Stability analysis for neural control systems is a difficult and important open research question. The problem is difficult because neural control systems are inherently non-linear and may involve large scale plants with unknown parameters and structure. The problem is important because practical application of neural control demands that the closed-loop system operate in a stable manner. In this project several ideas from different control system fields are integrated to generate stability arguments for neural control systems. The concepts of Lyapunov functions from adaptive control systems, performance measure minimization and dynamic programming from optimal control and the adaptive critic from reinforcement learning are combined to formulate a methodology for stability and convergence rate analysis for neural controller.
