BELBICBrain Emotional Learning Based Intelligent Controller
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The BSO algorithm parameters' values which are utilized to implement an optimized decoupling network as well as an optimized BELBIC are summarized in Table 1.
The BELBIC designed using the BSO algorithm showed a remarkable improvement in the transient and steady state errors of the last three control loops compared with the controller designed utilizing the PSO technique.
Barkhordari, "Designing PID and BELBIC controllers in path tracking problem," International Journal of Computers, Communications and Control, vol.
Caption: Figure 4: Control system configuration using BELBIC.
Caption: Figure 12: Comparison between the step response of the designed BELBIC and that of the PID controller in the presence of disturbance at t = 500 seconds.
9 BSO for BELBIC Parameter implementation Number of bacteria in the population 50 Dimension of search space 24 Maximum number of swim length 4 Maximum number of chemotactic steps 20 Number of reproductive steps 2 Number of elimination dispersal events 2 Elimination dispersal probability 0.
The Bacterial Swarm Optimization (BSO) algorithm is utilized regarding the parameterization of PID controllers with the same policies as BELBICs.
The resulting best gains' values for different BELBICs that minimize the summation of the integral time-weighted squared errors (ITSEs) for different decoupled loops are presented in Table 3.