An enhanced bacteria foraging optimization (EBFO) algorithm-based Proportional + integral + derivative (PID) controller tuning is proposed for a class of nonlinear process models. The EBFO algorithm is a modified form of standard BFO algorithm. A multiobjective performance index is considered to guide the EBFO algorithm for discovering the best possible value of controller parameters. The efficiency of the proposed scheme has been validated through a comparative study with classical BFO, adaptive BFO, PSO, and GA based controller tuning methods proposed in the literature. The proposed algorithm is tested in real time on a nonlinear spherical tank system. The real-time results show that, EBFO tuned PID controller gives a smooth response for setpoint tracking performance. 1. Introduction In control literature, despite of significant developments in advanced process control schemes such as model predictive control (MPC), internal model control (IMC), and sliding mode control (SMC), PID controllers are still widely used in industrial control system where reference tracking and disturbance rejection are a major task. The key merits of the PID controllers over the advanced control techniques are as follows: (i) available in a variety of structures such as series, parallel, and so forth; (ii) provides an optimal and robust performance for a variety of processes; (iii) supports online/offline tuning and retuning based on the performance requirement of the process under control (iv) simple structure and it can be easily implementable in analog or digital form; (v) along with the basic and the modified structures, it also supports the one degree of freedom (1DOF), 2DOF, and 3DOF controller structures. Most of the real-time chemical process loops such as continuous stirred tank reactor (CSTR), biochemical reactor, spherical tank system, and conical tank system are nonlinear in nature. These nonlinear processes can be modelled as linear processes (stable or unstable process model with a delay time) around the operating region. The linear model is then efficiently controlled by employing a PID controller. The precision and performance of the PID controller mainly rely on three controller parameters such as proportional gain ( ), integral gain ( ), and derivative gain ( ). In recent years, a number of tuning rules have been proposed for the PID controllers to enhance the performance of the process to be controlled. Maher and Samir [1] have discussed the robust stability criterion for a class of unstable systems under model uncertainty. Vijayan and Panda [2] have
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