  This article studies the control ideas of the optimal backstepping technique,
proposing an event-triggered optimal tracking control scheme for a class of
strict-feedback nonlinear systems with non-affine and nonlinear faults. A
simplified identifier-critic-actor framework is employed in the reinforcement
learning algorithm to achieve optimal control. The identifier estimates the
unknown dynamic functions, the critic evaluates the system performance, and the
actor implements control actions, enabling modeling and control of anonymous
systems for achieving optimal control performance. In this paper, a simplified
reinforcement learning algorithm is designed by deriving update rules from the
negative gradient of a simple positive function related to the
Hamilton-Jacobi-Bellman equation, and it also releases the stringent persistent
excitation condition. Then, a fault-tolerant control method is developed by
applying filtered signals for controller design. Additionally, to address
communication resource reduction, an event-triggered mechanism is employed for
designing the actual controller. Finally, the proposed scheme's feasibility is
validated through theoretical analysis and simulation.
