Tuning of a PID Controller Using Cultural Artificial Bee Colony Algorithm
This research is aimed at developing a cultural-algorithm based artificial bee colony algorithm (CABCA) for improved Proportional Integral Derivative (PID) controller parameters tuning. The normative and situational knowledge inherent in cultural algorithm were utilized to guide the step size as well as the direction of evolution of Artificial Bee Colony (ABC) at different configurations, in order to combat the disparity between exploration and exploitation associated with the standard ABC, which results to poor convergence and optimization efficiency. Consequently, four variants of CABCA (CABCA(Ns), CABCA(Sd), CABCA(Ns+Sd) and CABCA(Ns+Nd)) were accomplished in MATLAB R2015a using different configurations of cultural knowledge. A total of 20 standard applied mathematical optimization test functions (Ackley, Michalewicz, Quartic, Sphere etc) were employed to evaluate the performance of each CABCA variant. The results indicate that CABCA(Ns) performed best in 4 test functions (20%), CABCA(Ns+Nd) also in 4 functions (20%), while CABCA(Sd) and CABCA(Ns+Sd) performed best in 3 test cases (15%) and 2 test cases (10%) respectively. On the remaining 7 test functions (35%) of their results were similar. The CABCA(Ns) was chosen as the best performed variant based on the success ratio, which is the number of successful runs that found the solution. Hence, CABCA(Ns) was used to obtain the optimal parameters and of a PID controller which was employed in the speed control of a DC motor. The DC motor attained steady-state in 0.4178s with the CABCA-based PID controller as against 0.6778s and 2.2057s obtained using standard ABC and Ziegler-Nichols (Z-N) tuned PID controllers, respectively.