Time-optimal control for motors via neural networks

신경회로망을 이용한 모터의 시간최적 제어

A time-optimal control law for quick, strongly nonlinear systems has been developed and demonstrated. This procedure involves the utilization of neural networks as state feedback controllers that learn the time-optimal control actions by means of an iterative minimization of both the final time and the final state error for the known and unknown systems with constrained inputs and/or states. The nature of neural networks as a parallel processor would circumvent the problem of "curse of dimensionality". The control law has been demonstrated for a velocity input type motor identified by a genetic algorithm called GENOCOP.