Genetic adaptive control for an inverted wedge

The inverted wedge is a planar robot with two degrees of freedom and a single control input (i.e., it is “underactuated”). The goal is to develop a digital controller that can balance the wedge in the inverted position by shifting a weight on the top of the wedge. Because it is underactuated and has complicated nonlinear dynamics, the inverted wedge is a good testbed for the development of nonconventional advanced control techniques and comparative analysis between control methods. We begin with the development of a nonlinear state feedback controller and direct and adaptive fuzzy controllers, that we will later use as a baseline comparison to show what type of performance is possible for this testbed. Then we develop an adaptive control routine based on the genetic algorithm (GA). In particular, we will show that a GA can be used online in real-time to produce a particularly effective adaptive control method and this is the main contribution of this work. Computational and real-time implementation issues will be discussed and the genetic adaptive strategy will be compared with the state feedback and fuzzy control methods