Proportional integral derivative (PID) controller tuning is an area of interest for researchers in many disciplines of science and engineering. This paper presents a new algorithm for PID controller tuning based on a combination of the foraging behavior of E coli bacteria foraging and Particle Swarm Optimization (PSO). The E coli algorithm depends on random search directions which may lead to delay in reaching the global solution. The PSO algorithm may lead to possible entrapment in local minimum solutions. This paper proposed a new algorithm Bacteria Foraging oriented by PSO (BF-PSO). The new algorithm is proposed to combines both algorithms' advantages in order to get better optimization values. The proposed algorithm is applied to the problem of PID controller tuning and is compared with conveniently Bacterial Foraging algorithm and Particle swarm optimization.

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In the two-coupled distillation column process, keeping the tray temperatures within a specified range around their steady state values assures the specifications for top and bottom product purity. The two-coupled distillation column is a 4 Input/4 Output process. Normally, control engineers decouple the process into four independent loops. They assign a PID controller to control each loop. Tuning of conventional PID controllers is very difficult when the process is subject to external unknown factors. The paper proposes a Brain Emotional Learning Based Intelligent Controller (BELBIC) to replace conventional PID controllers. Moreover, the values of BELBIC and PID gains are optimized using a particle swarm optimization (PSO) technique with minimization of Integral Square Error (ISE) for all loops. The paper compares the performance of the proposed PSO-BELBICs with that of conventional PSO-PID controllers. PSO-BELBICs prove their usefulness in improving time domain behavior with keeping robustness for all loops.

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