Robust Electromechanical Servo System Parametric Synthesis as Multi Criteria Game Decision Based on Particles Multi Swarm Optimization

Kuznetsov, B. I.; Bovdui, I.; Nikitina, Tatyana

doi:10.1109/apuavd47061.2019.8943860

Cited by 13 publications

(1 citation statement)

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“…Most approaches focus on obtaining an “optimal” solution, i.e., a consensus between the efficiency and the acceptable energy consumed by the aforementioned system [ 1 , 2 , 3 ]. This approach leads to many problems in the context of minimizing/maximizing multi-criteria quality indices [ 4 , 5 , 6 , 7 ]. Moreover, often the optimization algorithm itself exceeds the computational capabilities of the system on which the target control system is to be implemented in, which opens the possibilities for the artificial intelligence systems (AI) [ 8 , 9 , 10 , 11 , 12 ].…”

Section: Introductionmentioning

confidence: 99%

A Sensor-Aided System for Physical Perfect Control Applications in the Continuous-Time Domain

Majewski

Hunek

Pawuś

et al. 2023

Sensors

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The recently introduced continuous-time perfect control algorithm has revealed a great potential in terms of the maximum-speed and maximum-accuracy behaviors. However, the discussed inverse model-originated control strategy is associated with considerable energy consumption, which has exceeded a technological limitation in a number of industrial cases. In order to prevent such an important drawback, several solutions could be considered. Therefore, an innovative perfect control scheme devoted to the multivariable real-life objects is investigated in this paper. Henceforth, the new IMC-related approach, strongly supported by the vital sensor-aided system, can successfully be employed in every real-time engineering task, where the precision of conducted processes plays an important role. Theoretical and practical examples strictly confirm the big implementation potential of the new established method over existing ones. It has been seen that the new perfect control algorithm outperforms the classical control law in the form of LQR (considered in two separate ways), which is clearly manifested by almost all simulation examples. For instance, in the case of the multi-tank system, the performance indices ISE, RT, and MOE for LQR without an integration action have been equal to 2.431, 2.4×102, and 3.655×10−6, respectively, whilst the respective values 1.638, 1.58×102, and 1.514×10−7 have been received for the proposed approach.

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