Design of an optimal fractional fuzzy gain-scheduled Smith Predictor for a time-delay process with experimental application

Özbek, Necdet Sinan; Eker, İlyas

doi:10.1016/j.isatra.2019.08.009

Cited by 32 publications

(9 citation statements)

References 64 publications

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“…In some other control systems, such as chemical engineering, oil refining, metallurgy and heat engineering process control systems, the Smith predictor has been used effectively for compensating for the pure time delay [25,26]. For a given signal, the Smith predictor estimates the dynamic characteristics of the system under disturbance in advance and then compensates the time delay error to reduce the overshoot of the system and shorten the adjustment process [27].…”

Section: Introductionmentioning

confidence: 99%

An Improved PID Controller for the Compliant Constant-Force Actuator Based on BP Neural Network and Smith Predictor

Pei

et al. 2021

Applied Sciences

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A compliant constant-force actuator based on the cylinder is an important tool for the contact operation of robots. Due to the nonlinearity and time delay of the pneumatic system, the traditional proportional–integral–derivative (PID) method for constant force control does not work so well. In this paper, an improved PID control method combining a backpropagation (BP) neural network and the Smith predictor is proposed. Through MATLAB simulation and experimental validation, the results show that the proposed method can shorten the maximum overshoot and the adjustment time compared with traditional the PID method.

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Section: Introductionmentioning

confidence: 99%

An Improved PID Controller for the Compliant Constant-Force Actuator Based on BP Neural Network and Smith Predictor

Pei

et al. 2021

Applied Sciences

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“…In terms of controller design aspect, the fractional‐order notions are utilised in both straightforward and advanced control methodologies such as internal model [3], Smith predictor [4, 5], fuzzy [6], adaptive [7, 8], robust [9, 10], and optimal control [11]. Several studies using these methodologies have shown that fractional‐order controllers (FOCs) outperform their integer‐order counterparts with respect to either time or frequency domain specifications.…”

Section: Introductionmentioning

confidence: 99%

Optimal fractional‐order controller design using direct synthesis method

Yumuk

Güzelkaya

Eksin

2020

IET Control Theory & Appl

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“…However, it may result in poor dynamic response in the presence of apparent time-delay and/or unpredictable disturbances. To overcome these difficulties, a number of novel algorithms, such as Smith predictor (SP), fuzzy logic controller and neural network (NN) controller are employed in [10]- [15]. SP can effectively cope with the time-delay characteristics; however, it demands modeling the control target in a precise manner [10].…”

Section: Introductionmentioning

confidence: 99%

“…To overcome these difficulties, a number of novel algorithms, such as Smith predictor (SP), fuzzy logic controller and neural network (NN) controller are employed in [10]- [15]. SP can effectively cope with the time-delay characteristics; however, it demands modeling the control target in a precise manner [10]. The fuzzy PID algorithm declares to improve robustness of temperature control by dynamically changing the controller's parameters online.…”

Section: Introductionmentioning

confidence: 99%

Design of an Improved Implicit Generalized Predictive Controller for Temperature Control Systems

et al. 2020

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In this study, an implicit proportional-integral-based generalized predictive controller (PIGPC) is proposed to effectively control temperatures of industrial systems with time-varying delay. The controller is designed to optimize the target function with the proportional-integral structure for improving the controlling performance of implicit PIGPC. Meanwhile, the recursive least square method is leveraged to directly identify the parameters of controller. Compared with the conventional implicit generalized predictive controller, the process of parameter identification converges faster. The Lagrange multiplier method is introduced to solve the optimal control law of implicit PIGPC by considering the input constraints of system. An approximate decoupling controller for multivariable systems is proposed to eliminate the coupling effects. Simulation and experimental results manifest the effectiveness and feasibility of the proposed controller in temperature control systems. INDEX TERMS Decoupling control, input constraint, proportional-integral generalized predictive controller (PIGPC), Lagrange multiplier method, temperature control system.

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Design of an optimal fractional fuzzy gain-scheduled Smith Predictor for a time-delay process with experimental application

Cited by 32 publications

References 64 publications

An Improved PID Controller for the Compliant Constant-Force Actuator Based on BP Neural Network and Smith Predictor

An Improved PID Controller for the Compliant Constant-Force Actuator Based on BP Neural Network and Smith Predictor

Optimal fractional‐order controller design using direct synthesis method

Design of an Improved Implicit Generalized Predictive Controller for Temperature Control Systems

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