Evolutionary learning of fuzzy logic controllers over a region of initial states

In this paper we discuss two evolutionary learning methods to learn a fuzzy knowledge base which is required to control a system not just from a single initial configuration (open loop control), but over a region of configuration states (closed loop control). It is applied to the control of three different systems, the control of an inverted pendulum (a nonlinear dynamic model), control of a simulated point mass, mobile robot to a fixed target in a two robot collision-avoidance problem, and control of a simulated point mass robot to a target moving with constant speed in a fixed direction (kinematic models). The first method involves amalgamation through averaging of fuzzy knowledge bases learnt across a grid of initial configurations representative of states in the region. The second method addresses this learning directly without having to acquire the knowledge via amalgamation by incorporating operators which would pass fuzzy logic knowledge in a local region from generation to generation, at the same time as accumulating this knowledge across the entire region.