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

机译:应用于汽车主动悬架系统的广义预测控制算法的嵌入式实现

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摘要

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.
机译:通用预测控制(GPC)算法依赖于每个采样周期的优化问题的解决方案。分析表明,矩阵运算消耗了算法最大的计算需求。本文基于Atmel最新的可定制高级处理器(CAP9)技术,针对汽车主动悬架系统,提出了一种称为GPC-on-Chip的GPC算法的嵌入式实时实现。我们的系统利用基于脉动阵列的矩阵协处理器来加速矩阵运算。拟议的嵌入式系统旨在适应拟议的平台,同时满足汽车主动悬架系统施加的严格实时约束。为了检查所提出的片上系统的适用性,针对各种GPC调整参数对它进行了分析,并与仅软件的实现进行了比较。实现了大约10倍的平均加速。

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