Search Results (142)

Achieving dynamic stability in bipedal locomotion against sustained external disturbances remains a significant challenge in humanoid robotics. Traditional methods, such as zero moment point (ZMP) preview control, often lack the reactive compliance and predictive planning necessary for robust performance on uneven terrain or under continuous force. This paper proposes a novel control framework that synergistically integrates a resistance torso compliance controller with a nonlinear model predictive control (NMPC)-based walking pattern generator. The compliance controller actively modulates the torso’s dynamics via impedance control, creating a virtual mass–spring–damper system that absorbs impacts and generates counterforces to resist sustained pushes. Concurrently, the NMPC module reformulates gait generation as a real-time optimization problem, simultaneously determining optimal footstep positions and orientations while respecting nonlinear constraints derived from centroidal momentum dynamics. Simulation results demonstrate that this integrated approach reduces the maximum ZMP error by 34.1% and the RMS ZMP error by 37.3% compared to traditional ZMP preview control, with a 38.9% improvement in settling time after a disturbance. This work establishes that the tight coupling of reactive impedance control and predictive optimization provides a foundational framework for enhancing the robustness and adaptability of bipedal locomotion. Full article

Partial demagnetization of multiple in-wheel motors changes torque distribution characteristics and can reduce vehicle stability, which poses a challenge for in-wheel motor drive electric vehicles (IWMDEVs) to maintain a balance between safety and efficiency. To address this issue, a hierarchical multi-objective adaptive fault-tolerant control (FTC) strategy based on wheel terminal torque compensation is developed. In the upper layer, a nonlinear model predictive controller (NMPC) generates the desired total driving force and corrective yaw moment according to vehicle dynamics and driving conditions. The lower layer employs a quadratic programming (QP) scheme to allocate the wheel torques under actuator and tire constraints. Two adaptive coefficients—the stability–efficiency weighting factor and the current compensation factor—are updated through a randomized ensembled double Q-learning (REDQ) algorithm, enabling the controller to adaptively balance yaw stability preservation and energy optimization under different fault scenarios. The proposed method is implemented and verified in a CarSim–Simulink–Python co-simulation environment. The simulation results show that the controller effectively improves yaw and lateral stability while reducing energy consumption, validating the feasibility and effectiveness of the proposed strategy. This approach offers a promising solution to achieve reliable and energy-efficient control of IWMDEVs. Full article

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. Full article

The primary objective of this research is to develop a probabilistic nonlinear model predictive control structure (NMPC) that efficiently operates uncrewed surface vehicles (USVs) in an environment that has probabilistic disturbances, such as wind, waves, and currents of the water, while simultaneously maneuvering the vehicle in a way that avoids stationary or moving stochastic obstacles in its path. The proposed controller structure considers the mean and covariances of the inputs or state variables of the vehicle in the cost function to handle probabilistic disturbances, where an extended Kalman filter (EKF) is utilized to calculate the mean, and the covariances are calculated dynamically via a linear matrix equality based on this mean and obtained system matrices with successive linearization for every sampling instance. The proposed control structure deals with non-zero-mean probabilistic disturbances such as water current via an innovative approach that treats the mean of the disturbance as a deterministic part, which is estimated by a disturbance observer and eliminated by a control term in the controller in addition to the control signal obtained via MPC optimization; the effect of the remaining zero-mean part is handled over its covariance during the probabilistic MPC optimization. The probabilistic constraints are also dealt with by converting them to deterministic constraints, as in linear probabilistic MPC. However, unlike the linear MPC, these constraints updated each sampling instance with the information obtained via successive linearization. The control structure incorporates the velocity obstacle (VO) method for collision avoidance. In order to ensure stability, the proposed NMPC adopts a dual-mode strategy, and a stability analysis is presented. In the second mode, an LQG design that ensures stability in the existence of non-zero mean disturbance is also provided. The simulation results demonstrate that the proposed probabilistic NMPC framework effectively handles probabilistic disturbances as well as both stationary and moving obstacles, ensuring collision avoidance while reaching the desired position and orientation through optimal path tracking, outperforming the conventional NMPC. Full article

