An embedded implementation of the Generalized Predictive Control algorithm applied to automotive active suspension systems

Authors:
Yasser Shoukry;M. Watheq El-Kharashi;Sherif Hammad
Affiliations:
Computer and Systems Engineering Department, Faculty of Engineering, Ain Shams University, Cairo 11517, Egypt;Computer and Systems Engineering Department, Faculty of Engineering, Ain Shams University, Cairo 11517, Egypt;Computer and Systems Engineering Department, Faculty of Engineering, Ain Shams University, Cairo 11517, Egypt
Venue:
Computers and Electrical Engineering
Year:
2013

Citing 6
Cited 0

Generalized predictive control—Part I. The basic algorithm

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Generalized predictive control—Part II. Extensions and interpretations

Automatica (Journal of IFAC)
A square root and division free givens rotation for solving least squares problems on systolic arrays

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Introduction to VLSI Systems

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Digital Control Systems: Design, Identification and Implementation (Communications and Control Engineering)

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MPC-On-Chip: An Embedded GPC Coprocessor for Automotive Active Suspension Systems

IEEE Embedded Systems Letters

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Abstract

The Generalized Predictive Control (GPC) algorithm relies on the solution of an optimization problem at every sampling period. Profiling shows that matrix operations consume the largest portion of the computation requirements of the algorithm. This paper presents an embedded real-time implementation of the GPC algorithm, called GPC-on-Chip, based on the state-of-the-art Customizable Advanced Processor (CAP9(TM)) technology from Atmel(R), targeting automotive active suspension systems. Our system utilizes a systolic-array based matrix co-processor in order to accelerate matrix operations. The proposed embedded system is designed to fit within the proposed platform while meeting tight real-time constraints imposed by automotive active suspension systems. In order to check the applicability of the proposed system-on-chip, it is profiled against a wide variety of GPC tuning parameters and compared against the software-only implementation. An average speedup of approximately 10x is achieved.