Finite-Time Command-Filtered Composite Adaptive Neural Control of Uncertain Nonlinear Systems

Robust Adaptive Control of Uncertain Fully Actuated Systems With Unknown Parameters and Perturbed Input Matrices

Robust adaptive control of fully actuated systems (FASs) with unknown parameters, perturbed input matrices and nonlinear uncertainties is considered. Two novel robust adaptive controllers are developed for the two cases where the unknown parameters are time-varying and constant. For both cases, different from the existing results on FASs with unknown parameters, this article allows the existence of a perturbation matrix that satisfies a certain assumption in the input matrix. Furthermore, for the case of time-varying parameters, under relaxed system assumptions, the global boundedness of the state variables and the estimation error is guaranteed. For the case of constant parameters, under certain assumptions on the nonlinear uncertainty and known nonlinear functions, no pre-estimation of the unknown parameters is required and the state variables globally asymptotically converge to the origin. In addition, a parallel extension of the proposed methods to the generalized multiorder FAS case is also given. The effectiveness of the developed methods is shown by the successful application in the control of electromechanical systems.

A Novel Parallel Control Method for Optimal Consensus of Nonlinear Multiagent Systems

This work concentrates on the initial introduction of parallel control to investigate an optimal consensus control strategy for continuous-time nonlinear multiagent systems (MASs) via adaptive dynamic programming (ADP). First, the control input is integrated into the feedback system for parallel control, facilitating an augmented system's optimal consensus control with an appropriate augmented performance index function to be established, which is identical to the original system's suboptimal control with a conventional performance index. Second, the feasibility of the proposed control scheme is evaluated based on the policy iteration algorithm, and the convergence of the algorithm is demonstrated. Then, an online learning algorithm becomes available to implement the ADP-based optimal parallel consensus control protocol without prior knowledge of the system. The Lyapunov approach is employed to indicate that the signals are convergent. Ultimately, the experimental data support the theoretical results.

Observer-Based Neural Control of N-Link Flexible-Joint Robots

This article concentrates on the adaptive neural control approach of n -link flexible-joint electrically driven robots. The presented control method only needs to know the position and armature current information of the flexible-joint manipulator. An adaptive observer is designed to estimate the velocities of links and motors, and radial basis function neural networks are applied to approximate the unknown nonlinearities. Based on the backstepping technique and the Lyapunov stability theory, the observer-based neural control issue is addressed by relying on uplink-event-triggered states only. It is demonstrated that all signals are semi-globally ultimately uniformly bounded and the tracking errors can converge to a small neighborhood of zero. Finally, simulation results are shown to validate the designed event-triggered control strategy.

Fixed-time command filtered output feedback control for twin-roll inclined casting system with prescribed performance

The article investigates the issue of fixed-time control with adaptive output feedback for a twin-roll inclined casting system (TRICS) with disturbance. First, by using the mean value theorem, the nonaffine functions are decoupled to simplify the system. Second, radial basis function neural networks (RBFNNs) are introduced to approximate an unknown term, and a nonlinear neural state observer is created to handle the effects of unmeasured states. Then, the backstepping design framework is combined with prescribed performance and command filtering techniques to demonstrate that the scheme proposed in this article guarantees system performance within a fixed-time. The control design parameters determine the upper bound of settling time, regardless of the initial state of the system. Meanwhile, it ensures that all signals in the closed-loop system (CLS) remain bounded, and it can also maintain the tracking error within a predefined range within a fixed time. Finally, simulation results assert the effectiveness of the method.

Neural network-based finite-time command-filtered adaptive backstepping control of electro-hydraulic servo system with a three-stage valve

This paper aims to improve the tracking control performance of the three-stage valve (TSV) controlled electro-hydraulic servo system (EHSS) with parameter uncertainties and other lumped unknown nonlinearities, including unknown dynamics and disturbances. A more accurate nonlinear model of the TSV-controlled EHSS is established and a neural network-based finite-time command-filtered adaptive backstepping control (NNFCABC) method is proposed for the EHSS. Adaptive control is used to deal with the system parameter uncertainties, and the radial basis function neural network (RBFNN) algorithm is introduced to approximate the lumped unknown nonlinearities. The prediction errors of serial-parallel estimation models (SPEMs) and the tracking errors are utilized together to design adaptive laws to estimate the system parameters and the weights of the RBFNNs. The entire control framework utilizes command-filtered control and backstepping techniques. By applying Levant differentiators as command filters and introducing fractional power terms into the virtual control laws and the SPEMs, the proposed NNFCABC theoretically guarantees the tracking performance of the closed-loop control system with finite-time convergence. Comparative simulations and experiments verify the feasibility and superiority of the proposed control scheme.

Integration of path planning and following control for the stratospheric airship with forecasted wind field data

The absence of real-time airspeed sensors, which was more often ignored in previous studies, and low dynamic characteristics render stratospheric airship control challenging. This study creatively overcomes the aforementioned problems in an integrated path planning and following control scheme using forecasted wind field data. Herein, an efficient and practicable path planning algorithm is designed. Further, a smooth vector field guidance law is proposed for solving the problem of complex path following. Subsequently, an event-triggered neural network-based adaptive tracking controller is designed, considering the wind forecast error influence. Finally, these three parts are organically integrated to achieve autonomous flight. The stability of the closed-loop system and the exclusion of Zeno behavior are rigorously proved. The simulation results reveal that the convergence rate is 63.8% improved, essentially exhibiting better optimization, the tracking errors are eliminated within 80 s, and 99.4% control input updating times are saved.