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The present research work presents a novel control scheme for tuning PID controllers using Internal Model control with the filter time constant optimized using Bee colony Optimization technique. PID controllers are used widely in Industrial Processes. Tuning of PID controllers is accomplished using Internal Model control scheme. IMC includes tuning of filter constant λ. Compromise is made in selecting the filter constant λ since an increased value of λ results in a sluggish response whereas decreased value of filter constant leads in an aggressive action. In the present work, an attempt has been made to optimize the value of the λ by Bee colony optimization technique. Simulation results show the validity of the proposed scheme for the PID controller tuning. [ABSTRACT FROM AUTHOR]

This paper presents the application of a neural fuzzy inference method to the field of control systems using the internal model control paradigm (IMC). Through a transductive reasoning system, a neuro-fuzzy inference system enables local models to be created for each input/output set in the system at issue. These local models are created for modeling the direct and inverse dynamics of the process. The models are then applied according to IMC paradigm. In order to demonstrate the benefits of this technique for control systems, it is applied for networked cutting force control in a high-performance drilling process. A comparative study between a well-established neuro-fuzzy technique and the suggested method is performed. [ABSTRACT FROM AUTHOR]