Output-Tracking Quantized Explicit Nonlinear Model Predictive Control Using Multiclass Support Vector Machines

Ankush Chakrabarty

doi:10.1109/TIE.2016.2638401

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Output-Tracking Quantized Explicit Nonlinear Model Predictive Control Using Multiclass Support Vector Machines

2017-05-01

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Ankush Chakrabarty *

Gregery T. Buzzard

Stanisław H. Żak

DOI：10.1109/TIE.2016.2638401

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Abstract

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In applications involving digital control, the set of admissible control actions is finite/quantized. Coupled with state constraints and fast dynamics, explicit model predictive control (EMPC) provides an attractive control formalism. However, the design of data-driven EMPCs with finite admissible control sets is a challenging and relatively unexplored problem. In this paper, a systematic data-driven method is proposed for the design of quantized EMPCs (Q-EMPCs) for time-varying output tracking in nonlinear systems. The design involves: 1) sampling the admissible state space using low-discrepancy sequences to provide scalability to higher dimensional nonlinear systems; 2) at each sampled data point, solving for optimal quantized model predictive control actions and determining feasibility of the intrinsic mixed-integer nonlinear programming problem; and 3) constructing the Q-EMPC control surface using multiclass support vector machines (MC-SVMs). In particular, four widely used MC-SVM algorithms are employed to construct the proposed data-driven Q-EMPC. Extensive testing and comparison among the different MC-SVM algorithms is performed on 2-D and 5-D benchmark examples to demonstrate the effectiveness and scalability of the proposed methodology.

Keywords:

Explicit nonlinear model predictive control

finite control set

multicategory learning

output tracking

supervised learning

support vector machines (SVMs)

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