This study addresses the trajectory tracking problem for unmanned hovercrafts operating under unknown time-varying environmental disturbances and actuator saturation. To balance real-time performance with control accuracy, an event-triggered adaptive nonlinear model predictive control (EANMPC) method is proposed. The approach dynamically adjusts the prediction horizon based on tracking error and incorporates an event-triggering mechanism to reduce unnecessary control updates. This design significantly alleviates computational burden while maintaining robust tracking performance. Furthermore, a rigorous input-to-state stability proof is provided without resorting to local linearization. Simulation results under two distinct trajectories demonstrate that the proposed method achieves superior tracking accuracy and reduces computational cost by 57% compared to conventional NMPC. The framework thus offers a practical and efficient control solution for underactuated hovercraft systems operating in complex maritime environments. Full article

This article presents a cooperative framework for multi-robot wildfire monitoring that integrates dynamic Voronoi partitioning with large language model (LLM)-enhanced nonlinear model predictive control (NMPC) to address challenges in dynamic unknown environments. Conventional methods, particularly fixed-weight NMPC, lack adaptability in scenarios with suddenly changing obstacles, such as spreading fire fronts. Our approach employs a hierarchical architecture. At the task allocation level, an enhanced dynamic Voronoi algorithm ensures robust and collision-free area partitioning. At the motion control level, we innovatively leverage the semantic reasoning capability of LLMs to dynamically adjust the cost function weights of the NMPC in real time based on environmental features, overcoming the parameter rigidity of traditional controllers. Extensive simulations in benchmark environments demonstrate the framework’s superior performance over deep deterministic policy gradient (DDPG) and fixed-weight NMPC baselines, showing significant improvements in exploration efficiency and obstacle avoidance success rate. This work provides a viable solution that bridges high-level semantic cognition with low-level optimal control for robust autonomous surveillance. Full article

Mining articulated vehicles (MAVs) are widely used as primary transportation equipment in both underground and open-pit mines. These include various machines such as Load–Haul–Dump machines and mining trucks. Path tracking control for MAVs has been an important research topic. Most current research focuses on path tracking control during forward driving. However, there are relatively limited studies on reverse path tracking control. Reversing plays a crucial role in the operation of MAVs. Nevertheless, existing methods typically use the center of the front axle as the control point; therefore, the positioning system is usually installed at the front axle. In practice, however, this means the positioning system is actually located at the rear axle during reverse operations. While it is theoretically possible to infer the position and orientation of the front axle from the rear axle, a strong nonlinear relationship exists between the motion states of the front and rear axles, which introduces significant errors in the system. As a result, these existing methods are not suitable for reverse driving conditions. To address this issue, this paper proposes a nonlinear model predictive control (NMPC) method for path tracking during mining-articulated vehicle (MAV) reverse operations. This method innovatively reconstructs the reverse-motion model by selecting the center of the rear axle as the control point, effectively addressing the instability issues encountered in traditional control methods during reverse maneuvers without requiring additional positioning devices. A comparative analysis with other control strategies, such as NMPC for forward driving, reverse NMPC using the front axle model, and reverse linear model predictive control (LMPC), reveals that the proposed NMPC method achieves excellent control accuracy. Displacement and heading error amplitudes do not exceed 0.101 m and 0.0372 rad, respectively. The maximum solution time per control period is 0.007 s. In addition, as the complexity of the reverse path increases, it continues to perform excellently. Simulation results show that as the curvature of the U-shaped curve increases, the proposed NMPC method consistently maintains high accuracy under various operational conditions. Full article

In order to address the significant nonlinear dynamic characteristics and limited trajectory tracking accuracy of unmanned vehicles under cornering conditions, this paper proposes a trajectory tracking control strategy based on Adaptive Model Predictive Control (AMPC). First, to enhance the accuracy of the vehicle model, an 11-degree-of-freedom vehicle dynamics model is established, incorporating pitch, roll, yaw, rotation around the Z-axis, and wheel-axis rotation. The vehicle motion equations are derived using Lagrangian analytical mechanics. Meanwhile, the tire model is optimized by accounting for the influence of vehicle attitude changes on tire mechanical properties. Based on the principles of nonlinear model predictive control (NMPC) and adaptive control, the AMPC algorithm is developed, its prediction model is constructed, and appropriate control constraints are defined to ensure improved accuracy and stability in trajectory tracking. Finally, simulations under double-lane-change and serpentine driving conditions are conducted using a co-simulation platform involving Carsim and Matlab/Simulink. The results demonstrate that the proposed controller achieves high trajectory tracking accuracy, effectively suppresses vehicle yaw, pitch, and roll motions, and enhances both the smoothness of trajectory tracking and the overall dynamic stability of the vehicle. Full article

