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Observer based robust H _∞ fuzzy tracking control: application to an activated sludge process.

ABSTRACT: The design of an observer-based robust tracking controller is investigated and successfully applied to control an Activated Sludge Process (ASP) in this study. To this end, the Takagi-Sugeno (TS) fuzzy modeling is used to describe the dynamics of a nonlinear system with disturbance. Since the states of the system are not fully available, a fuzzy observer is designed. Based on the observed states and a reference state model, a reduced fuzzy controller for trajectory tracking purposes is then proposed. While the controller and the observer are developed, the design goal is to achieve the convergence and a guaranteed H _∞ performance. By using Lyapunov and H _∞ theories, sufficient conditions for synthesis of a fuzzy observer and a fuzzy controller for TS fuzzy systems are derived. Using some special manipulations, these conditions are reformulated in terms of linear matrix inequalities (LMIs) problem. Finally, the robust and effective tracking performance of the proposed controller is tested through simulations to control the dissolved oxygen and the substrate concentrations in an activated sludge process.

SUBMITTER: Khallouq A

PROVIDER: S-EPMC8053019 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature

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Observer based robust <i>H</i> <sub>∞</sub> fuzzy tracking control: application to an activated sludge process.

Khallouq Abdelmounaim A Karama Asma A Abyad Mohamed M

PeerJ. Computer science 20210413

The design of an observer-based robust tracking controller is investigated and successfully applied to control an Activated Sludge Process (ASP) in this study. To this end, the Takagi-Sugeno (TS) fuzzy modeling is used to describe the dynamics of a nonlinear system with disturbance. Since the states of the system are not fully available, a fuzzy observer is designed. Based on the observed states and a reference state model, a reduced fuzzy controller for trajectory tracking purposes is then prop ...[more]

PMID: 33954239

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Project description:Shock absorbers are indispensable components utilized for vibration mitigation in various fields, including construction, bridges, wind power, and pipelines. The shock absorber test bench serves as an essential apparatus for evaluating the dynamic and static characteristics of these absorbers. To address the significant tracking errors between actual and expected displacements during displacement loading on shock absorber damper test benches, a novel control strategy is proposed. This strategy incorporates a robust adaptive controller (RAC) enhanced by a dung beetle optimization (DBO) algorithm and a disturbance observer (DO). The dung beetle optimization algorithm is specifically designed to iteratively optimize the control parameters of the robust adaptive controller. Concurrently, a disturbance observer is implemented to accurately estimate external disturbances and perform feedforward compensation. A mathematical model of the electro-hydraulic servo control system of the test bench is established, and the stability of the proposed controller is rigorously verified using Lyapunov theory. To simulate and analyze the control method for the electro-hydraulic servo system of the test bench, a joint simulation model integrating Simulink and AEMSim is constructed. The performance of the proposed robust adaptive controller with DBO and DO is compared against an unoptimized robust adaptive controller and a traditional PID controller in terms of load displacement tracking. Simulation results demonstrate that the control method proposed in this study significantly outperforms other controllers in enhancing position tracking accuracy and improving system robustness. Furthermore, experimental verification was carried out on the proposed control strategy, compared with unoptimized robust adaptive control, the maximum displacement tracking error was reduced by 54.8%, and the response speed was improved by 36.3%, compared with traditional PID control, the minimum displacement tracking error was reduced by 67.4%, and the response speed was improved by 47.7%. The results confirmed the superior performance of the proposed control method.

Dataset Information

Observer based robust H _∞ fuzzy tracking control: application to an activated sludge process.

Publications

Observer based robust <i>H</i> <sub>∞</sub> fuzzy tracking control: application to an activated sludge process.

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Dataset Information

Observer based robust H ∞ fuzzy tracking control: application to an activated sludge process.

Publications

Observer based robust <i>H</i> <sub>∞</sub> fuzzy tracking control: application to an activated sludge process.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets

Observer based robust H _∞ fuzzy tracking control: application to an activated sludge process.