A Neural Circuit Model of Adaptive Robust Tracking Control for Continuous-Time Nonlinear Systems

A neural circuit model of adaptive robust tracking control for continuous-time unknown nonlinear dynamic systems is presented. A first-order differential equation with variable structure and an output equation are used to describe the circuit. A corresponding functional block-diagram of the circuit is given. There is discussed a possibility of software and hardware implementation of the circuit. Stability and convergence analysis of the circuit states is performed based on Lyapunov second method. An upper bound is estimated for the time required to reach a steady state by the circuit. The circuit operation with disturbances of its nonlinearity is discussed. The circuit has simple architecture, it can provide bounded tracking error and finite controlled convergence time to steady states and does not need off-line learning phase. Computer simulations of the circuit operation confirming the theoretical derivations and illustrating the performance of the circuit are provided.