Subspace Data-Driven Control for Linear Parameter Varying Systems

Abstract

In this research, a unique subspace data driven control for linear parameter changing system with scheduling parameters is presented. This control paves the way for investigating the nonlinear system based on the results regarding the linear system that are already known. Only the data matrix is utilized to represent the output prediction value in the future various time instants, while the input-output observation data matrix is used to identify Markov parameters in the form of state space forms. The cost function in data-driven control is then adjusted using the output prediction value. The optimal control input value of this quadratic cost function is solved using a parallel distribution technique, and the algorithm's iterative convergence is thoroughly examined. Finally, the DC motor, whose mass distribution factor is considered to be one linear parameter varying system, is controlled using the suggested subspace data driven control approach.

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“…Robustness is commonly characterised as the bare minimum that a control system must meet in order to be usable in a real situation. Once created, the controller's settings do not alter, and control performance is assured (Ahmed et al, 2023a(Ahmed et al, ,b, 2022aSergiyenko et al, 2023 ;Kengne et al, 2023a,b ;Hashim et al, 2023 ;Hameed et al, 2023 ;Fekik et al, 2023a,b ;Zhang et al, 2023 ;Wang et al, 2023 ;Dendani et al, 2023 ;Toumi et al, 2022 ;Mahdi et al 2022 ;Ali el al., 2022a,b ;Abdul-Kareem et al, 2022;Abed et al, 2022;Sekhar et al, 2022 ;Saidi et al, 2022;Al Mhdawi et al, 2022 ;Lajouad et al, 2021;El Kafazi et al, 2021 ;Sundaram et al, 2021 ;Tolba et al, 2017a,b ;Soliman et al, 2017Soliman et al, , 2020Wang et al, 2017;Humaidi et al 2022Humaidi et al , 2021Humaidi et al , 2020Djeddi et al, 2019;Shukla et al, 2018;Khettab et al, 2018;Vaidyanathan et al, 2021aVaidyanathan et al, ,b,c,d,e, 2019Vaidyanathan et al, , 2018aVaidyanathan et al, ,b,c,d, 2017aVaidyanathan et al, ,b,c, 2015aVaidyanathan and Azar, 2021, 2016a,b,c,d,e,f, 2015aSantoro et al, 2013).…”

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“…Artificial Intelligence (AI) stands as the overarching field that encompasses computational intelligence, metaheuristic algorithms, control and robotics, forming a dynamic and interconnected landscape of technological advancement (Ahmed et al, 2023a(Ahmed et al, ,b,c, 2022aSayed et al, 2023;Sergiyenko et al, 2023;Vaidyanathan et al, 2023Vaidyanathan et al, , 2019Vaidyanathan et al, , 2018aVaidyanathan et al, ,b, 2017aVaidyanathan et al, ,b, 2015Kengne et al, 2023a,b;Azar et al, ,d, 2022aAzar et al, ,b, 2020aHashim et al, 2023;Hameed et al, 2023;Fekik et al, 2023aFekik et al, ,b,c, 2022aFekik et al, ,b,c, 2021aZhang et al, 2023;Wang et al, 2023;Dendani et al, 2023;Bousbaine et al, 2023;Hasan et al, 2023;Hamida et al, 2023Hamida et al, , 2022bNaoui et al, 2023). Artificial intelligence (AI) is rapidly transforming the field of robotics, enabling robots to perform more complex tasks and operate in more challenging environments.…”