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Technical Brief

A Dual-Mode Model Predictive Control Algorithm Trajectory Tracking in Discrete-Time Nonlinear Dynamic Systems

[+] Author and Article Information
Asad A. Ul Haq

Department of Mechanical Engineering,
The University of Texas at Austin,
Austin, TX 78705
e-mail: asadulhaq@utexas.edu

Michael E. Cholette

Science and Engineering Faculty,
Queensland University of Technology,
Brisbane, QLD 4001, Australia
e-mail: michael.cholette@qut.edu.au

Dragan Djurdjanovic

Department of Mechanical Engineering,
The University of Texas at Austin,
Austin, TX 78705
e-mail: dragand@me.utexas.edu

1Corresponding author.

Contributed by the Dynamic Systems Division of ASME for publication in the JOURNAL OF DYNAMIC SYSTEMS, MEASUREMENT, AND CONTROL. Manuscript received March 31, 2016; final manuscript received October 21, 2016; published online February 9, 2017. Assoc. Editor: Zongxuan Sun.

J. Dyn. Sys., Meas., Control 139(4), 044501 (Feb 09, 2017) (8 pages) Paper No: DS-16-1165; doi: 10.1115/1.4035096 History: Received March 31, 2016; Revised October 21, 2016

In this paper, a dual-mode model predictive/linear control method is presented, which extends the concept of dual-mode model predictive control (MPC) to trajectory tracking control of nonlinear dynamic systems described by discrete-time state-space models. The dual-mode controller comprises of a time-varying linear control law, implemented when the states lie within a sufficiently small neighborhood of the reference trajectory, and a model predictive control strategy driving the system toward that neighborhood. The boundary of this neighborhood is characterized so as to ensure stability of the closed-loop system and terminate the optimization procedure in a finite number of iterations, without jeopardizing the stability of the closed-loop system. The developed controller is applied to the central air handling unit (AHU) of a two-zone variable air volume (VAV) heating, ventilation, and air conditioning (HVAC) system.

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Figures

Grahic Jump Location
Fig. 1

Schematic of the AHU system

Grahic Jump Location
Fig. 2

Results of simulated tracking of a step shift with DMMPLC and PID control. The results include all the output references and achieved trajectories, as well as the fan supply voltage.

Grahic Jump Location
Fig. 3

Results of simulated tracking of a filtered step shift with DMMPLC and PID control. The results include all the output references and achieved trajectories, as well as the fan supply voltage.

Grahic Jump Location
Fig. 4

Results of simulated tracking of a generic trajectory with DMMPLC and PID control. The results include all the output references and achieved trajectories, as well as the fan supply voltage.

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