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In this paper, a novel feature-based sampling strategy for nonlinear Model Predictive Path Integral (MPPI) control is presented. Using the MPPI approach, the optimal feedback control is calculated by solving a stochastic optimal control (OCP) problem online by evaluating the weighted inference of sampled stochastic trajectories. While the MPPI algorithm can be excellently parallelized, the closed-loop performance strongly depends on the information quality of the sampled trajectories. To draw samples, a proposal density is used. The solver’s and thus, the controller’s performance is of high quality if the sampled trajectories drawn from this proposal density are located in low-cost regions of state-space. In classical MPPI control, the explored state-space is strongly constrained by assumptions that refer to the control value’s covariance matrix, which are necessary for transforming the stochastic Hamilton–Jacobi–Bellman (HJB) equation into a linear second-order partial differential equation. To achieve excellent performance even with discontinuous cost functions, in this novel approach, knowledge-based features are introduced to constitute the proposal density and thus the low-cost region of state-space for exploration. This paper addresses the question of how the performance of the MPPI algorithm can be improved using a feature-based mixture of base densities. Furthermore, the developed algorithm is applied to an autonomous vessel that follows a track and concurrently avoids collisions using an emergency braking feature. Therefore, the presented feature-based MPPI algorithm is applied and analyzed in both simulation and full-scale experiments.

This paper proposes an event-triggered robust nonlinear model predictive control (NMPC) framework for cyber-physical systems (CPS) in the presence of denial-of-service (DoS) attacks and additive disturbances. In the framework, a new robustness constraint is introduced to the NMPC optimization problem in order to deal with additive disturbances, and a packet transmission strategy is designed for NMPC such that DoS attacks can be tackled. Then, an event-triggered mechanism, which accommodates DoS attacks occurring in the communication network, is proposed to reduce the communication cost for resource-constrained CPSs. Besides, we prove that the NMPC algorithm is recursively feasible and the closed-loop system is input-to-state practical stable under some sufficient conditions. Simulation examples and comparisons are conducted to show the effectiveness of the proposed NMPC algorithm.

Real-time nonlinear model predictive control (NMPC) of a nonlinear system with extremely short sampling periods poses significant challenges, particularly in balancing optimality in solving non-convex optimization problems with the computational efficiency required for real-time implementation. To address this, a trust region nonlinear model predictive control (TR-NMPC) method is proposed based on a real-time iteration scheme, enabling stable and effective solutions to the non-convex minimization problem inherent in NMPC. Firstly, radial basis function-based autoregressive model with exogenous variables (RBF-ARX) is employed to describe the dynamics of a magnetic levitation ball system, forming the basis in NMPC design. Then, the non-convex optimization problem in NMPC is approximated in the real-time iteration scheme. To constrain the approximation error, we propose and analyze a trust region optimization method, which dynamically adjusts the trust region in each iteration based on the discrepancy between the designed and approximated objective functions. By combining the trust region optimization method with the RBF-ARX model-based parameter scheduling strategy in real-time iteration scheme, the non-convex optimization problem in NMPC is solved with high real-time efficiency. Simulation and real-time control experiments on the magnetic levitation ball system demonstrate that the proposed NMPC method achieves both exceptional computational efficiency and superior transient performance.

Magnetic shape memory alloy-based actuator (MSMA-BA) has the advantages of large strain and high resolution. However, the inherent hysteresis characteristics accompanied by the dead zone in MSMA seriously degrade the positioning accuracy of MSMA-BA. In this study, a gated recurrent neural network (GRNN)-based nonlinear model predictive control (NMPC) method is designed to achieve precise trajectory tracking control of the MSMA-BA. First, a GRNN-based nonlinear auto-regressive moving average with exogenous inputs (NARMAX) model is designed to predict the various nonlinear characteristics of MSMA-BA. Based on the established model, an NMPC method with an anti-dead-zone function is designed. The introduced anti-dead-zone function enables the proposed NMPC algorithm to accelerate the response speed within the dead zone and prevents violent oscillations in the system. The ability of the NMPC to address the hysteresis characteristics accompanied by the dead zone is enhanced. Additionally, the convergence of the proposed NMPC method is analyzed using the Lyapunov stability theory. Extensive experiments are conducted on the MSMA-BA to validate the effectiveness of the proposed method.

Manipulator mounted on an unmanned satellite could be used for performing orbital capture maneuver in order to repair satellites or remove space debris from orbit. Use of manipulators for such purposes presents unique challenges, as high level of autonomy is required and the motion of the manipulator influences the position and orientation of the manipulator-equipped satellite. This paper presents a new control system that consists of two modules: trajectory planning module (based on trajectory optimization algorithm) and Model Predictive Controller. Both modules take into account the free-floating nature of the satellite-manipulator system. Proposed control system was tested in numerical simulations performed for a simplified planar case. In the first set of simulations Nonlinear Model Predictive Control (NMPC) was used to ensure realization of a square reference end-effector trajectory, while in the second set control system was used for optimizing and then ensuring realization of the trajectory that leads to grasping of the rotating target satellite. Simulations were performed with disturbances and with the assumed non-perfect knowledge of parameters of the satellite-manipulator system. Results obtained with NMPC are better than results obtained with the controller based on the Dynamic Jacobian inverse and with the Modified Simple Adaptive Control (MSAC).

