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Feedback control systems01:26

Feedback control systems

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Feedback control systems are categorized in various ways based on their design, analysis, and signal types.
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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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Controller configurations are crucial in a car's cruise control system because they manage speed over time to maintain a consistent pace regardless of road conditions, thereby meeting design goals. In traditional control systems, fixed-configuration design involves predetermined controller placement. System performance modifications are known as compensation.
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Self-Tuning fuzzy controller for sun-tracker system using Gray Wolf Optimization (GWO) technique.

Sandeep Tripathi1, Ashish Shrivastava2, Kartick C Jana1

  • 1Indian Institute of Technology (ISM) Dhanbad, Jharkhand, India.

ISA Transactions
|January 28, 2020
PubMed
Summary
This summary is machine-generated.

This study proposes an optimized Takagi, Sugeno, and Kang (TSK) fuzzy controller for solar tracker systems using Gray Wolf Optimization. This method enhances energy output by improving continuous axis position control for photovoltaic panels.

Keywords:
Gray Wolf OptimizationParticle Swarm OptimizationSun-tracker systemTSK-fuzzy controllerWhale optimization

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Area of Science:

  • Renewable Energy Systems
  • Control Systems Engineering
  • Artificial Intelligence

Background:

  • Global electricity demand is rapidly increasing, necessitating efficient renewable energy solutions.
  • Photovoltaic (PV) solar tracker systems (STS) maximize energy generation when sunlight is perpendicular to the PV surface.
  • Traditional fuzzy control systems (FCS) often rely on suboptimal trial-and-error tuning for parameter optimization.

Purpose of the Study:

  • To propose an optimal tuning technique for a Takagi, Sugeno, and Kang (TSK) fuzzy controller (TFC) for STS.
  • To enhance the continuous axis position control of STS for maximum current output.
  • To utilize the Gray Wolf Optimization (GWO) algorithm for TFC parameter tuning.

Main Methods:

  • Implementation of a TSK fuzzy controller for STS position control.
  • Application of the Gray Wolf Optimization (GWO) algorithm for optimal tuning of fuzzy controller parameters.
  • Validation using various objective functions and comparative analysis with Whale Optimization Technique (WOT) and Particle Swarm Optimization (PSO).

Main Results:

  • The GWO algorithm demonstrated effective optimization of TFC parameters for STS.
  • Comparative analysis showed performance improvements in settling time and maximum overshoot.
  • Optimized fuzzy parameters were achieved under different constraint sets.

Conclusions:

  • The proposed GWO-based TSK fuzzy controller offers an effective approach for optimizing STS performance.
  • This method provides a superior alternative to traditional trial-and-error tuning for fuzzy controllers in solar energy applications.
  • The GWO algorithm shows competitive or superior performance compared to WOT and PSO for this specific control problem.