In this paper, the command filter-based adaptive fuzzy finite-time output feedback control (FOFC) is investigated for the Electro-hydraulic servo system. For the uncertainties in the system, we utilize the fuzzy logic systems (FLSs) to estimate these uncertain nonlinearities and fuzzy state observer is established to approximate the unmeasurable hydraulic cylinder stem speed and the internal cylinder force. Then, a command filter-based finite time output feedback control is proposed to achieve high tracking precision, where the tracking errors can be regulated into a small neighborhood around the equilibrium proved by the Lyapunov finite-time stability theory. Moreover, a command filter is introduced to avoid the explosion of complexity in the backstepping procedure, where a compensation mechanism is developed to compensate for filter errors. Finally, simulation results are provided to demonstrate the effectiveness of the proposed control.

Guaranteed safety and performance under various circumstances remain technically critical and practically challenging for the wide deployment of autonomous vehicles. Safety-critical systems in general, require safe performance even during the reinforcement learning (RL) period. To address this issue, a Barrier Lyapunov Function-based safe RL (BLF-SRL) algorithm is proposed here for the formulated nonlinear system in strict-feedback form. This approach appropriately arranges and incorporates the BLF items into the optimized backstepping control method to constrain the state-variables in the designed safety region during learning. Wherein, thus, the optimal virtual/actual control in every backstepping subsystem is decomposed with BLF items and also with an adaptive uncertain item to be learned, which achieves safe exploration during the learning process. Then, the principle of Bellman optimality of continuous-time Hamilton–Jacobi–Bellman equation in every backstepping subsystem is satisfied with independently approximated actor and critic under the framework of actor-critic through the designed iterative updating. Eventually, the overall system control is optimized with the proposed BLF-SRL method. It is furthermore noteworthy that the variance of the attained control performance under uncertainty is also reduced with the proposed method. The effectiveness of the proposed method is verified with two motion control problems for autonomous vehicles through appropriate comparison simulations.

High-performance learning-based control for the typical safety-critical autonomous vehicles invariably requires that the full-state variables are constrained within the safety region even during the learning process. To solve this technically critical and challenging problem, this work proposes an adaptive safe reinforcement learning (RL) algorithm that invokes innovative safety-related RL methods with the consideration of constraining the full-state variables within the safety region with adaptation. These are developed toward assuring the attainment of the specified requirements on the full-state variables with two notable aspects. First, thus, an appropriately optimized backstepping technique and the asymmetric barrier Lyapunov function (BLF) methodology are used to establish the safe learning framework to ensure system full-state constraints requirements. More specifically, each subsystem’s control and partial derivative of the value function are decomposed with asymmetric BLF-related items and an independent learning part. Then, the independent learning part is updated to solve the Hamilton–Jacobi–Bellman equation through an adaptive learning implementation to attain the desired performance in system control. Second, with further Lyapunov-based analysis, it is demonstrated that safety performance is effectively doubly assured via a methodology of a constrained adaptation algorithm during optimization (which incorporates the projection operator and can deal with the conflict between safety and optimization). Therefore, this algorithm optimizes system control and ensures that the full set of state variables involved is always constrained within the safety region during the whole learning process. Comparison simulations and ablation studies are carried out on motion control problems for autonomous vehicles, which have verified superior performance with smaller variance and better convergence performance under uncertain circumstances. The effectiveness of the safe performance of overall system control with the proposed method accordingly has been verified.

This article proposes an adaptive neural trajectory tracking control scheme for n -DOF robotic manipulators subjected to parameter variations, unknown functions, and time-varying external disturbances. First, the computed torque control (CTC) method is designed to reduce the system's nonlinearity. Second, radial basis function neural networks (RBFNNs) are constructed to approximate the uncertainties due to parameter variations and unknown functions. It is also important to note that the RBFNN's centers and widths are defined by state constraints. As a result of the nonlinear disturbance observer (NDO), the RBFNNs' approximation errors and disturbances are estimated to further improve tracking performance. The barrier Lyapunov function (BLF) ensures the closed-loop system's stability, guaranteeing tracking performance while preventing state constraint violation. Furthermore, sensitivity analysis provides a ranking of the importance of design parameters in influencing dynamic responses. Finally, simulations on a seven-degrees of freedom robotic manipulator are performed to validate the effectiveness of the proposed method.

This paper considers integrated guidance and control system with input saturation for homing missiles based on the three channels independence design idea. An auxiliary system is first introduced into the integrated guidance and control model for the pitch channel. Then, a smooth function and a Nussbaum type function are employed in the dynamic surface control design to deal with the problem of input saturation constraint. The proposed control scheme guarantees the desired interception performance and the stability of the missile dynamics. The similar control scheme is further applied to the yaw channel and roll channel respectively. Nonlinear 6-DOF simulations are carried out to demonstrate the effect of the approach.