The research on the path following control of the bionic underwater vehicle with high maneuverability and propulsive performance is a crucial issue. This paper investigates the planar path following control task of a bionic underwater vehicle with superior maneuverability. The steering characteristics of the sinusoidal offset and unilateral asymmetric steering signals are analyzed. The experimental results demonstrate that the two signals separately have smaller steering radius and power consumption in the low-frequency and relatively high-frequency range, and are consequently suitable for distinct scenarios. Furthermore, a switching nonlinear model predictive control strategy is proposed, which regulates the state error weighting values according to the real-time yaw angle error. The control strategy realizes autonomous switching of multiple locomotion modes to enhance the swimming speed of the bionic underwater vehicle and fulfills the purpose of improving the task-completing efficiency. The simulation and experimental results indicate that the bionic underwater vehicle achieved 3.49 and 2.92 times enhancement in swimming speed performance at straight and steering target path following control tasks, respectively. The proposed methods as well as obtained results can provide universal inspiration for the multiple motion-based path following control of autonomous underwater vehicles.

This paper presents a new flight control framework for tiltrotor multirotor uncrewed aerial vehicles (MRUAVs). Tiltrotor designs offer full actuation but introduce complexity in control allocation due to actuator redundancy. We propose a new approach where the allocator is tightly coupled with the controller, ensuring that the control signals generated by the controller are feasible within the vehicle actuation space. We leverage Nonlinear Model Predictive Control (NMPC) to implement the above framework, providing feasible control signals and optimizing performance. This unified control structure simultaneously manages both position and attitude, which eliminates the need for cascaded position and attitude control loops. Extensive numerical experiments demonstrate that our approach significantly outperforms conventional techniques that are based on Linear Quadratic Regulator (LQR) and Sliding Mode Control (SMC), especially in high-acceleration trajectories and disturbance rejection scenarios, making the proposed approach a viable option for enhanced control precision and robustness, particularly in challenging missions.

The field of autonomous driving vehicles is growing and expanding rapidly. However, the control systems for autonomous driving vehicles still pose challenges, since vehicle speed and steering angle are always subject to strict constraints in vehicle dynamics. The optimal control action for vehicle speed and steering angular velocity can be obtained from the online objective function, subject to the dynamic constraints of the vehicle’s physical limitations, the environmental conditions, and the surrounding obstacles. This paper presents the design of a nonlinear model predictive controller subject to hard and softened constraints. Nonlinear model predictive control subject to softened constraints provides a higher probability of the controller finding the optimal control actions and maintaining system stability. Different parameters of the nonlinear model predictive controller are simulated and analyzed. Results show that nonlinear model predictive control with softened constraints can considerably improve the ability of autonomous driving vehicles to track exactly on different trajectories.

Current control systems for autonomous surface vessels (ASVs) often disregard model uncertainties and the need to adapt dynamically to varying model parameters. This limitation hinders their ability to ensure reliable performance under complex and frequently changing maritime conditions, highlighting the need for more adaptive and robust approaches. Therefore, this study introduces an innovative approach that integrates deep reinforcement learning (DRL) with nonlinear model predictive control (NMPC) to optimize the control performance and model parameters of ASVs. The primary objective is to ensure that the digital twin of the ASV remains continuously synchronized with its physical counterpart, thereby enhancing the accuracy, reliability, and adaptability of the digital twin in representing the vessel under complex and dynamic maritime conditions. Leveraging the capabilities of digital twins, agents can be trained in safety-critical applications within a risk-free virtual environment, minimizing the hazards associated with real-world experimentation. The DRL framework optimizes NMPC by tuning its parameters for peak performance and identifying unknown model parameters in real-time, ensuring precise and dependable vessel control. Extensive simulations confirm the effectiveness of this approach in improving the safety, efficiency, and reliability of ASVs. The proposed methods address critical challenges in ASV control by enhancing reliability and adaptability under dynamic conditions, providing a foundation for future advancements in autonomous maritime navigation and control system development.

This paper considers the problem of formation control for a team of nonholonomic wheeled mobile robots considering obstacle avoidance. A new control algorithm based on the model predictive control (MPC) and the nonlinear dynamics of the system is presented here. The control algorithm is applied to the nonlinear system using two different controllers including linear MPC and nonlinear MPC. The virtual structure formation approach and artificial potential field method are employed to determine the reference trajectories of the robots and to solve the problem of obstacle avoidance. A control algorithm consisting of two parts is proposed to track the trajectories and maintain the team’s formation. Two advantages of using MPC techniques are the ability to consider control and state constraints which are of high importance in practical applications. The main contribution of this paper is the design of two robust control systems to disturbance with respect to actuator saturation limits. Simulation results demonstrate the effectiveness and robustness of the proposed control algorithm in trajectory tracking and formation maintenance in the presence of disturbance and actuator limits.