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A swarm intelligence-based tuning method for the Sliding Mode Generalized Predictive Control.

J B Oliveira1, J Boaventura-Cunha1, P B Moura Oliveira1

  • 1INESC TEC - INESC Technology and Science (formerly INESC Porto, UTAD pole) Department of Engineering, School of Sciences and Technology 5001-811 Vila Real, Portugal.

ISA Transactions
|July 14, 2014
PubMed
Summary
This summary is machine-generated.

This study introduces an automatic tuning method for constrained Sliding Mode Generalized Predictive Controllers (SMGPC) using Particle Swarm Optimization (PSO). The approach enhances robustness and tracking accuracy for industrial process models.

Keywords:
Model Predictive ControlParticle Swarm OptimizationRobustnessSliding ModeSoft computing

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Area of Science:

  • Control Engineering
  • Optimization Algorithms
  • Industrial Automation

Background:

  • Model Predictive Control (MPC) is widely used in industry.
  • Sliding Mode Control (SMC) offers robustness but can be challenging to tune.
  • Generalized Predictive Control (GPC) combines MPC with predictive models.

Purpose of the Study:

  • To develop an automatic tuning method for the discontinuous component of Sliding Mode Generalized Predictive Controllers (SMGPC).
  • To address constraint handling within the SMGPC framework.
  • To improve the performance and robustness of SMGPC for industrial applications.

Main Methods:

  • Utilizing Particle Swarm Optimization (PSO) to minimize an aggregated cost function for tuning the discontinuous SMC component.
  • Employing Quadratic Programming (QP) for the standard procedure to obtain the continuous component.
  • Implementing an online dual optimization scheme.

Main Results:

  • Demonstrated superior performance compared to standard methods on common industrial process models.
  • Achieved improved robustness against system uncertainties and disturbances.
  • Enhanced tracking accuracy for setpoint changes and load disturbances.
  • Validated effectiveness on nonminimum phase and time-delayed systems.

Conclusions:

  • The proposed automatic tuning method effectively optimizes the SMGPC.
  • The PSO-based approach provides a robust and accurate control solution for constrained systems.
  • This method offers significant advantages for industrial process control, particularly for complex dynamics.