On-line multiobjective automatic control system generation by evolutionary algorithms

Evolutionary algorithms are applied to the on- line generation of servo-motor control systems. In this paper, the evolving population of controllers is evaluated at run-time via hardware in the loop, rather than on a simulated model. Disturbances are also introduced at run-time in order to pro- duce robust performance. Multiobjective optimisation of both PI and Fuzzy Logic controllers is considered. Finally an on-line implementation of Genetic Programming is presented based around the Simulink standard blockset. The on-line designed controllers are shown to be robust to both system noise and ex- ternal disturbances while still demonstrating excellent steady- state and dvnamic characteristics

Real-time evolution of an embedded controller for an autonomous helicopter

In this paper we evolve the parameters of a proportional, integral, and derivative (PID) controller for an unstable, complex and nonlinear system. The individuals of the applied genetic algorithm (GA) are evaluated on the actual system rather than on a simulation of it, thus avoiding the ldquoreality gaprdquo. This makes implicit a formal model identification for the implementation of a simulator. This also calls for the GA to be approached in an unusual way, where we need to consider new aspects not normally present in the usual situations using an unnaturally consistent simulator for fitness evaluation. Although elitism is used in the GAs, no monotonic increase in fitness is exhibited by the algorithm. Instead, we show that the GApsilas individuals converge towards more robust solutions