This paper studies the infinite time horizon optimal control problem for continuous-time nonlinear systems. A completely model-free approximate optimal control design method is proposed, which only makes use of the real-time measured data from trajectories instead of a dynamical model of the system. This approach is based on the actor-critic structure, where the weights of the critic neural network and the actor neural network are updated sequentially by the method of weighted residuals. It should be noted that an external input is introduced to replace the input-to-state dynamics to improve the control policy. Moreover, strict proof of convergence to the optimal solution along with the stability of the closed-loop system is given. Finally, a numerical example is given to show the efficiency of the method.

This paper studies H∞ control for networked control systems with packet loss. In networked control systems, packet loss is one of major weakness because the control performance deteriorates due to packet loss. H∞ control, which is one of robust control, can design a controller to reduce the influence of disturbances acting on the controlled object. This paper proposes an H∞ control design that considers packet loss as a disturbance. Numerical examples show that the proposed H∞ control design can more effectively reduce control performance deterioration due to packet loss than the conventional H∞ control design. In addition, this paper provides control performance comparisons of H∞ control and Linear Quadratic (LQ) control. Numerical examples show that the control performance of the proposed H∞ control design is better than that of the LQ control design.