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An Adaptive PID Controller Based on Bayesian Theory

[+] Author Affiliations
S. Andrew Gadsden

University of Guelph, Guelph, ON, Canada

Paper No. DSCC2017-5340, pp. V002T12A005; 7 pages
  • ASME 2017 Dynamic Systems and Control Conference
  • Volume 2: Mechatronics; Estimation and Identification; Uncertain Systems and Robustness; Path Planning and Motion Control; Tracking Control Systems; Multi-Agent and Networked Systems; Manufacturing; Intelligent Transportation and Vehicles; Sensors and Actuators; Diagnostics and Detection; Unmanned, Ground and Surface Robotics; Motion and Vibration Control Applications
  • Tysons, Virginia, USA, October 11–13, 2017
  • Conference Sponsors: Dynamic Systems and Control Division
  • ISBN: 978-0-7918-5828-8
  • Copyright © 2017 by ASME


One of the most popular trajectory-tracking controllers used in industry is the PID controller. The PID controller utilizes three types of gains and the tracking error in order to provide a control gain to a system. The PID gains may be tuned manually or using a number of different techniques. Under most operating conditions, only one set of PID gains are used. However, techniques exist to compensate for dynamic systems such as gain scheduling or basic time-varying functions. In this paper, an adaptive PID controller is presented based on Bayesian theory. The interacting multiple model (IMM) method, which utilizes Bayes’ theorem and likelihood functions, is implemented on the PID controller to present an adaptive control strategy. The strategy is applied to a simulated electromechanical system, and the results of the proposed controller are compared with the standard PID method. Future work is also considered.

Copyright © 2017 by ASME



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