Study On GA-BPNN Based PID Adjustor Real-Time Adjust Speed

Genetic algorithm (GA) and Back Propagation neural network (BPNN) have a strong capacity of optimizing parameters. GA is able to optimize parameters in wide range, BPNN have good converge effect in part, but it is prone to run into apices in part, so, In this paper, combining them to optimize the parameters of PID Adjustor. In order to make PID Adjustor become better stability, more quick rate and higher precision.Because the convergent rate of GA and BPNN is very slow, PID Adjustor based on GA-BPNN can't be widely used in real-time control system. In order to conquer the limitation, in the paper, the late-model control strategy is presented, which combines Off-line Roughly Adjust and On-line Precisely Adjust. In real-time adjust speed system, The Brush -less Direct Current Motor (BLDCM) is relatively fastness, so the mostly work is done in Off-line state, and PID Adjustor Based on trained BPNN is used to real-time control BLDCM. Because BPNN have capabilities of adaptive and memory, the control system have characters of adaptive and fast respond.For the intelligent algorithm: GA and BPNN optimize parameters of PID Adjustor respectively, which is simulated and validated. And in the paper, it use GA to optimize the initial values of the power of BPNN. The late-model control strategy is also simulated and validated. Based on TMS320F240 EVM board tapped by WINTECH Co. The intelligent algorithm and the late-model control strategy is partly hardware simulation. These simulations get very good control effects, which shows that the intelligent algorithm and the late-model control strategy are feasible and reliable.