This Small Business Innovation Research (SBIR) Phase I project will design an adaptive critic based controller for use in optimizing control of complex large-scale, multi-goal systems which exhibit nonlinear dynamics. Currently implemented Adaptive Critic controllers often work well on small-scale problems, but convergence is slow on complex large-scale problems. In addition, the need to optimize performance with competing goals makes learning and convergence of adaptive critic controllers difficult. Phase I will demonstrate this adaptive critic controller on a typical but challenging problem: optimization of auto driving characteristics over a standard driving cycle. The controller will optimize throttle control, acceleration, deceleration, and braking so as to minimize energy expenditure over the course while also traversing the cycle in a minimum time span. This is similar to a long-haul truck driver searching for an optimal sequence of gas pedal and brake controls to go from New York to Los Angeles in minimum time and burning minimum fuel (and traveling through hills and mountains in between). Commercial applications of adaptive critic controllers of this kind are expected semiconductor manufacturing and flight control as well as in the automotive control problem.
