This article focuses on the scaled bipartite consensus control for high-order multiagent systems (MASs) based on fast fixed-time stability with a novel active disturbance rejection control (ADRC) strategy. First, a time-varying fast fixed-time extended state observer (TVFFxTESO) is designed to observe the unknown states and disturbances, which can increase the observation speed and accuracy while reducing the peak value (PV). Moreover, a fast fixed-time distributed observer (FFxTDO) is proposed to realize the estimation of the leader’s state by each follower, to obtain its expected reference trajectory. Furthermore, a fast fixed-time control protocol is constructed for each follower to achieve scaled bipartite consensus. Simulation results, including a numerical example and an application to wheeled mobile robots (WMRs), validate that the proposed method can effectively improve the dynamic and steady-state control performance.
This article explores an event-triggered self-learning parallel tracking control strategy for continuous-time nonlinear systems with actuator faults. To mitigate the impact of actuator faults, an enhanced performance index function is developed based on the unknown upper bound of such faults. Furthermore, an augmented error nonlinear system is constructed by incorporating the tracking error and control input of the original system, so as to implement parallel tracking control via the adaptive dynamic programming (ADP) approach. By designing an adaptive law, the unknown upper bound of faults is estimated online, which adjusts the control parameters in real-time and compensates for the loss of control effectiveness induced by faults. A critic network is employed to approximate the optimal value function within an event-triggered framework, while a dynamic triggering mechanism is adopted to reduce communication overhead. Through the design of appropriate Lyapunov functions, the asymptotic stability of the closed-loop system is rigorously proven, and the weight estimation error is demonstrated to be uniformly ultimately bounded (UUB). Finally, the simulation results are provided to validate the effectiveness of the proposed control strategy.
This work investigates the zero-error tracking problem for linear systems with unknown dynamics. The proposed method integrates a data-driven system model with an auxiliary system for error characterization. First, a nonminimal state-space (NMSS) model is constructed from historical input-output data. Then, an auxiliary system is formulated by leveraging the common minimal polynomial shared by the disturbance and reference trajectory. The stabilizability and detectability of the auxiliary system are rigorously verified, thereby guaranteeing the existence of a unique positive semidefinite solution to the Riccati equation. Inspired by reinforcement learning (RL) principles, a data-driven output-feedback optimal tracking controller is developed by iteratively learning the optimal Q-function. The theoretical superiority of the proposed controller over existing approaches is established. Simulation results further demonstrate its effectiveness in addressing the tracking problem subject to measurement delays and unmeasurable disturbances.
Safe trajectory tracking for nonlinear stochastic systems operating in obstacle-cluttered environments remains a significant challenge, as random disturbances and obstacle-induced constraints can simultaneously degrade tracking accuracy and threaten system safety. To overcome this issue, this article develops a safety-aware optimal tracking control framework that integrates stochastic control barrier functions (CBFs) with adaptive dynamic programming (ADP) for obstacle avoidance. To ensure safety, a logarithmic-type stochastic CBF is constructed to enforce obstacle-avoidance constraints, and theoretical guarantees are provided for the stochastic forward invariance (SFI) of the safe set. Furthermore, based on the integral reinforcement learning (RL) framework, an ADP algorithm is developed to relax the need for system dynamics. A critic-only neural network (NN) scheme is employed to approximate the solution of the Hamilton-Jacobi-Bellman (HJB) equation, with a fixed-time weight update rule established to guarantee convergence independent of initial conditions. Meanwhile, an experience replay mechanism is incorporated to relax the persistent excitation condition. It is further shown that the estimation error of the NN weights is fixed-time stable (FxTS). Finally, simulation results demonstrate that the designed method achieves optimal trajectory tracking while ensuring safety under stochastic dynamics, even in scenarios involving multiple obstacles.
Because of the similar structures and motion mechanisms between pneumatic artificial muscles (PAMs) and biological muscles, PAM-actuated wrist joints exhibit substantial potential for applications in rehabilitation and industrial fields. Nevertheless, the complex nonlinearities, hysteresis, and creep inherent in PAMs bring significant challenges to precise motion control. Moreover, the material compliance and limited motion ranges of PAMs make it difficult to guarantee the safe operation of multi-PAM-actuated robots. To this end, a new iterative learning control (ILC) method is investigated to accomplish the efficient tracking control of PAM-actuated wrist joints. Without complex modeling work, ILC is introduced to estimate iteration-invariant dynamic model parameters. In particular, the identical initial condition in traditional ILC is relaxed by implementing a trajectory reconstruction mechanism. Also, a tanh-type adaptive law is designed to estimate the upper bounds of the lumped disturbances without prior knowledge. Moreover, an asymmetric time-varying barrier Lyapunov function (BLF) is utilized, ensuring that the tracking errors are maintained within permissible limits. The convergence of the proposed control method is demonstrated in both the time and iteration domains. Eventually, the effectiveness and superiority of the control method are validated via experiments.
