Fully Distributed Control for a Class of Uncertain Multi-Agent Systems with a Directed Topology and Unknown State-Dependent Control Coefficients

To address the control of uncertain multi-agent systems (MAS) with completely unknown system nonlinearities and unknown control coefficients, a global consensus method is proposed by constructing novel filters and barrier function-based distributed controllers. The main contributions are as follows. Firstly, a novel two-order filter is designed for each agent to produce informational estimates from the leader, such that a connectivity matrix is not used in the controller's design, solving the difficultly caused by the time-varying control coefficients in a MAS with a directed graph. Secondly, combined with the novel filters, barrier functions are used to construct the distributed controller to deal with the completely unknown system nonlinearities, resulting in the global consensus of the MAS. Finally, it is rigorously proved that the consensus of the MAS is achieved while guaranteeing the prescribed tracking-error performance. Two examples are given to verify the effectiveness of the proposed method, in which the simulation results demonstrate the claims.

Optimal adaptive computed torque control for haptic-teleoperation system with uncertain dynamics

This study aimed to eliminate dynamic uncertainty, one of the main problems of haptic teleoperation robotic systems. The optimal adaptive computed torque control method was used to overcome this problem. As is known, excellent stability and transparency are required in teleoperation systems. However, dynamic uncertainty that causes stability problems in the control of these systems also causes poor performance. In conventional adaptive computed torque control methods, updating the parameters of the system is generally discussed, but updating the control coefficients of vital importance in the control of the system is not considered. In the proposed method, an adaptation rule has been created to update uncertain parameters. In addition, the gray wolf optimization algorithm, one of the current optimization algorithms, has been proposed and applied to obtain the control coefficients of the system. The position tracking stability of the system was examined by using Lyapunov stability analysis method. As a result, both simulation and real-time optimal adaptive computational torque control method were used and bilateral position and force control was performed and the performance results of the system are obtained graphically and examined. Optimal adaptive computed torque control method obtained using the gray wolf optimization algorithm was used first in the literature search and success results have been obtained. In this regard, the authors have the idea that this work is an innovative aspect of both simulation and real time with the optimal adaptive computed torque control method.

Adaptive tracking control for uncertain nonlinear systems subject to unknown control coefficients, state constraints, and input saturation

This article is committed to studying the tracking control problem for a class of uncertain nonlinear system with unknown control coefficients. The system is subject to full state constraints, input saturation constraint, and external disturbances simultaneously. By introducing a hyperbolic tangent function to approximate the saturated input function, the sharp corner caused by the input saturation is avoided. In the meanwhile, an auxiliary system is constructed to compensate the resulting approximation error. By using the barrier Lyapunov function (BLF) based adaptive backsteping control, the Nussbaum-type adaptive controllers are constructed for the augmented system with unknown control direction. It not only ensures the system states are always within the constrained range, but also guarantees the tracking performance of the system, no matter whether the control direction of the system is known or not. Meanwhile, dynamic surface control (DSC) is used in the controller design, which avoids ”computation explosion” caused by the repeated derivation of virtual control law. Aiming at the nonparametric uncertainty of the system, a common adaptive law is designed by combining the unknown constant bounds of the external disturbance with the error term caused by input saturation estimation. It improves the tracking performance of the system and reduces the burden of the controller greatly. Finally, a simulation example is given to demonstrate the effectiveness of the proposed control scheme in three scenarios.