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

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™) technology from Atmel®, 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 10× is achieved.

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► We apply Generalized Predictive Control (GPC) to automotive active suspension system.
► We propose a systolic-array based SoC, called GPC-on-Chip.
► The proposed GPC-on-Chip satisfies real-time constraints imposed by the automotive system.
► Our GPC-on-Chip achieves an average of 10× speedup in the execution of the GPC algorithm.