Abstract
Recent studies have suggested that Model Predictive Controllers (MPC) could benefit single-input single-output (SISO) systems. However, some key factors inhibiting MPC implementation are the efforts associated with plant tests and the need for expert knowledge on the design and tuning. Moreover, plant tests are time-consuming; they interfere with process operations and can even lead to instability in extreme cases. This paper mitigates these problems by generating a model predictive controller starting with a pre-tuned PI controller and routine plant data. This paper presents the studies of relevant methodologies, which was then harmonized into a concise algorithm. A genetic algorithm was used with routine plant data to obtain the MPC parameters. These parameters are used to design an MPC that gives similar performance to a pre-tuned PI Controller. Monte Carlo simulations were used to demonstrate the method's viability. Results show that the respective MPC-parameters' mean values converges to their actual values. Hence, this is critical in developing MPC from routine plant data.