Cascade Control: Data-Driven Tuning Approach Based on Bayesian Optimization. Issue 2 (2020)

• Record Type: Journal Article
• Authors: Khosravi, Mohammad; Behrunani, Varsha; Smith, Roy S.; Rupenyan, Alisa; Lygeros, John
• Is Part Of: IFAC-PapersOnLine. Volume 53:Issue 2(2020)
• Page Start: 382
• Page End: 387
• Publication Date: 2020
• Subjects: PID tuning -- auto-tuning -- Gaussian process -- Bayesian optimization; Automatic control -- Periodicals; 629.805
• DOI: 10.1016/j.ifacol.2020.12.193
• Languages: English
• ISSNs: 2405-8963

Abstract

Abstract: Cascaded controller tuning is a multi-step iterative procedure that needs to be performed routinely upon maintenance and modification of mechanical systems. An automated data-driven method for cascaded controller tuning based on Bayesian optimization is proposed. The method is tested on a linear axis drive, modeled using a combination of first principles model and system identification. A custom cost function based on performance indicators derived from system data at different candidate configurations of controller parameters is modeled by a Gaussian process. It is further optimized by minimization of an acquisition function which serves as a sampling criterion to determine the subsequent candidate configuration for experimental trial and improvement of the cost model iteratively, until a minimum according to a termination criterion is found. This results in a data-efficient procedure that can be easily adapted to varying loads or mechanical modifications of the system. The method is further compared to several classical methods for auto-tuning, and demonstrates higher performance according to the defined data-driven performance indicators. The influence of the training data on a cost prior on the number of iterations required to reach optimum is studied, demonstrating the efficiency of the Bayesian optimization tuning method.