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

Self-improving Q-learning based controller for a class of dynamical processes

This paper presents how Q-learning algorithm can be applied as a general-purpose self-improving controller for use in industrial automation as a substitute for conventional PI controller implemented without proper tuning. Traditional Q-learning approach is redefined to better fit the applications in practical control loops, including new definition of the goal state by the closed loop reference trajectory and discretization of state space and accessible actions (manipulating variables). Properties of Q-learning algorithm are investigated in terms of practical applicability with a special emphasis on initializing of Q-matrix based only on preliminary PI tunings to ensure bumpless switching between existing controller and replacing Q-learning algorithm. A general approach for design of Q-matrix and learning policy is suggested and the concept is systematically validated by simulation in the application to control two examples of processes exhibiting first order dynamics and oscillatory second order dynamics. Results show that online learning using interaction with controlled process is possible and it ensures significant improvement in control performance compared to arbitrarily tuned PI controller

ADRC-Based Habituating Control of Double-Heater Heat Source

This paper deals with the proposition of improvement in the performance of a heat source by modification of its structure and by deriving a dedicated control system. Traditional heat sources consist of a single heater of high nominal power, but slow dynamics, that is regulated by a single closed loop control system. In this paper, an existing heater serially-connected with a supplemental heater with low nominal power but fast dynamics is proposed. A dedicated control system was derived with two active disturbance rejection controllers (ADRC) implemented in the habituating control structure. The proposed solution was validated using a virtual commissioning procedure where the heating system was simulated in the SIEMENS® Simit v10.3 industrial software, and ADRC controllers were implemented in SIEMENS® PLCSIM Advanced using dedicated library function blocks. The results showed the superiority of the proposed approach in comparison with the traditional single closed loop solution. The proposed dedicated habituating control system provided better robustness to the changes in dynamics of a heat source and to the measurement noise. At the same time, it will ensure lower (or in some cases comparable) values of popular closed loop performance indices