This paper aims at developing a robust gain-scheduled proportional-integral-derivative (PID) control design method for a linear-parameter-varying (LPV) system. It is recognized in the literature that robust fixed-order controller design can be formulated as a feasibility problem of a bilinear matrix inequality (BMI) constraint. Unfortunately, the search for a feasible solution of a BMI constraint is a NP hard problem in general. A common way to solve this dilemma is to apply a linearization method, such as variable change method or congruence transformation, to transform the BMI into LMI. The applicability of the linearization method depends on the specific structure of the problem at hand and cannot be generalized. This paper formulates the gain-scheduled PID controller design as a feasibility problem of a quadratic matrix inequality (QMI) constraint, which covers the BMI constraint as a special case. An augmentation of the newly developed sequential LMI optimization method is proposed to search for a feasible solution of a QMI constraint iteratively. In the application part, a vehicle lateral control problem is presented to demonstrate the applicability of the proposed algorithm to a real-world output feedback control design.
|Original language||English (US)|
|Title of host publication||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|
|Publisher||American Society of Mechanical Engineers|
|State||Published - 2017|
|Event||ASME 2017 Dynamic Systems and Control Conference, DSCC 2017 - Tysons, United States|
Duration: Oct 11 2017 → Oct 13 2017
|Name||ASME 2017 Dynamic Systems and Control Conference, DSCC 2017|
|Other||ASME 2017 Dynamic Systems and Control Conference, DSCC 2017|
|Period||10/11/17 → 10/13/17|
Bibliographical noteFunding Information:
This work was supported by funding from the National Science Foundation under Grant CMMI 1562006.