Genetic algorithm (GA) based PID (proportional integral derivative) controller has been proposed for tuning optimized PID parameters in a continuous stirred tank reactor (CSTR) process using a weighted combination of objective functions, namely, integral square error (ISE), integral absolute error (IAE), and integrated time absolute error (ITAE). Optimization of PID controller parameters is the key goal in chemical and biochemical industries. PID controllers have narrowed down the operating range of processes with dynamic nonlinearity. In our proposed work, globally optimized PID parameters tend to operate the CSTR process in its entire operating range to overcome the limitations of the linear PID controller. The simulation study reveals that the GA based PID controller tuned with fixed PID parameters provides satisfactory performance in terms of set point tracking and disturbance rejection. 1. Introduction PID controllers are still widely used in 90% of industries, since no other advanced control schemes such as model predictive control, internal model control (IMC), and sliding mode control (SMC) match the simplicity, clear functionality, applicability, and ease of use provided by this controller [1]. PID controller tuned at a particular operating point will not provide a satisfying response when there exists deviation in the process operating range [2]. Hence, soft computing based PID controller tuning is widely proposed by the researchers during the last few decades [3–7]. CSTR process exhibits typical nonlinear and time varying behaviour where control of the parameter reactor concentration with few computation steps is a challenge to the researchers. Therefore, it becomes essential to use powerful heuristic algorithms like GA to tune the PID parameters, thereby achieving good set point tracking and disturbance rejection control in the chemical system CSTR. Vinodha et al. have designed three control schemes for nonlinear CSTR process such as extraction of PID controller parameters based on artificial intelligence, model predictive controllers using the weighted sum of the output from local predictive controllers, and multiple model PID controllers [8]. The presence of parameter uncertainty in nonlinear systems like CSTR has been controlled by designing the nonlinear predictive adaptive controller (NPAC) and a nonlinear disturbance observer (NDO) in literature [9] and the proposed compensation method improves system tracking accuracy and robustness. Prakash and Senthil formulated a nonlinear observer based model predictive controller (NMPC) based on
C. Ou and W. Lin, “Comparison between PSO and GA for parameters optimization of PID controller,” in Proceedings of the IEEE International Conference on Mechatronics and Automation (ICMA '06), pp. 2471–2475, IEEE, Luoyang, China, June 2006.
M. Abachizadeh, M. R. H. Yazdi, and A. Y. Koma, “Optimal tuning of PID controllers using artificial bee colony algorithm,” in Proceedings of the Advanced Intelligent Mechatronics Conference, pp. 379–384, 2010.
A. Karimi, H. Eskandari, M. Sedighizadeh, A. Rezazadeh, and A. Pirayesh, “Optimal PID controller design for AVR system using new optimization algorithm,” International Journal on Technical and Physical Problems of Engineering, vol. 5, no. 15, pp. 123–128, 2013.
V. Rajinikanth and K. Latha, “Controller parameter optimization for nonlinear systems using enhanced bacteria foraging algorithm,” Applied Computational Intelligence and Soft Computing, vol. 2012, Article ID 214264, 12 pages, 2012.
M. Geetha, K. A. Balajee, and J. Jerome, “Optimal tuning of virtual feedback PID controller for a continuous stirred tank reactor (CSTR) using particle swarm optimization (PSO) algorithm,” in Proceedings of the 1st International Conference on Advances in Engineering, Science and Management (ICAESM '12), pp. 94–99, March 2012.
R. Vinodha, S. A. Lincoln, and J. Prakash, “Design and implementation of simple adaptive control schemes on simulated model of CSTR process,” International Journal of Modelling, Identification and Control, vol. 14, no. 3, pp. 159–169, 2011.
H. Man and C. Shao, “Nonlinear predictive adaptive controller for CSTR process,” Journal of Computational Information Systems, vol. 8, no. 22, pp. 9473–9479, 2012.
