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- W4286776191 abstract "• Control approach suitable for implementation on embedded platforms. • Optimization-free evaluation of close-to-optimal control inputs. • Guarantee of asymptotic stability with respect to physical constraints. • The benefits of the approach demonstrated using a 32-bit microcontroller. • Reduction of computational time and energy consumption. Advanced, optimization-based control methods are implemented at each level of industrial production. Although the model predictive control (MPC) represents a state-of-the-art control strategy maximizing profit, and, simultaneously, minimizing the energy losses, its industrial implementation is limited by the requirements on the software and hardware resources. This paper proposes an approximated MPC for such cases when the implementation of implicit MPC has a prohibitive effort on industrial hardware and its explicit counterpart is limited by its memory footprint requirements. The recursive random shooting-based approach is introduced to eliminate the conventional optimization and minimize the memory requirements, meanwhile guarantying the asymptotic stability subject to the physical constraints on control inputs and system states. The benefits of the proposed method are significantly reduced computational complexity by 93%, and decreased energy consumption by 93%, compared to non-recursive random shooting, while the performance loss is approximately 8%." @default.
- W4286776191 created "2022-07-23" @default.
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- W4286776191 date "2022-09-01" @default.
- W4286776191 modified "2023-10-18" @default.
- W4286776191 title "Approximated MPC for embedded hardware: Recursive random shooting approach" @default.
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- W4286776191 doi "https://doi.org/10.1016/j.compchemeng.2022.107928" @default.
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