Asymmetric relay autotuning - Practical features for industrial use

https://portal.research.lu.se/portal/files/38031798/berner_cep18.pdf

An experimental comparison of PID autotuners

The advent of Industry 4.0 (I4.0) has pushed technology beyond its physical limits, making the process prone to errors and poorer performance. Whether it is about smart manufacturing where mass customization is envisaged, or collaborative human–robot engineering systems, the pyramid of process operation has changed to a matrix form and control is the backbone of all process elements. The paper gives a concise guideline as to how, when, where, and what to apply when it comes to choosing the most suitable control strategy as a function of multi-parameter objective optimization. Both proportional-integral-derivative (PID) and model predictive control (MPC) control are addressed in this context.

https://www.mdpi.com/2411-5134/4/1/10/pdf

The 5W’s for Control as Part of Industry 4.0: Why, What, Where, Who, and When—A PID and MPC Control Perspective

The one-third rule for PI controller tuning

Many industrial processes utilize proportional-integral-derivative (PID) controllers due to their practicality and often satisfactory performance. The proper controller parameters depend highly on the operational conditions and process uncertainties. This dependence brings the necessity of frequent tuning for real-time control problems due to process drifts and operational condition changes. This study combines the recent developments in computer sciences and control theory to address the tuning problem. It formulates the PID tuning problem as a reinforcement learning task with constraints. The proposed scheme identifies an initial approximate step-response model and lets the agent learn dynamics off-line from the model with minimal effort. After achieving a satisfactory training performance on the model, the agent is fine-tuned on-line on the actual process to adapt to the real dynamics, thereby minimizing the training time on the real process and avoiding unnecessary wear, which can be beneficial for industrial applications. This sample efficient method is applied to a pilot-scale multi-modal tank system. The performance of the method is demonstrated by setpoint tracking and disturbance regulatory experiments.

Reinforcement Learning Approach to Autonomous PID Tuning

http://www.nt.ntnu.no/users/skoge/prost/proceedings/ifac2014/media/files/1591.pdf

Towards a New Generation of Relay Autotuners

The relay autotuner provides a simple way of finding PID controllers of sufficient performance. By using an asymmetric relay function the excitation of the process is improved. This gives better models, and hence a better tuning, without increasing the time consumption or complexity of the experiment. Some processes demand more accurate modeling and tuning to obtain controllers of sufficient performance. These processes can be singled out by their normalized time delays and be subject to further modeling efforts. The autotuner proposed in this paper provides a simple way of finding the normalized time delay from the experiment, and uses it for model and controller selection. The autotuner has been implemented and evaluated both in a simulation environment and by industrial experiments.

/pdf/improved-relay-autotuning-using-normalized-time-delay-24bf69dyq9.pdf

Improved relay autotuning using normalized time delay

/pdf/autotuner-identification-of-tito-systems-using-a-single-4g5f4uhddp.pdf

Josefin Berner

Papers

Asymmetric relay autotuning - Practical features for industrial use

An experimental comparison of PID autotuners

Towards a New Generation of Relay Autotuners

Improved relay autotuning using normalized time delay

Autotuner identification of TITO systems using a single relay feedback experiment