A Robust Internal Model-Based Fractional Order Controller for Fractional Order Plus Time Delay Processes

Arya, Pushkar Prakash; Chakrabarty, Sohom

doi:10.1109/lcsys.2020.2994606

Cited by 44 publications

(19 citation statements)

References 23 publications

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“…Therefore, it is necessary to do a correlation analysis between the load and the influencing factors, and select the appropriate influencing factors. In the selection of prediction models, a model with strong nonlinear mapping ability should also be selected for prediction [18].…”

Section: Short-term Forecast Of Multiple Loads In the Integrated Ener...mentioning

confidence: 99%

Short-term Load Forecast of Integrated Energy System in Multi-station Fusion Scenario Based on Robust Model

2021

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Integrated Energy System (IES) is the development direction of energy consumption in the future. Through the coordinated design and planning of energy systems such as electricity, cooling, and heat, energy utilization can be effectively improved and the development of renewable energy units can be promoted. The purpose of this paper is to predict the short-term load of an integrated energy system in a multi-station fusion scenario based on a robust model. Convex Quadratic Loss Functions to Suppress Negative Effects of Outliers Non-convex Quadratic Loss Functions limit the maximum loss penalty for outliers. First, the loss function is expressed as the difference of two quadratic functions, and the corresponding robust model is built. Second, the optimization problem corresponding to the robust model is transformed into a system of linear equations using the CCCP technique and KKT conditions. Finally, the prediction accuracy of RLS-SVR model, SVR model and BP neural network model is compared. The results show that for cooling load prediction, the average relative error of the prediction model constructed by RLS-SVR is 1.05% and 1.12%, which is lower than other models.

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Section: Short-term Forecast Of Multiple Loads In the Integrated Ener...mentioning

confidence: 99%

Short-term Load Forecast of Integrated Energy System in Multi-station Fusion Scenario Based on Robust Model

2021

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“…Nowadays, fractional-order controllers (FOCs) in engineering applications are evolving into an attractive area. 17–19 The methods with FOC provide an extra degree of freedom in tuning parameters. The fractional-order proportional–integral–derivative (FOPID) presented by Podlubny 20 has two additional parameters: fractional differential and fractional integral orders.…”

Section: Introductionmentioning

confidence: 99%

Improved control of integrating cascade processes with time delays using fractional-order internal model controller with the Smith predictor

Ranjan

Mehta

2023

Proceedings of the Institution of Mechanical Engineers, Part I:

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This article proposes an enhanced control of integrating cascade processes. The importance of the scheme shows by controlling a double integrating process with time delay under significant parametric uncertainties and load disturbances. A novel design is proposed based on the Smith predictor principle and uses a fractional-order internal model controller in the outer loop. The required fractional-order tuning parameter follows the desired gain and phase margins. At the same time, another tuning parameter, such as the fractional filter’s time constant, is calculated from the desired performance constraint. Numerical analysis and comparison are performed to showcase the feature of the hybrid structure. The simulation results show that the fractional-order internal model controller–based scheme provides enhanced tracking and faster regulation capabilities and works well under nominal and parameter uncertainty conditions.

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“…Due to the addition of multiple adjustable parameters, the degree of freedom and flexibility of the controller are enhanced, making the control performance more likely to be improved [ 24 ], so it has received extensive attention in recent years. For example, scholars [ 25 ] proposed a fractional-order internal model controller for fractional-order process model with time delay, which shows that a fractional order controller can obtain more accurate and better results. Ref.…”

Section: Introductionmentioning

confidence: 99%

Fractional-Order Active Disturbance Rejection Control with Fuzzy Self-Tuning for Precision Stabilized Platform

Zhao

Liu

2022

Entropy

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In this paper, a novel fractional-order active disturbance rejection control with fuzzy self-tuning method (FSFOADRC) is proposed for photoelectric tracking system (PTS). Firstly, aiming at the internal uncertainty of PTS and external disturbance, a fraction-order extended state observer (FOESO) is designed, and the FOESO can transform the plant into a simple form, which greatly simplifies the mathematical model. Secondly, a fuzzy regulator is applied to the proportion–differentiation controller (PD), increasing the flexibility and adaptivity of the controller. In addition, the stability of the whole control system can be guaranteed. Eventually, numerical comparative simulations are implemented to verify the feasibility and superiority of the proposed method. Compared with the integral-order active disturbance rejection control (IOADRC), fractional-order active disturbance rejection control (FOADRC) without the fuzzy regulator and proportion–integration–differentiation (PID) controller, the proposed method performs better with faster response, smaller overshoot, and stronger disturbance suppression capability.

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A Robust Internal Model-Based Fractional Order Controller for Fractional Order Plus Time Delay Processes

Cited by 44 publications

References 23 publications

Short-term Load Forecast of Integrated Energy System in Multi-station Fusion Scenario Based on Robust Model

Short-term Load Forecast of Integrated Energy System in Multi-station Fusion Scenario Based on Robust Model

Improved control of integrating cascade processes with time delays using fractional-order internal model controller with the Smith predictor

Fractional-Order Active Disturbance Rejection Control with Fuzzy Self-Tuning for Precision Stabilized Platform

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