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Updated: Jun 16, 2025

Gain-compensation Methodology for a Sinusoidal Scan of a Galvanometer Mirror in Proportional-Integral-Differential Control Using Pre-emphasis Techniques

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Polytopic inclusion-based model predictive control for quasi-LPV systems using vertex system models and gain

Rangoli Singh¹, Sandip Ghosh¹, Devender Singh¹

¹Department of Electrical Engineering, Indian Institute of Technology (Banaras Hindu University), Varanasi, India.

ISA Transactions

|June 13, 2025

Summary

A new Model Predictive Control (MPC) strategy is introduced for quasi-Linear Parameter-Varying (quasi-LPV) systems. This advanced quasi-LPV-MPC enhances control precision and adaptability in dynamic systems.

Keywords:

Cascaded coupled tank system Model predictive control (MPC)Quasi-linear parameter varying (quasi-LPV) systems Twin rotor multi-input multi-output system

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Published on: October 14, 2017

Area of Science:

Control Systems Engineering
Automation and Robotics

Background:

Quasi-Linear Parameter-Varying (quasi-LPV) systems present challenges due to their measurable time-varying parameters.
Traditional Model Predictive Control (MPC) methods may struggle with the dynamic nature of quasi-LPV systems.

Purpose of the Study:

To develop and validate a novel Model Predictive Control (MPC) strategy specifically designed for quasi-LPV systems.
To improve control precision, adaptability, and system responsiveness compared to existing MPC approaches.

Main Methods:

The proposed controller utilizes a polytopic representation and a gain-scheduled controller for quasi-LPV systems.
A terminal cost dependent on the scheduling parameter and a complementary cost function are employed to relax prediction horizon updates.
The strategy is implemented and tested on both simulated (twin rotor multi-input multi-output system) and experimental (cascaded coupled tank system) platforms.

Main Results:

The quasi-LPV-MPC strategy demonstrates improved control performance over traditional MPC schemes.
Experimental validation confirms the practical efficacy of the proposed controller.
Comparative evaluations show superior control precision, adaptability, and responsiveness compared to linear and nonlinear MPCs.

Conclusions:

The developed quasi-LPV-MPC is an effective solution for controlling quasi-LPV systems.
The method offers enhanced performance metrics, including precision and adaptability.
The controller's practical applicability is validated through experimental and simulation studies.