Fault-tolerant adaptive tracking control of Euler-Lagrange systems – An echo state network approach driven by reinforcement learning

Reinforcement learning (RL) has enjoyed considerable success in application to nonlinear systems. However, very few RL-based works that explicitly address the control problem of MIMO nonlinear systems with subject to actuator failures. In this work, we develop a fault-tolerant adaptive tracking control method fused with an echo state network (ESN) driven by reinforcement learning for Euler-Lagrange systems subject to actuation faults. The proposed control includes an associative search network (ASN), a control gain network (CGN), and an adaptive critic network (ACN), with ASN to estimate the unknown items of the control system, CGN to deal with the time-varying and unknown control gains matrix, and ACN to generate the reinforcement signal, all together ensuring stable tracking and accommodate modeling uncertainties and actuation failures. Different from traditional reinforcement learning controllers that utilizes radial basis function neural networks (RBFNN) or fuzzy systems, the proposed one adopts an echo state network, a paradigm of recurrent neural networks, to implement the ASN, ACN and CGN, resulting in enhanced learning capabilities and stronger robustness against external uncertainties and disturbances, thus better control performance.