A modified particle swarm optimization for lower order model formulation of linear time invariant continuous systems

Deepa, S. N.; Sugumaran, G.

doi:10.1109/icinnovct.2010.5440097

Cited by 3 publications

(4 citation statements)

References 12 publications

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“…Therefore, the mean filter is used to estimate the partial gross error and nonlinear signals in the data of random drift error after pre-treatment, which can overcome the inaccurate estimation of the model parameters. The expression of the mean value estimation method is [19]…”

Section: Mean Filter and Time Series Modeling 41 Average Filtering Amentioning

confidence: 99%

Research on the modeling of random drift error and filtering technology of low cost mems gyroscope

Xiong¹,

Tang²,

Wang³

et al. 2017

MMC_A

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In order to improve the precision of the low cost MEMS gyroscope and reduce the influence of the random drift error on the measurement system. In this paper, the Allan variance method, mean filtering method, time series analysis method and Kalman filtering technique are used to analyze and filter the random error of static output of MEMS gyroscope. The results show that the amplitude of the random drift data is significantly reduced after filtering, the peak value of error data is 19.3% of that before filtering, and the variance is 3.1% of that before filtering. Main noises such as the angle random walk, the bias instability and the rate ramp are effectively suppressed. Above all, the method proposed in this paper can effectively reduce the random drift error of MEMS gyroscope and improve the output precision of MEMS gyroscope.

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Section: Mean Filter and Time Series Modeling 41 Average Filtering Amentioning

confidence: 99%

Research on the modeling of random drift error and filtering technology of low cost mems gyroscope

Xiong¹,

Tang²,

Wang³

et al. 2017

MMC_A

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show abstract

“…The advantages of PID controllers over other feedback controllers lie in their robustness, simple design algorithms and stability. Additionally, PID controllers have been widely used in high-order systems since the early 20th century [12], [13]. If appropriately tuned, then the PID feedback controller can perform satisfactorily, even controlling systems with non-linearities.…”

Section: Introductionmentioning

confidence: 99%

Pole placement tuning of proportional integral derivative feedback controller for knee extension model

Arof,

Noorsal,

Yahaya

et al. 2024

IJEECS

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Functional electrical stimulation (FES) has shown potential in rehabilitative exercises for patients recovering from spinal cord injuries. In recent developments, conventional open-loop FES control techniques have evolved into closed-loop systems that employ feedback controllers for automation. However, closed-loop FES systems often face challenges due to muscle non-linear effects, such as fatigue, time delays, stiffness, and spasticity. Therefore, an accurate non-linear knee model is required during the design stage, and precise tuning of the feedback controller parameters is vital. A proportional– integral–derivative (PID) controller is commonly used as a feedback controller due to its simplicity and ease of implementation. However, most PID tuning methods are complex and time consuming. This paper investigates the viability of employing the pole placement technique for tuning a PID controller that regulates the non-linear knee extension model. The pole placement method aims to improve the control and adaptability of the PID controller in closed-loop FES systems, specifically by facilitating knee extension exercises. MATLAB Simulink was used to assess the effectiveness of this tuning approach. Results showed that the PID controller performed satisfactorily without non-linearities, but performance varied with the inclusion of specific non-linearities. The pole placement tuning method facilitated preliminary assessments of PID controller performance, preceding highly advanced optimization.

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“…The proportional-integral-derivative controller (Deepa and Sugumaran, 2011) was designed on a reduced-order model for an eighth-order continuous-time LTI system using modified PSO-based techniques. Although it might significantly improve the response, this did not particularly emphasize its suitability for a class of large-scale systems.…”

Section: Introductionmentioning

confidence: 99%

“…In the last four decades, many reduction techniques (Davison, 1966; Langholz and Feinmesser, 1978; Shamash, 1974) have been presented to reduce a higher order system and facilitate the design of a controller for the equivalent reduced-order system. Several reduction techniques (Chen et al, 1979; Deepa and Sugumaran, 2011; Hutton and Friedland, 1975; Krishnamurthy and Seshadri, 1978; Shamash, 1974; Sinha and Pal, 1990) were proposed for model reduction of higher order linear time-invariant (LTI) systems. When these reduction techniques were used, there were stability problems in a certain category of large-scale original systems.…”

Section: Introductionmentioning

confidence: 99%

An effective controller design strategy for higher order systems

Chandramouleeswaran,

Deivanayagam Pillai,

Ramasamy

et al. 2023

Journal of Vibration and Control

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The recent methods that guarantee the stability of the reduced-order models are Mihailov stability, improved Pade approximation and truncation, improved generalized pole clustering, moment matching and salp swarm optimization. Further, these methods could overcome the limitations such as non-uniqueness, pole clustering, gain adjustment and difficulty to maintain the dominant roots in the lower order system for non-minimum higher order plants. This research emphasizes the design of the proposed compensating algorithm by using an additional open loop zero for the stable reduced-order models of large-scale single-input single-output linear time-invariant continuous time systems. The results of the proposed algorithm are compared with the existing compensating methods and the design is validated and illustrated numerically.

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A modified particle swarm optimization for lower order model formulation of linear time invariant continuous systems

Cited by 3 publications

References 12 publications

Research on the modeling of random drift error and filtering technology of low cost mems gyroscope

Research on the modeling of random drift error and filtering technology of low cost mems gyroscope

Pole placement tuning of proportional integral derivative feedback controller for knee extension model

An effective controller design strategy for higher order systems

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