Ducted fan flying robots with robotic arms can perform physical interaction tasks in complex environments such as indoors. However, the coupling effects between the aerial platform, the robotic arm, and physical environment pose significant challenges for the robot to accurately approach and stably contact the target. To address this problem, we propose a unified control framework for a ducted fan flying robot that encompasses both flight planning and physical interaction. This contribution mainly includes the following: (1) A nonlinear model predictive control (NMPC)-based trajectory optimization controller is proposed, which achieves accurate and smooth tracking of the robot’s end effector by considering the coupling of redundant states and various motion and performance constraints, while avoiding potential singularities and dangers. (2) On this basis, an easy-to-practice hierarchical control framework is proposed, achieving stable and compliant contact of the end effector without controller switching between the flight and interaction processes. The results of experimental tests show that the proposed method exhibits accurate position tracking of the end effector without overshoot, while the maximum fluctuation is reduced by up to 75.5% without wind and 71.0% with wind compared to the closed-loop inverse kinematics (CLIK) method, and it can also ensure continuous stable contact of the end effector with the vertical wall target. Full article

To enhance the path-tracking accuracy and control stability of articulated underground vehicles navigating high-curvature tunnels, this paper proposes a novel Multi-Time-Scale Nonlinear Model Predictive Control (MTS-NMPC) strategy. The core innovation lies in its dynamic adaptation of the prediction horizon to simultaneously compensate for the body torsion effects and yaw deviations induced by high-speed cornering. A high-fidelity vehicle dynamics model is first established. Subsequently, an adaptive mechanism is designed to adjust the prediction horizon based on the reference speed and road curvature. Experimental results demonstrate that the proposed MTS-NMPC achieves remarkable reductions of 35% and 17% in the maximum lateral tracking error and heading deviation, respectively, compared to conventional NMPC. It also improves stability by suppressing the velocity fluctuations of the articulated joint. The superior control performance and robustness of our method are further validated through field tests in an underground mine. Full article

To enhance wind farm frequency regulation in renewable-dominant power systems, this paper proposes a bi-level hybrid framework integrating deep learning and model predictive control (MPC) by retaining the critical wake propagation delay while neglecting higher-order turbulence effects. The upper layer employs a synthetic inertial intelligent control strategy based on contractive autoencoder (CAE) and deep neural network (DNN). Particle swarm optimization (PSO) obtains optimal synthetic inertial parameters for dataset construction, CAE extracts features from multi-dimensional inputs, and DNN outputs optimal coefficients to determine the total power deficit the wind farm needs to supply. The lower layer uses a nonlinear model predictive control (NMPC) strategy with the discretized rotor motion equation as the prediction model and optimization under constraints to allocate the total power deficit to each turbine. MATLAB/Simulink case studies show that, compared with fixed-coefficient synthetic inertial control, the proposed framework raises the frequency nadir by 0.01–0.02 Hz, shortens the settling time by over 200 s under 2–4% load disturbances, and maintains rotor speed within the safe range. This work significantly enhances the wind farm’s frequency regulation performance, contributing to power system and energy sustainability. Full article

To address the issues of external unknown disturbances and roll motion in the tracking control of underactuated unmanned surface vehicle (USV) formation, a cooperative formation control method based on nonlinear model predictive control (NMPC) algorithm and finite-time disturbance observer is proposed. Initially, a tracking error model for the USV formation is established within a leader–follower framework, utilizing a four-degree-of-freedom (4-DOF) dynamic model to simultaneously account for roll motion and trajectory tracking. This error model is then approximately linearized and discretized. To mitigate the initial non-smoothness in the desired trajectories of the follower USVs, a tracking differentiator is designed to smooth the heading angle of the leader USV. Thereafter, a quasi-infinite horizon NMPC algorithm is developed, in which a terminal penalty function is constructed based on quasi-infinite horizon theory. Furthermore, a finite-time disturbance observer is developed to facilitate real-time estimation and compensation for unknown marine disturbances. The proposed method’s effectiveness is validated both mathematically and in simulation. Mathematically, closed-loop stability is rigorously guaranteed via a Lyapunov-based proof of the quasi-infinite horizon NMPC design. In simulations, the algorithm demonstrates superior performance, reducing steady-state tracking errors by over 80% and shortening convergence times by up to 75% compared to a conventional PID controller. These results confirm the method’s robustness and high performance for complex USV formation tasks. Full article

