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
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