Model Predictive Control (MPC) has its reputation since it can handle multiple inputs and outputs with consideration to constraints. However, this comes at the cost of high computational complexity, which limits MPC to slow dynamic systems. This paper provides an overview of the available methods to accelerate the MPC process. Various parallel computing approaches using different technologies were proposed to speed up the execution of MPC, some of these approaches are focused on building dedicated hardware for MPC using field programmable arrays (FPGA), and others are focused on parallelizing MPC computation using multi-core processors (CPUs) and many-core processors (GPUs). The focus of this survey is to review the available methods for accelerating MPC process. A brief introduction to the theory of MPC is provided first followed by a description of each approach. A comparison between the different methods is presented in terms of complexity and performance followed by a valid application for each approach. Finally, this paper discusses the challenges and requirements of MPC for future applications.
The paper explores the use of a GPU-Event-Mechanics (GEM) simulation to assess local ice loads on a vessel operating in pack ice. The methodology uses an event mechanics concept implemented using massively parallel programming on a GPU enabled workstation. The simulation domain contains hundreds of discrete and interacting ice floes. A simple vessel is modeled as it navigates through the domain. Each ship-ice collision is modeled, as is every ice-ice contact. Each ship-ice collision event is logged, along with all relevant ice and ship data. Thousands of collisions are logged as the vessel transits many tens of kilometers of ice pack. The GEM methodology allows the simulations to be performed much faster than real time. The resulting impact load statistics are qualitatively evaluated and compared to published field data. The analysis provides insight into the nature of loads in pack ice. The work is part of a large research project at Memorial University called STePS2 (Sustainable Technology for Polar Ships and Structures).
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