit as a case study. The approach integrates control-oriented modeling, parameter estimation, and experimental validation based on drivetrain test bench measurements under controlled high-temperature ambient conditions. A lumped-parameter model of the medium-temperature circuit, including coolant, oil, electric motors, and power-electronics auxiliaries, is derived and implemented in a Simulink environment, with heat-transfer parameters calibrated from test bench data and radiator air-side resistance and fan characteristics derived from CFD simulations and manufacturer specifications, respectively. Model parameters are identified using a systematic estimation procedure and the resulting model is validated against long-duration roller test measurements, achieving coefficients of determination above R² = 0.9 and normalized RMSE values below 10% for all key temperatures. The validated model is then used as the prediction model in a model predictive controller that manipulates radiator fan and coolant-pump speeds, while treating component heat losses, vehicle speed and ambient temperature as measured disturbances. The controller is evaluated in a model-in-the-loop environment for representative long-haul and urban driving cycles and different ambient temperatures, and its performance is benchmarked against conventional rule-based and PI-based control strategies. Depending on the driving cycle and ambient conditions, the proposed NMPC reduces cooling system energy consumption by up to 39.6% compared to a PI controller (VECTO Urban Delivery cycle, 35 °C ambient), with an average reduction of 16.6% across all investigated driving cycles and ambient conditions, without a significant increase in average or maximum coolant temperature. The proposed methodology provides a transferable workflow for developing predictive thermal management control in fuel-cell heavy-duty vehicles and other complex automotive cooling systems. Full article

Tethered unmanned surface vehicle (USV) and remotely operated vehicle (ROV) systems are widely used in deep-sea inspection, observation, and intervention tasks. During cooperative operations, the USV must follow the mission trajectory of the ROV while avoiding surface obstacles and maintaining a prescribed tether-related safety envelope. This study proposes a multi-objective nonlinear model predictive control (NMPC) framework for USV-side cooperative tracking and dynamic obstacle avoidance in tethered USV-ROV operations. The framework integrates the predicted three-dimensional ROV trajectory, USV nonholonomic motion, surface-obstacle avoidance, straight-line tether-length-related geometric constraints, and control-smoothness regulation into a unified receding-horizon optimization problem. Sequential Least Squares Programming is used to compute the online control sequence. Numerical simulations include obstacle-free tracking under bounded ocean-current disturbances and heterogeneous surface–underwater obstacle scenarios. The results show that the proposed controller provides improved tracking performance and maintains the straight-line tether-length proxy below the prescribed limit in the tested simulations. The current-disturbance results further indicate preliminary disturbance-rejection capability under bounded time-varying ocean currents. The study provides a controller-level numerical framework for cooperative tracking and surface obstacle avoidance in tethered USV-ROV operations. Full article

Liquid storage tanks are widely used in sectors such as water treatment, oil and gas, food processing, and chemical manufacturing. Knowing the exact amount of liquid in a tank is essential for ensuring safety, preventing spills, and optimizing process control; therefore, the liquid level in a tank must be maintained at a precise reference point. This is where liquid level control for tanks becomes crucial and constitutes a fundamental problem in the industrial sector due to nonlinearities, multivariable coupling, and stochastic disturbances. Given the drawbacks of available control methods, such as classical Model Predictive Control (MPC), which are highly dependent on model accuracy and struggle to reject complex stochastic noise, predicting random disturbances represents a major technological challenge. A new approach is proposed to specifically address the problem and challenge of the four-tank system, where water levels in two lower tanks must be controlled by two pumps, often with varying delays and significant parameter disturbances. To establish a relationship between expected performance and MPC parameters, this approach uses a novel hybrid nonlinear MPC, Extended State Observer, and Physics-Informed Neural State Estimation (NMPC-ESO-PINSE) architecture. A Physics-Informed Neural State Estimation (PINSE) layer, chosen for its learning capacity, is designed to filter sensor noise by applying Bernoulli’s physical laws, while an Extended State Observer (ESO) is integrated to capture and compensate for unmodeled uncertainties in the process. Finally, a proposed hybrid (NMPC-ESO-PINSE) strategy leverages these clean, physically consistent state estimations to solve a non-convex optimization problem via Sequential Quadratic Programming (SQP), computing optimal pump voltages. Extensive numerical simulations demonstrate the superior resilience of this decoupled framework against parametric drifts and continuous noise sequences, yielding a $+27.36\%$ reduction in global Root Mean Square Error (RMSE) compared to standard NMPC, accelerating the closed-loop settling time to $15.2 \mathrm{s}$, and restricting transient overshoot to just $0.18\%$. Full article