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Received: 17 December 2019 Accepted: 29 February 2020 The optimization problem for multi-variable industrial high-efficiency control systems is to find the optimal parameters that could minimize the errors. In order to get such a stable control system, various control tuning methods were proposed by many researchers, but still, it is a challenge to get an effective controlled system. In this work, a method is proposed in order to attain a stable control system, called Error Recursion Reduction Computational (ERRC) technique. Two processes, level, and flow are considered; their respective process models are identified and validated. The performance of the proposed technique has verified by implementing real-time transducers' interfaced experimental process. Results are compared with the conventional PID tuning technique and by optimization algorithms. The proposed experimental results show that better closed-loop performance can be achieved than other tuning techniques.

Error Recursion Reduction Computational Technique Based Control System Design for a Multivariable Process