Application of Machine Learning to Performance Assessment for a class of PID-based Control Systems

In this paper, a novel machine learning derived control performance assesment (CPA) classification system is proposed. It is dedicated for a class of PID-based control loops with processes exhibiting second order plus delay time (SOPDT) dynamical properties. The proposed concept is based on deriving and combining a number of different, diverse control performance indices (CPIs) that separately do not provide sufficient information about the control performance. However, when combined together and used as discriminative features of the assessed control system, they can provide consistent and accurate CPA information. This concept is discussed in terms of the introduced extended set of CPIs, comprehensive performance assessment of different machine learning based classification methods and practical applicability of the suggested solution. The latter is shown and verified by practical application of the proposed approach to a CPA system for a laboratory heat exchange and ditribution setup.Comment: Submitted to IEEE Transactions on Industrial Electronic

A metamorphic controller for plant control system design

One of the major problems in the design of industrial control systems is the selection and parameterization of the control algorithm. In practice, the most common solution is the PI (proportional-integral) controller, which is simple to implement, but is not always the best control strategy. The use of more advanced controllers may result in a better efficiency of the control system. However, the implementation of advanced control algorithms is more time-consuming and requires specialized knowledge from control engineers. To overcome these problems and to support control engineers at the controller design stage, the paper describes a tool, i.e., a metamorphic controller with extended functionality, for selection and implementation of the most suitable control algorithm. In comparison to existing solutions, the main advantage of the metamorphic controller is its possibility of changing the control algorithm. In turn, the candidate algorithms can be tested through simulations and the total time needed to perform all simulations can be less than a few minutes, which is less than or comparable to the design time in the concurrent design approach. Moreover, the use of well-known tuning procedures, makes the system easy to understand and operate even by inexperienced control engineers. The application was implemented in the real industrial programmable logic controller (PLC) and tested with linear and nonlinear virtual plants. The obtained simulation results confirm that the change of the control algorithm allows the control objectives to be achieved at lower costs and in less time

A metamorphic controller for plant control system design

One of the major problems in the design of industrial control systems is the selection and parameterization of the control algorithm. In practice, the most common solution is the PI (proportional-integral) controller, which is simple to implement, but is not always the best control strategy. The use of more advanced controllers may result in a better efficiency of the control system. However, the implementation of advanced control algorithms is more time-consuming and requires specialized knowledge from control engineers. To overcome these problems and to support control engineers at the controller design stage, the paper describes a tool, i.e., a metamorphic controller with extended functionality, for selection and implementation of the most suitable control algorithm. In comparison to existing solutions, the main advantage of the metamorphic controller is its possibility of changing the control algorithm. In turn, the candidate algorithms can be tested through simulations and the total time needed to perform all simulations can be less than a few minutes, which is less than or comparable to the design time in the concurrent design approach. Moreover, the use of well-known tuning procedures, makes the system easy to understand and operate even by inexperienced control engineers. The application was implemented in the real industrial programmable logic controller (PLC) and tested with linear and nonlinear virtual plants. The obtained simulation results confirm that the change of the control algorithm allows the control objectives to be achieved at lower costs and in less time