
Abstract This paper presents design and development of an optimal robust PI controller based on linear matrix inequalities for a typical industrial process control system. In this method, the design problem has been transformed into a state feedback controller design problem for an augmented uncertainty MIMO system. In this work, a dynamic decoupler is designed to handle loop interaction. The control problem is then solved for the obtained decoupled subsystem by employing a constraint optimization approach, ensuring compliance with a defined LQ cost objective function. Simulation results verify the efficacy of the proposed controller, presenting its effectiveness in achieving both set-point accuracy and disturbance attenuation. Furthermore, the study employs disk margin analysis to ascertain safe ranges for gain and phase margin.
Artificial intelligence, Science, Geometry, Control (management), Multivariable calculus, Process Fault Detection and Diagnosis in Industries, Mathematical analysis, Article, PID Controller, Engineering, Linear system, Control theory (sociology), FOS: Mathematics, Controller Tuning, Robustness, Analysis and Design of Fractional Order Control Systems, Temperature control, Decoupling Control, Control engineering, Q, R, Pi, Linear control systems, Linear matrix inequality, Computer science, Model Predictive Control in Industrial Processes, Inequality, Control and Systems Engineering, Combinatorics, Physical Sciences, Process control, Medicine, PID controller, Loop (graph theory), Nonlinear Systems, Mathematics, Nonlinear MPC
Artificial intelligence, Science, Geometry, Control (management), Multivariable calculus, Process Fault Detection and Diagnosis in Industries, Mathematical analysis, Article, PID Controller, Engineering, Linear system, Control theory (sociology), FOS: Mathematics, Controller Tuning, Robustness, Analysis and Design of Fractional Order Control Systems, Temperature control, Decoupling Control, Control engineering, Q, R, Pi, Linear control systems, Linear matrix inequality, Computer science, Model Predictive Control in Industrial Processes, Inequality, Control and Systems Engineering, Combinatorics, Physical Sciences, Process control, Medicine, PID controller, Loop (graph theory), Nonlinear Systems, Mathematics, Nonlinear MPC
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