Automatic Tuning of MPC using Genetic Algorithm with Historic Process Data

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.

I. Introduction

Control is a critical component for the development of industry 4.0. However, there is the argument that the current approach to control system design is not flexible enough to meet the needs for industry 4.0 [1]. A recent survey identified PID, MPC and System identification as the top three most successful industrial control technologies [2]. About 90 to 95 % of all industrial processes are controlled using PI/PID Controllers [3], [4]. However, in some situations, PID control is not the most suitable for regulating certain processes such as dead-time dominant processes [5]-[7]. Notwithstanding, PID remains the default controller considered by practitioners for most applications. The implementation of PID on non-suitable processes has resulted in poor performance in a high percentage of industrial control loops [3], [7]. There is, therefore, often the need to improve the performance of such low performing loops. For these loops, an obvious solution is to replace the existing PID controllers with more suitable alternatives.