Adaptive Fuzzy Control for Coordinated Multiple Robots With Constraint Using Impedance Learning

Adaptive Tracking Control of State Constraint Systems Based on Differential Neural Networks: A Barrier Lyapunov Function Approach

The aim of this article is to investigate the trajectory tracking problem of systems with uncertain models and state restrictions using differential neural networks (DNNs). The adaptive control design considers the design of a nonparametric identifier based on a class of continuous artificial neural networks (ANNs). The design of adaptive controllers used the estimated weights on the identifier structure yielding a compensating structure and a linear correction element on the tracking error. The stability of both the identification and tracking errors, considering the DNN, uses a barrier Lyapunov function (BLF) that grow to infinity whenever its arguments approach some finite limits for the state satisfying some predefined ellipsoid bounds. The analysis guarantees the semi-globally uniformly ultimately bounded (SGUUB) solution for the tracking error, which implies the achievement of an invariant set. The suggested controller produces closed-loop bounded signals. This article also presents the comparison between the tracking states forced by the adaptive controller estimated with the DNN based on BLF and quadratic Lyapunov functions as well. The effectiveness of the proposal is demonstrated with a numerical example and an implementation in a real plant (mass-spring system). This comparison confirmed the superiority of the suggested controller based on the BLF using the estimates of the upper bounds for the system states.

Appointed-Time Integral Barrier Lyapunov Function-Based Trajectory Tracking Control for a Hovercraft with Performance Constraints

This paper develops a totally new appointed-time integral barrier Lyapunov function-based trajectory tracking algorithm for a hovercraft in the presence of multiple performance constraints and model uncertainties. Firstly, an appointed-time performance constraint function is skillfully designed, which proposes to pre-specify the a priori transient and steady performances on the system tracking errors. Secondly, a new integral barrier Lyapunov function is constructed, which combines with the appointed-time performance constraint function to guarantee that the performance constraints on the system tracking errors are never violated. On this basis, an adaptive trajectory tracking controller is derived using the appointed-time integral barrier Lyapunov function technique in the combination of neural networks. According to Lyapunov’s stability theory, it can be shown that the proposed controller is capable of ensuring transient and steady performances on the output tracking errors. In particular, the position and speed tracking can be fulfilled in a user-appointed time without requiring complex control parameters selection. Finally, results from a comparative simulation study verify the efficacy and advantage of the proposed control approach.

Adaptive boundary control of a vibrating cantilever nanobeam considering small scale effects

This paper presents vibration control analysis for a cantilever nanobeam system. The dynamics of the system is obtained by the non-local elastic relationship which characterizes the small scale effects. The boundary conditions and governing equation are respectively expressed by several ordinary differential equations (ODE) and a partial differential equation (PDE) with the help of the Hamilton's principle. Model-based control and adaptive control are both designed at the free end to regulate the vibration in the control section. By employing the Lyapunov stability approach, the system state can be proven to be substantiated to converge to zero's small neighbourhood with appropriate parameters. Simulation results illustrate that the designed control is feasible for the nanobeam system.

Leader-Follower Bipartite Output Synchronization on Signed Digraphs Under Adversarial Factors via Data-Based Reinforcement Learning

The optimal solution to the leader-follower bipartite output synchronization problem is proposed for heterogeneous multiagent systems (MASs) over signed digraphs in the presence of adversarial inputs in this article. For the MASs, the dynamics and dimensions of the followers are different. Distributed observers are first designed to estimate the leader's two-way state and output over signed digraphs. Then, the leader-follower bipartite output synchronization problem on signed graphs is translated into a conventional output distributed leader-follower problem over nonnegative graphs after the state transformation by using the information of followers and observers. The effect of adversarial inputs in sensors or actuators of agents is mitigated by designing the resilient H_∞ controller. A data-based reinforcement learning (RL) algorithm is proposed to obtain the optimal control law, which implies that the dynamics of the followers is not required. Finally, a simulation example is given to verify the effectiveness of the proposed algorithm.