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Proportional-Derivative (PD) control is a widely used control method in various engineering systems to enhance stability and performance. In a system with only proportional control, common issues include high maximum overshoot and oscillation, observed in both the error signal and its rate of change. This behavior can be divided into three distinct phases: initial overshoot, subsequent undershoot, and gradual stabilization.
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Proportional-Derivative (PD) controllers are widely used in fan control systems to improve stability and performance. A fan control system can be effectively represented using a Bode plot to illustrate the impact of a PD controller through its transfer function. The Bode plot visually conveys how PD control modifies the fan's response across various frequencies, providing a frequency domain interpretation of the controller's behavior.
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Proportional-Integral-Derivative (PID) controllers are widely used in various control systems to enhance stability and performance. In a thermostat, it adjusts heating or cooling based on the temperature difference between the actual and desired levels. They are often used in automotive speed systems, effectively managing sudden speed changes while maintaining a constant speed under varying conditions. On the other hand, PI controllers, commonly employed in voltage regulation, enhance stability...
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Sensitivity-based dynamic performance assessment for model predictive control with Gaussian noise.

Jianbang Liu1, Song Bo2, Benjamin Decardi-Nelson2

  • 1School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, China; Department of Chemical & Materials Engineering, University of Alberta, Edmonton, AB T6G 1H9, Canada.

ISA Transactions
|April 14, 2023
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Summary
This summary is machine-generated.

This study introduces a sensitivity-based approach to assess economic and tracking performance for advanced process control strategies, aiding controller selection under noise. The method evaluates controller gains and noise propagation to guide optimal performance and stability.

Keywords:
Controller gainDynamic performance assessmentGaussian noiseModel predictive controlSensitivity analysis

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Area of Science:

  • Process Control Engineering
  • Advanced Control Strategies
  • Dynamic Systems Analysis

Background:

  • Economic Model Predictive Control (EMPC) and Tracking Model Predictive Control (TMPC) are widely used advanced process control strategies.
  • Selecting between EMPC and TMPC for optimal performance in the presence of process and measurement noise remains a challenge.
  • Existing methods lack a robust approach for pre-evaluating and comparing the dynamic performance of these controllers under noisy conditions.

Purpose of the Study:

  • To propose a sensitivity-based performance assessment approach for pre-evaluating dynamic economic and tracking performance of EMPC and TMPC.
  • To provide guidance for selecting the most suitable controller based on process characteristics and noise levels.
  • To enable pre-configuration of control strategies for guaranteed stability, including boundary and target moving.

Main Methods:

  • Evaluation of controller gains around the optimal steady state using sensitivities of constrained dynamic programming problems.
  • Derivation of process and measurement noise propagation by substituting controller gains into the control loop.
  • Application of Taylor expansion for simplifying the calculation of variable variance and mean.
  • Calculation of performance indices by integrating objective functions with probability density functions to plot performance surfaces.

Main Results:

  • The sensitivity-based approach accurately assesses the dynamic economic and tracking performance of EMPC and TMPC.
  • Performance surfaces and indices are precisely calculated, offering a quantitative basis for controller selection.
  • The method allows for pre-configuration of boundary moving (back off) and target moving to ensure process stability.

Conclusions:

  • The proposed sensitivity-based approach effectively guides the selection between economic and tracking model predictive control strategies.
  • It provides valuable insights into controller performance under noisy conditions, leading to improved process design and control.
  • The methodology contributes to robust advanced process control by enabling pre-evaluation and stability guarantees.