Neural-genetic control algorithm of robots

The paper deals with a soft computing state control method for multi input - multi output (MIMO) non-linear dynamic model of a robot. Soft methods based on neural networks and genetic algorithms have proven their effectiveness for this application. They are based on quite simple principles, but take advantage of their mathematical nature: non-linear iterative computation solutions. One way of controlling such nonlinear systems is to use of neural networks as effective controllers. In this paper a new methodology is proposed, where neural controller structure and parameters are computed by a genetic algorithm (GA). The proposed approach is represented by a direct neural controller using a multilayer perceptron (MLP) network in the feedback control loop. The training method using GA allows finding optimal adjustment of neural network weights so that high performance is achieved. The proposed control method is realized in Matlab/Simulink and demonstrated on a typical non-linear system with two inputs and two outputs (two-link robot).