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A hierarchical approach to data-driven LPV control design of constrained systems

A hierarchical approach to data-driven LPV control design of constrained systems

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Modeling is recognized to be one of the toughest and most time-consuming tasks in modern nonlinear control engineering applications. Linear parameter-varying (LPV) models deal with such complex problems in an effective way, by exploiting wellestablished tools for linear systems while, at the same time, being able to accurately describe highly nonlinear and time-varying plants. When LPV models are derived from experimental data, it is difficult to estimate a priori how modeling errors will affect the closed-loop performance. In this work, a method is proposed to directly map data onto LPV controllers. Specifically, a hierarchical structure is proposed both to maximize the system performance and to handle signal constraints. The effectiveness of the approach is illustrated via suitable simulation tests.

Chapter Contents:

  • 11.1 Introduction
  • 11.2 Related works
  • 11.3 Problem formulation
  • 11.4 A hierarchical approach
  • 11.5 Data-driven inner controller design
  • 11.5.1 Inversion of the reference model
  • 11.5.2 Data-driven controller design
  • 11.5.3 Dual problem
  • 11.6 Outer controller design
  • 11.7 Case study: servo-positioning system
  • 11.7.1 System description
  • 11.7.2 Desired inner closed-loop behavior
  • 11.7.3 Inner controller design
  • 11.7.4 Achieved inner closed-loop behavior
  • 11.7.5 Outer controller design
  • 11.8 Conclusions
  • References

Inspec keywords: control system synthesis; closed loop systems; nonlinear control systems; linear parameter varying systems

Other keywords: nonlinear control engineering; data-driven LPV control design; linear closed-loop performance; constrained systems; time-consuming tasks; hierarchical structure; linear parameter-varying models

Subjects: Control system analysis and synthesis methods

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