This paper describes a gain scheduling strategy that can be used in conjunction with explicit Model “Predictive Control (MPC). Traditionally, explicit MPC is not reconfigurable to online model changes. To handle off-nominal plant conditions, a common practice is to design multiple explicit MPC’s which are each valid locally around their respective operating points. This inevitably requires large amounts of memory to store the explicit MPC’s and implementation of switching logic and observers. The gain scheduling strategy presented in this paper bypasses the need to store multiple explicit MPC’s. This is done by multiplying the control signal obtained from the nominal explicit MPC by a gain scheduling matrix such that the plant at off-nominal operating conditions is approximately matched to the nominal plant. This is further accomplished in a manner such that the original control constraints are satisfied. The gain scheduling strategy is demonstrated in simulations on a nonlinear diesel air path model over the New European Drive Cycle (NEDC).
- Dynamic Systems and Control Division
A Low Complexity Gain Scheduling Strategy for Explicit Model Predictive Control of a Diesel Air Path
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Huang, M, Butts, K, & Kolmanovsky, I. "A Low Complexity Gain Scheduling Strategy for Explicit Model Predictive Control of a Diesel Air Path." Proceedings of the ASME 2015 Dynamic Systems and Control Conference. Volume 1: Adaptive and Intelligent Systems Control; Advances in Control Design Methods; Advances in Non-Linear and Optimal Control; Advances in Robotics; Advances in Wind Energy Systems; Aerospace Applications; Aerospace Power Optimization; Assistive Robotics; Automotive 2: Hybrid Electric Vehicles; Automotive 3: Internal Combustion Engines; Automotive Engine Control; Battery Management; Bio Engineering Applications; Biomed and Neural Systems; Connected Vehicles; Control of Robotic Systems. Columbus, Ohio, USA. October 28–30, 2015. V001T12A004. ASME. https://doi.org/10.1115/DSCC2015-9754
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