<span class="word">Adaptive <span class="word"><span class="changedDisabled">Actor-<span class="word"><span class="changedDisabled">Critic <span class="word"><span class="changedDisabled">Optimal <span class="w

This study develops an adaptive optimal tracking control law using neural network (NN)-based reinforcement learning (RL) for high-order partially unknown nonlinear systems. By designing a cost function associated with the sliding mode surface (SMS), the original tracking control problem is equivalently transformed into solving the optimal control problem related to the tracking Hamilton-Jacobi-Bellman (HJB) equation. Since the analytical solution of the HJB equation is generally intractable, we employ a policy iteration algorithm derived from the HJB equation, where both the partial derivative of the optimal tracking cost function and the optimal control law are approximated by NNs. The proposed RL framework achieves simplification through actor-critic training laws derived under the condition that a simple function is zero. Finally, two simulative examples are provided to demonstrate the effectiveness and advantages of the proposed adaptive optimal tracking control method.