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Related Concept Videos

First Order Systems01:21

First Order Systems

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First-order systems, such as RC circuits, are foundational in understanding dynamic systems due to their straightforward input-output relationship. Analyzing their responses to different input functions under zero initial conditions reveals significant insights into system behavior.
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A system is linear if it displays the characteristics of homogeneity and additivity, together termed the superposition property. This principle is fundamental in all linear systems. Linear time-invariant (LTI) systems include systems with linear elements and constant parameters.
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Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
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Impulse Response01:17

Impulse Response

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The impulse response is the system's reaction to an input impulse. In an RC circuit, the voltage source is the input, and the capacitor's voltage is the output. The system's state and output response before and after input excitation are distinctly defined.
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Deconvolution, also known as inverse filtering, is the process of extracting the impulse response from known input and output signals. This technique is vital in scenarios where the system's characteristics are unknown, and they must be inferred from the observable signals.
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Feedback control systems are categorized in various ways based on their design, analysis, and signal types.
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Adaptive infinite impulse response system identification using modified-interior search algorithm with Lèvy flight.

Manjeet Kumar1, Tarun Kumar Rawat2, Apoorva Aggarwal2

  • 1Department of Electronics and Communication Engineering, Bennett University, Greater Noida, Uttar Pradesh 201310, India.

ISA Transactions
|February 4, 2017
PubMed
Summary
This summary is machine-generated.

A novel meta-heuristic optimization technique, the interior search algorithm (ISA) with Lèvy flight, efficiently identifies unknown infinite impulse response (IIR) systems. This modified ISA (M-ISA) offers faster convergence and requires no derivative information for system identification.

Keywords:
Infinite impulse response (IIR) systemInterior search algorithm (ISA)Meta-heuristic and Lèvy flightSystem identification

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Area of Science:

  • Control Systems Engineering
  • Computational Intelligence
  • Signal Processing

Background:

  • System identification is crucial for understanding and controlling dynamic systems.
  • Infinite Impulse Response (IIR) systems present complex challenges due to their recursive nature.
  • Existing optimization techniques often require derivative information or exhibit slower convergence.

Purpose of the Study:

  • To introduce a new meta-heuristic optimization technique, the interior search algorithm (ISA) with Lèvy flight.
  • To apply the proposed algorithm for the optimal parameter determination in infinite impulse response (IIR) system identification.
  • To evaluate the performance and efficiency of the modified ISA (M-ISA) compared to other established algorithms.

Main Methods:

  • The proposed method utilizes the interior search algorithm (ISA), inspired by aesthetic principles, incorporating composition and mirror phases.
  • Lèvy flight is integrated into ISA to enhance stochastic random search capabilities, leading to modified ISA (M-ISA).
  • Performance is evaluated using mean square error (MSE), computation time, and percentage improvement, with simulations on benchmark IIR systems.

Main Results:

  • The modified ISA (M-ISA) demonstrates faster convergence and requires only single parameter tuning, without needing derivative information.
  • Simulations on benchmark IIR systems (same and reduced order) show the effectiveness of the M-ISA based method.
  • Comparative analysis against algorithms like GA, PSO, CSO, CSA, DEWM, FFA, CRPSO, HS, OHS, and HPSO-GSA confirms M-ISA's efficiency.

Conclusions:

  • The proposed interior search algorithm (ISA) with Lèvy flight provides an efficient and effective approach for IIR system identification.
  • M-ISA offers advantages in convergence speed, parameter tuning simplicity, and independence from derivative calculations.
  • The method's performance is validated through comprehensive simulations and comparisons, highlighting its superiority in system identification problems.