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Research Papers

A Control Lyapunov Approach to Finite Control Set Model Predictive Control for Permanent Magnet Synchronous Motors

[+] Author and Article Information
Gideon Prior

Department of Electrical
and Computer Engineering,
University of California San Diego,
San Diego, CA 90293
e-mail: gprior@ucsd.edu

Miroslav Krstic

Department of Mechanical
and Aerospace Engineering,
University of California San Diego,
San Diego, CA 90293
e-mail: krstic@ucsd.edu

Contributed by the Dynamic Systems Division of ASME for publication in the JOURNAL OF DYNAMIC SYSTEMS, MEASUREMENT, AND CONTROL. Manuscript received November 10, 2012; final manuscript received July 1, 2014; published online August 28, 2014. Assoc. Editor: Srinivasa M. Salapaka.

J. Dyn. Sys., Meas., Control 137(1), 011001 (Aug 28, 2014) (10 pages) Paper No: DS-12-1366; doi: 10.1115/1.4028223 History: Received November 10, 2012; Revised July 01, 2014

In this paper, we present a novel model predictive control (MPC) scheme that incorporates stability information derived from a control Lyapunov function (CLF) to dynamically prune suboptimal sequences from the search space and decrease the computational burden placed on the controller. The CLF used for pruning is then incorporated into a cost function that penalizes energy in the error system as well as energy loss due to switching. Despite the very small control periods allowed due dynamic pruning, experimental results are given, showing the resulting controller generates low switching frequencies and low total harmonic distortion.

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Figures

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Fig. 1

Generalized architecture of a two-level power inverter

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Fig. 2

State propagation flow graph of a FCS-MPC process posed in the rotating reference frame

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Fig. 3

Combinatorial growth of the search space as the horizon is increased for standard FCS-MPC applied to a two level inverter

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Fig. 4

The improved FCS-MPC algorithm with reduced search space growth through dynamic pruning of candidate input sequences

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Fig. 5

A block diagram description of the proposed FCS-MPC algorithm

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Fig. 6

Test bed used for controller evaluation

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Fig. 7

Comparison of THD and switching frequency as a function of horizon and weighting factor α

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Fig. 8

Phase currents in the stationary reference frame, rotating reference frame and average switching frequency for N = 0, 1, 2 with weighting factor α = 0.01

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Fig. 9

Phase currents in the stationary reference frame, rotating reference frame and average switching frequency for N = 0, 1, 2 with weighting factor α = 0.5

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Fig. 10

Phase currents in the stationary reference frame, rotating reference frame and average switching frequency for N = 0, 1, 2 with weighting factor α = 0.99

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