This paper addresses the challenge of stabilizing an unmanned aerial vehicle with an arm (UAM) on a pipeline with disturbance, where the disturbance factors include white noise, mass uncertainty, and wind disturbance. An anti-disturbance control method is proposed utilizing nonlinear model predictive control (NMPC). Initially, the natural wind field model is developed. Considering wind disturbance, the UAM dynamics are analyzed utilizing Newton–Euler theory. Subsequently, the no-slip constraints and the terminal constraints are defined to prevent UAM from destabilizing and falling. The NMPC-based algorithm is developed to ensure the stable control of UAM, transforming the optimization problem into a nonlinear programming problem. The terminal cost function and the inequality constraints for establishing the state variables using linear quadratic regulator (LQR) are meticulously studied. Finally, numerical simulations are carried out to further verify the proposed method, considering internal disturbance about physical parameters and external disturbance about wind. Simulation results show that the disturbance is well compensated, and the UAM tilt angle is less than 0.3 deg. Therefore, the proposed control method can comprehensively consider the input energy consumption and the realization of stability, and has a certain degree of anti-interference. Full article

Autonomous vehicle trajectory tracking control is one of the hot topics in the autonomous driving field. One of the most widely used control methods is MPC (Model Predictive Control). As the control system generally becomes more nonlinear and complex, more nonlinear system factors are added to the MPC method. However, tracking accuracy and the amount of calculation needed are both dependent on a lot of contradictions for NMPC (Nonlinear Model Predictive Control). This research proposes a control algorithm for MPC-fused PID (Proportional-Integral-Derivative) control that ensures tracking accuracy under different high-speed driving conditions on wet and slippery road surfaces. The objective of the algorithm is twofold: first, to enhance trajectory tracking accuracy, and second, to ensure real-time control and optimize the vehicle’s comfort, economy, and safety indexes. The results of the joint simulation in Carsim/MATLAB Simulink show that trajectory tracking accuracy is improved by at least 22.2% under high-speed driving conditions of a vehicle on a wet and slippery road. At the same time, the comfort, economy, and safety of the vehicle are improved by at least 9.4%, 19.8%, and 5.3%, respectively. Full article

Detecting internal defects in narrow and curved pipelines remains a significant challenge, due to the difficulty of achieving reliable defect perception under low-light conditions and generating collision-free motion trajectories. To address these challenges, this article proposes an event-aware ES-YOLO framework, and develops a pipeline defect inspection experimental environment that utilizes a hyper-redundant manipulator (HRM) to insert an event camera into the pipeline in a collision-free manner for defect inspection. First, to address the lack of datasets for event-based pipeline inspection, the ES-YOLO framework is proposed. This framework converts RGB data into an event dataset, N-neudet, which is subsequently used to train and evaluate the detection model. Concurrently, comparative experiments are conducted on steel and acrylic pipelines under three different illumination conditions. The experimental results demonstrate that, under low-light conditions, the event-based detection model significantly outperforms the RGB detection model in defect recognition rates for both types of pipelines. Second, a pipeline defect detection physical system is developed, integrating a visual perception module based on the ES-YOLO framework and a control module for the snake-like HRM. The system controls the HRM using a combination of Nonlinear Model Predictive Control (NMPC) and the Serpentine Crawling Algorithm (SCA), enabling the event camera to perform collision-free inspection within the pipeline. Finally, extensive pipeline insertion experiments are conducted to validate the feasibility and effectiveness of the proposed framework. The results demonstrate that the framework can effectively identify steel pipeline defects in a 2 Lux low-light environment, achieving a detection accuracy of 84%. Full article