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Modified Active Disturbance Rejection Predictive Control: A fixed-order state-space formulation for SISO systems.

Blanca Viviana Martínez Carvajal1, Javier Sanchis Sáez1, Sergio García-Nieto Rodríguez1

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Summary

This study introduces a new control strategy combining Model Predictive Control (MPC) and Active Disturbance Rejection Control (ADRC) for systems without a precise model. It effectively handles disturbances and constraints for improved system performance.

Keywords:
Active Disturbance Rejection ControlConstrained systemsExtended State ObserverModel Predictive ControlState space model

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Area of Science:

  • Control Systems Engineering
  • Automation and Robotics
  • Nonlinear System Analysis

Background:

  • Model-based control strategies often require accurate system models, which are difficult to obtain for complex or unknown systems.
  • Existing methods struggle to simultaneously address model uncertainties, external disturbances, and system constraints effectively.
  • Active Disturbance Rejection Control (ADRC) and Model Predictive Control (MPC) are powerful techniques but have limitations when applied independently to constrained systems without identified models.

Purpose of the Study:

  • To develop a novel control strategy integrating ADRC and MPC for systems lacking a nominal identified model.
  • To enable robust control of constrained linear and nonlinear systems despite model uncertainties and external disturbances.
  • To relax stringent modeling requirements by utilizing a simplified plant realization and an Extended State Observer (ESO).

Main Methods:

  • A novel control architecture merging state-space Model Predictive Control (MPC) and Active Disturbance Rejection Control (ADRC).
  • Utilization of a third-order discrete Extended State Observer (ESO) to estimate unmodeled dynamics and system states.
  • Incorporation of a total perturbation compensation term within the optimization problem constraints for effective constraint handling.

Main Results:

  • The proposed control strategy successfully estimates and rejects unknown disturbances and model mismatches.
  • Effective handling of system constraints by integrating perturbation compensation into the optimization framework.
  • Demonstrated applicability to both linear and nonlinear systems in servo-regulatory tasks through validation examples.

Conclusions:

  • The novel ADRC-MPC integrated control strategy offers a robust solution for constrained systems with no nominal identified model.
  • The approach relaxes modeling requirements while maintaining high performance in the presence of uncertainties and disturbances.
  • This work provides a unified framework for advanced control of complex systems, enhancing servo-regulatory capabilities.