A linear regression variable time delay estimation algorithm for the analysis of hydraulic manipulators

Albrecht, Olivia; Taylor, C. James

doi:10.1109/control55989.2022.9781372

Cited by 3 publications

(2 citation statements)

References 15 publications

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“…Berbeda dengan klasifikasi yang memprediksi nilai variabel yang bersifat diskret, regresi melakukan fungsi pembelajaran yang memetakan sebuah unsur data ke sebuah variabel prediksi bernilai nyata, yang digunakan untuk memprediksi nilai variabel yang bersifat kontinyu. Terdapat beberapa jenis regresi antara lain: a. Regresi Linier [7], merupakan sebuah pendekatan untuk memodelkan hubungan antara variabel terikat Y dan satu atau lebih variabel X yang merupakan variabel bebas, dimana seluruh variabelnya adalah data kuantitatif. Disebut linier karena setiap estimasi atas nilai diharapkan memgalami peningkatan atau penurunan mengikuti garis lurus.…”

Section: Regresiunclassified

Prediksi Daya Output Sistem Pembangkit Listrik Tenaga Surya (PLTS) Menggunakan Regresi Linear Berganda

Bramasto,

Khairiani

2022

FaktorExacta

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<table width="593" border="1" cellspacing="0" cellpadding="0"><tbody><tr><td valign="top" width="388"><p>The power generated by Solar Power Plants (Pembangkit Listrik Tenaga Surya/PLTS) from time to time is fluctuating due to the influence of weather and other external conditions. This study predicts the output power of PLTS Sumalata in North Gorontalo Regency with data analytics on datasets obtained from measurements at 2 plants in PLTS Sumalata. Data analytics to predict the output power of PLTS Sumalata is using a multiple linear regression approach, which is applied by implementing the Cross-industry standard for data mining (CRISP-DM) process model. The tools used are the Weka 3.0 application and Jupyter Notebook with the Python programming language. With data analytics using Weka 3.0 on datasets obtained from measurements at 2 plants in PLTS Sumalata, multiple linear regression equations were obtained as well as evaluation of prediction results using Correlation Coefficient (CC), Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), Relative Absolute Error (RAE), and Root Relative Squared Error (RRSE). The equation formed from the prediction of the output power in Plant 1 is Y = -22216632810.1123 - 771640073.1888 X<sub>1</sub> + 2349039057.8254 X<sub>2</sub> -25796134709.3552 X<sub>3</sub>. While the equation formed from the prediction of the output power in Plant 2 is Y = -2784.107 + 300.0146 X<sub>1</sub> – 173.7016 X<sub>2</sub> + 21773.3845 X<sub>3</sub>. Based on the test, the correlation coefficient on the Plant 1 dataset is 0.52 and the Plant 2 dataset is 0.92. Those can be concluded that the irradiation data, module temperature, and ambient temperature have a significant effect of 52% on the output power generated in the PLTS system at Plant 1 and 92% on Plant 2. Then the MAE, RMSE, RAE, and RRSE values in the Plant 1 dataset are higher than Plant 2, while the relationship between the independent variables and the dependent variables in the Plant 2 dataset is stronger than the Plant 1 dataset. In order to improve the accuracy of the prediction that can be used for evaluating the performance of the PLTS system, measurement data with a minimum measurement duration of one year is needed to be able to represent seasonal conditions throughout the year, such as the dry season, rainy season, and extreme weather conditions.</p></td></tr></tbody></table>

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Section: Regresiunclassified

Prediksi Daya Output Sistem Pembangkit Listrik Tenaga Surya (PLTS) Menggunakan Regresi Linear Berganda

Bramasto,

Khairiani

2022

FaktorExacta

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“…For control system design, it will be assumed that τ (k) is known at sample k. Of course, in general, τ (k) is unknown and must be estimated using one of the methods from the literature, such as the multi-model approach of [7], an on-line version of the first author's recently proposed polynomialbased approach [23], or e.g. [24,25].…”

Section: Introductionmentioning

confidence: 99%

Pole assignment control design for time–varying time–delay systems using radial basis functions

Albrecht

Taylor

2022

2022 UKACC 13th International Conference on Control (CONTROL)

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Systems with time-varying time delays present a particularly challenging control problem. They have been observed across a wide array of domains, from hydraulic actuators to insulin delivery control systems. Control systems that address system time-delays, nonlinearities and uncertainty are the subject of much research but, whilst the specific concept of varying time delays is sometimes acknowledged (for example in the control of hydraulic manipulators), this appears to be less widely investigated than some other types of nonlinearity. In part motivated by recent research into internal multi-model control, as similarly applied to systems with unknown time-varying delays, the present work utilises a Gaussian radial basis function to switch between two or more partial controllers. Each partial controller is based on a linear model with a (time-invariant) time delay. The new algorithm is developed and evaluated via simulation using a non-minimal state space (NMSS) framework, with pole assignment as the design criterion. Simulation results suggest that it yields improved performance in comparison to a simpler switching approach and the equivalent linear control system. However, laboratory examples and further research into robustness and stability is required in the next step.

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Research on Hierarchical Control of Intelligent Industrial Vehicle Path Tracking Based on Fully Hydraulic Steering Time Delay

Xia,

Jiang,

Tang

et al. 2024

Int.J Automot. Technol.

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A linear regression variable time delay estimation algorithm for the analysis of hydraulic manipulators

Cited by 3 publications

References 15 publications

Prediksi Daya Output Sistem Pembangkit Listrik Tenaga Surya (PLTS) Menggunakan Regresi Linear Berganda

Prediksi Daya Output Sistem Pembangkit Listrik Tenaga Surya (PLTS) Menggunakan Regresi Linear Berganda

Pole assignment control design for time–varying time–delay systems using radial basis functions

Research on Hierarchical Control of Intelligent Industrial Vehicle Path Tracking Based on Fully Hydraulic Steering Time Delay

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