J. Prakash and R. Senthil, “Design of observer based nonlinear model predictive controller for a continuous stirred tank reactor,” Journal of Process Control, vol. 18, no. 5, pp. 504–514, 2008.
J. Prakash and K. Srinivasan, “Design of nonlinear PID controller and nonlinear model predictive controller for a continuous stirred tank reactor,” ISA Transactions, vol. 48, no. 3, pp. 273–282, 2009.
R. Senthil, K. Janarthanan, and J. Prakash, “Nonlinear state estimation using fuzzy Kalman filter,” Industrial and Engineering Chemistry Research, vol. 45, no. 25, pp. 8678–8688, 2006.
J. Yu and Y. Wu, “Global set-point tracking control for a class of non-linear systems and its application in continuously stirred tank reactor systems,” IET Control Theory and Applications, vol. 6, no. 12, pp. 1965–1971, 2012.
M. Delbari, K. Salahshoor, and B. Moshiri, “Adaptive generalized predictive control and model reference adaptive control for CSTR reactor,” in Proceedings of the IEEE International Conference on Intelligent Control and Information Processing (ICICIP '10), pp. 165–169, Dalian, China, August 2010.
E. G. Shopova and N. G. Vaklieva-Bancheva, “BASIC—a genetic algorithm for engineering problems solution,” Computers and Chemical Engineering, vol. 30, no. 8, pp. 1293–1309, 2006.
J. Zhang, J. Zhuang, H. Du, and S. Wang, “Self-organizing genetic algorithm based tuning of PID controllers,” Information Sciences, vol. 179, no. 7, pp. 1007–1018, 2009.
R. A. Krohling and J. P. Rey, “Design of optimal disturbance rejection PID controllers using genetic algorithms,” IEEE Transactions on Evolutionary Computation, vol. 5, no. 1, pp. 78–82, 2001.
S. M. G. Kumar, R. Jain, N. Anantharaman, V. Dharmalingam, and K. M. M. S. Begum, “Genetic algorithm based PID controller tuning for a model bioreactor,” Indian Chemical Engineer, Indian Institute of Chemical Engineers, vol. 50, no. 3, pp. 214–226, 2008.
A. Bagis, “Determination of the PID controller parameters by modified genetic algorithm for improved performance,” Journal of Information Science and Engineering, vol. 23, no. 5, pp. 1469–1480, 2007.
B. Nagaraj and N. Murugananth, “A comparative study of PID controller tuning using GA, EP, PSO and ACO,” in Proceedings of the IEEE International Conference on Communication Control and Computing Technologies (ICCCCT '10), pp. 305–313, IEEE, Ramanathapuram, India, October 2010.
W.-D. Chang, “Nonlinear CSTR control system design using an artificial bee colony algorithm,” Simulation Modelling Practice and Theory, vol. 31, pp. 1–9, 2013.
W. Wang and X. Jin, “An optimization tuning method of nonlinear non-minimum phase systems and its application to chemical process,” in Proceedings of the 26th Chinese Control and Decision Conference (CCDC '14), pp. 4929–4935, Changsha, China, June 2014.
A. Singh and V. Sharma, “Concentration control of CSTR through fractional order PID controller by using soft techniques,” in Proceedings of the 4th International Conference on Computing, Communications and Networking Technologies (ICCCNT '13), pp. 1–6, Tiruchengode, India, July 2013.
A. Y. Jaen-Cuellar, R. D. J. Romero-Troncoso, L. Morales-Velazquez, and R. A. Osornio-Rios, “PID-controller tuning optimization with genetic algorithms in servo systems,” International Journal of Advanced Robotic Systems, vol. 10, p. 324, 2013.
M. Pottman and D. E. Seborg, “Identification of non-linear processes using reciprocal multiquadric functions,” Journal of Process Control, vol. 2, no. 4, pp. 189–203, 1992.
K. F. Man, K. S. Tang, and S. Kwong, “Genetic algorithms: concepts and applications,” IEEE Transactions on Industrial Electronics, vol. 43, no. 5, pp. 519–534, 1996.