Neural network solution for suboptimal control of non-holonomic chained form system
Abbreviated Journal Title
Trans. Inst. Meas. Control
Constrained input systems; finite-horizon optimal control; Hamilton-Jacobi-Bellman; neural network control; non-holonomic systems; CONTINUOUS-TIME SYSTEMS; SATURATING ACTUATORS; NONLINEAR-SYSTEMS; FEEDBACK-CONTROL; LINEAR-SYSTEMS; Automation & Control Systems; Instruments & Instrumentation
In this paper, we develop fixed-final time nearly optimal control laws for a class of non-holonomic chained form systems by using neural networks to approximately solve a Hamilton-Jacobi-Bellman equation. A certain time-folding method is applied to recover uniform complete controllability for the chained form system. This method requires an innovative design of a certain dynamic control component. Using this time-folding method, the chained form system is mapped into a controllable linear system for which controllers can systematically be designed to ensure exponential or asymptotic stability as well as nearly optimal performance. The result is a neural network feedback controller that has time-varying coefficients found by a priori offline tuning. The results of this paper are demonstrated in an example.
Transactions of the Institute of Measurement and Control
"Neural network solution for suboptimal control of non-holonomic chained form system" (2009). Faculty Bibliography 2000s. 1420.