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Proportional Integral Derivative controller is one of the simplest and most commonly used ones in various industries for control applications. Despite significant advancements in control technology, over 80% of industrial control loops are incorporated with PID controller. Though it is widely accepted, it should be properly tuned to meet desired behavior. Extensive work of Ziegler and Nichols [1] is the breakthrough in tuning methodology and Cohen Coon, Lambda tuning, and Chen Hrown Reswick (CHR) methods are a few of the other tuning methods reported in the literature [2]. Existing tuning methods are classified [3] based on nature and usage as analytical methods, Heuristic method, Frequency response method, Optimization method and Adaptive tuning methods. Among those, optimization method is widely utilized around the globe as it is conceptually simple and widely accepted one for tuning PID controller [2]. In this method, controller parameters are adjusted based on the chosen objective function chiefly integral performance measures. A classical optimization technique namely gradient method is often used to find optimal values. The shortcoming of gradient descent methods is sensitivity to the selection of initial values and their tendency to lock into a local extreme point [4]. Evolutionary Computation techniques are proposed to tune the PID controller by taking all non-linearity and additional process characteristics into account [5], [6]. Genetic Algorithm (GA) has the capability to solve nonlinear and complex optimization problems [7]. Porter and Jones proposed a GA-based simple and generic method of tuning digital PID controller [8].

Tuning of Multivariable Decentralized PID Controller Using State Transition Algorithm