Tangent barrier Lyapunov function-based constrained control of flexible manipulator system with actuator failure

This article puts forward an adaptive neural network fault-tolerant control scheme under the state constraints for the flexible manipulator system with uncertain terms. The dynamic model of the system is described by partial differential equations. The tangent barrier Lyapunov functions are chosen in the design process for the sake of ensuring that all states in the system satisfy the constrained conditions. The uncertainties resulted from load mass, hub inertia, and bending stiffness in the system are approximated by using the universal approximation property of neural networks. The adaptive method is used to counteract the influence of joint motor actuator failure. At the same time, combining with the backstepping design framework to design effective controllers to assure that all signals in the closed-loop system are bounded. Lyapunov stability analysis method is used to prove the stability of the system. Finally, the simulation results prove the availability of the raised control method.

Automated summary

This article proposes an adaptive neural network fault-tolerant control scheme for flexible manipulator systems under state constraints, using Lyapunov functions and neural network approximation to achieve stable control and designing effective controllers in the closed-loop system to ensure bounded signals. The stability of the system is proven using Lyapunov stability analysis method, and simulation results confirm the effectiveness of the proposed control method.