Neural-Network-Based Design of Optimal Controllers for Nonlinear Systems

A neural-network-based methodology for the design of optimal controllers for nonlinear systems is presented. The overall architecture consists of two neural networks. The first neural network is a cost-to-go function approximator (CTGA), which is trained to predict the cost to go from the present state of the system. The second neural network converges to an optimal controller as it is trained to minimize the output of the first network. The CTGA can be trained using available simulation or experimental data. Hence an explicit analytical model of the system is not required. The key to the success of the approach is giving the CTGA a special decentralized structure that makes its training relatively straightforward and its prediction quality carefully controlled. The specific structure eliminates many of the uncertainties often involved in using artificial neural networks for this type of application. Validity of the approach is illustrated for the optimal attitude control of a spacecraft with reaction wheels.