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The event-triggered time-varying formation tracking for a class of second-order multi-agent systems (MASs) subject to a non-cooperative leader is investigated in this paper. First, in the presence of the unknown input of the leader and external disturbances, a distributed observer with adaptive parameters is put forward for followers to estimate the velocity tracking error. Then, based on the estimated tracking error and an auxiliary variable, a finite time formation controller is further constructed, which is updated depending on a pre-designed event-triggered mechanism. As a result, the desired time-varying formation configuration can be realized in finite time with less communication resource consumption. It's noted that the constructed formation strategy doesn't rely on any global information and thus is fully distributed. The stability of the controlled multi-agent system is proved rigorously and it's verified that event-triggered intervals are with a positive lower bound. At last, simulations are carried out to illustrate the effectiveness of the presented algorithm. [ABSTRACT FROM AUTHOR]

In this note, a high-order disturbance observer for a class of underactuated robotic systems with mismatched uncertainties is proposed. To improve its estimation performance, a choice method of optimal gain matrices of the high-order disturbance observer is obtained as the solution of an optimization problem. A high-order disturbance observer based sliding mode control method is also designed for this class of underactuated robotic system with mismatched disturbance. The stabilities of both the disturbance observer and the controlled closed-loop system are proved by the Lyapunov theorem. Simulations on a benchmark Acrobot system demonstrate the efficiency of proposed approaches. [ABSTRACT FROM AUTHOR]

To realize a high-accurate trajectory tracking control of the Pneumatic Muscle Actuator (PMA), a comprehensive single-layer neural network (SNN) and Echo State Neural Network (ESN) based predictive control with particle swarm optimization (PSO) is proposed, where PSO optimizes the weight coefficients of the SNN and the ESN state is updated by the online Recursive Least Square (RLS) algorithm for predictive control. Based on Lyapunov theory, the learning convergence theorem is established to guarantee the stability of the closed-loop system. The proposed control algorithm is employed for the trajectory tracking control of PMA. The interface between the xPC target and the virtual instrument was established to realize the real-time control and to make the control more accurate and stable. Both simulations and experiments were performed to verify the proposed methods. The experiments were conducted on the real PMA system, which was connected with the xPC target system. The results demonstrate the validity of PMA as well as the effectiveness of the novel control algorithm. [ABSTRACT FROM AUTHOR]