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This paper presents a support vector machine (SVM) based model predictive control (MPC) strategy to manage the engine speed to the set-point of idle speed. The predictive model is trained by SVM due to its accuracy of learning nonlinear process, simple training program and no over-fitting nature. To reduce the computational burden of controller and retain the dynamic information of system, the instantaneous...
Nonlinear model predictive control (NMPC) requires a repeated online solution of a nonlinear optimal control problem. The computation load remains the main challenge for the real-time practical application of the NMPC technique, particularly for fast systems. This paper presents a fast NMPC algorithm implemented on a field-programmable gate array (FPGA) that employs a particle swarm optimization (PSO)...
The online computational burden associated with the solution of the NMPC optimization problem is a key issue when applying nonlinear model predictive control (MPC) to fast dynamic systems. In order to improve the computation efficiency, a novel hardware implementation method for NMPC on a field programmable gate array (FPGA) chip is proposed. The optimization problem formed by NMPC is solved by particle...
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