3.3 Self-tuning regulators (STR) structure

Self-tuning regulators are adaptive control systems that automatically adjust controller parameters based on real-time system behavior. They incorporate parameter estimation and online tuning to optimize performance, adapting to changing plant dynamics and improving robustness in uncertain environments.

The STR approach involves two main stages: identification, where plant parameters are estimated, and control design, where the controller is updated based on these estimates. Parameter estimation techniques like Recursive Least Squares and Extended Least Squares are crucial for accurate real-time estimation.

Self-Tuning Regulators (STR) Structure

Concept of self-tuning regulators

• Self-Tuning Regulators function as adaptive control systems automatically adjusting controller parameters based on real-time system behavior
• STR incorporates real-time parameter estimation and online controller tuning to optimize performance (PID controllers)
• Adapts to changing plant dynamics and improves performance in uncertain environments enhancing system robustness
• Widely applied in process control (chemical reactors), robotics (manipulator control), and aerospace systems (flight control)

Stages of STR approach

• Identification stage involves plant parameter estimation through real-time data collection and model structure selection (ARX, ARMAX)
• Control design stage updates controller based on estimated parameters, selects appropriate control law (pole placement, MPC), and ensures stability and performance

Parameter estimation techniques

• Recursive Least Squares algorithm efficiently estimates parameters in real-time using forgetting factor to weight recent data more heavily
• Extended Least Squares handles colored noise improving accuracy for certain system types (ARMAX models)
• RLS offers computational efficiency while ELS provides improved accuracy for complex systems with noise considerations

Design and implementation of STR systems

• Analyze plant characteristics considering linearity, time-variance, and system order to inform design choices
• Define performance requirements specifying desired settling time, overshoot, and steady-state error targets
• Select parameter estimation method based on system complexity (RLS for simpler systems, ELS for colored noise)
• Choose control design method considering pole placement, minimum variance control, or model predictive control approaches
• Implementation steps:
  1. Model system identifying input-output relationship and selecting appropriate structure
  2. Design parameter estimator initializing estimates and setting up covariance matrix
  3. Formulate control law defining objectives and selecting controller structure
  4. Address real-time implementation considering sampling rate and computational resources
  5. Analyze stability using Lyapunov theory and evaluate robustness
  6. Evaluate performance through simulation studies and experimental validation