IEEE International Conference on Advances in Engineering & Technology Research (ICAETR - 2014), August 01-02, 2014, Dr. Virendra Swarup Group of Institutions, Unnao, India 978-1-4799-6393-5/14/$31.00 ©2014 IEEE Optimal Tuning of DC Motor Via Simulated Annealing Meenakshi Kishnani Shubham Pareek Dr. Rajeev Gupta Department of ECE (UCE) Department of ECE (UCE) Department of ECE (UCE) RTU, Kota(Rajasthan),India RTU, Kota(Rajasthan),India RTU, Kota(Rajasthan),India Abstract— Despite the popularity, the tuning of PID controller is a difficult task to attain by researchers and plant operators. Several conventional methods had been proposed to optimize the PID controller, yet, the results were highly unsatisfactory in terms of large overshoot and big surge, thus came the requirement for more convergent algorithm and therefore modern heuristic approach such as Simulated Annealing is employed to improve the capability of traditional techniques. In this paper, a simulated annealing PID controller is designed using IAE, ISE, ITAE , ITSE and MSE error criteria for stable linear time invariant continuous system. The gain parameters Kp Ki and Kd are tuned and applied to PID controller. A comparison of system performance is observed for Ziegler- Nicholas PID and SA-PID. Index Terms—PID controller, Simulated Annealing, IAE, ISE, ITAE, ITSE, MSE, DC MOTOR I. INTRODUCTION During the past decades, process control techniques in the industry have made great progress. Numerous control methods have been proposed such as: process control, adaptive control, neural control and fuzzy control have been studied. Still PID controller remain to be the main component in industrial control systems included in various forms such as: embedded controllers, programmable logic controllers and distributed control systems. These controllers are popular among control engineers because these are simple in structure, reliable in operation, cost effective and robust in performance within a wide range of operating conditions. Van Overschee and De Moor [2] report that 80% of PID type controllers in the industry are poorly or less optimally tuned. They have stated that 30% of the PID loops operate in the manual mode and 25% of PID loops actually operate under default factory settings. Over the years many strategies have been proposed to determine the optimum setting of PID parameters. PID controller can be tuned with conventional and intelligent methods. Conventional methods such as Ziegler and Nichols[3] and Simplex method can tune the optimal PID parameters for only linear and stable systems. Moreover they tend to produce big surge and large overshoot. The main drawback of this tuning method is that it is limited merely to certain operational zones and has an unsatisfactory design robustness property. Intelligent methods include meta- heuristic algorithms, fuzzy logic etc. This paper proposes to tune the parameters of PID with one of the intelligent algorithms named as Simulated Annealing algorithm.[4] Simulated Annealing was introduced by Kirkpatrick et al in 1982.it is a technique to solve combinatorial optimization problems by minimizing the functions of many variables.[5]using the cooling schedules to select the optimal parameters, this method repeatedly generates, judges and accepts/rejects the control parameters .[6]Since the simulated annealing selects the control parameters in its own evolutionary process and does not require a rule of thumb , has diffused into many areas of application. Simulated Annealing is inspired by an analogy to annealing in solids. The algorithm simulates the cooling process by lowering the temperature of the system(cooling schedule) until it converges to a stable or frozen state. SA’s major advantage over other methods is its ability to avoid getting trapped at local minima. The algorithm employs a random search, which not only accepts changes that decrease objective function, but also some changes that increase it with some probability. PID controller is designed for the DC motor plant with its parameters being optimized by the Simulated Algorithm. The objective of this paper is to show that by using SA, an optimization can be achieved .And optimized system response for five fitness functions i.e. IAE, ISE, ITAE,ITSE and MSE is compared in this paper.