Fuzzy Adaptive Prescribed Performance Control for a Class of Uncertain Nonlinear Systems with Unknown Dead‐Zone Inputs

Xiang, Wang; Li, Ning; Sun, Yumei

doi:10.1155/2017/4386515

Cited by 5 publications

(3 citation statements)

References 32 publications

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“…If we use “the singleton fuzzifier and the center average defuzzifier, the output of the fuzzy system can be expressed as follows” (Takagi and Sugeno, 1985: 3; Wang, 1999; Xiang et al, 2017)…”

Section: Fuzzy Systemsmentioning

confidence: 99%

Finite-time adaptive fuzzy backstepping control of drug dosage regimen in cancer treatment

Zirkohi

2019

Transactions of the Institute of Measurement and Control

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In this paper, the design of robust finite-time adaptive fuzzy backstepping control for multi-input multi-output (MIMO) cancer immunotherapy system with fully unknown parameters is addressed. The aim of the treatment is to reduce the tumor cells by providing a suitable scheduling scheme for drug dosage. The main contribution of the proposed control scheme is that it not only has the advantages of the conventional backstepping control, but also guarantees the finite-time convergence. To approximate the unknown nonlinear parts of the system, the fuzzy system is employed. Using the Lyapunov stability theory, the fuzzy system parameters are tuned online using the derived adaptation laws. Compared with conventional backstepping control, the proposed controller does not include the time derivative of the virtual control law. Therefore, the proposed control structure has a simple robust structure and the problem of “explosion of terms” is avoided. Numerical simulation results are conducted that demonstrate that, as a result of drug treatment, the number of tumor cells is rapidly reduced to zero value.

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Section: Fuzzy Systemsmentioning

confidence: 99%

Finite-time adaptive fuzzy backstepping control of drug dosage regimen in cancer treatment

Zirkohi

2019

Transactions of the Institute of Measurement and Control

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“…In [14], a constrainedinput system is tackled in combination with the optimal control to ensure a good tradeoff between control performance and energy consumption. For output constraints, artificial potential field [15], prescribed performance control [16,17], model predictive control [18], and reference governor [19] are some of the existing strategies to handle this problem. In [20,21], Barrier Lyapunov Function (BLF) is introduced which needs less initial conditions and does not require explicit system solution.…”

Section: Introductionmentioning

confidence: 99%

Neural Network Based Central Heating System Load Prediction and Constrained Control

Wang

Tu³

et al. 2018

Mathematical Problems in Engineering

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A neural network (NN) based heating system load prediction and control scheme are proposed. Different from traditional physical principle based load calculation method, a multilayer NN is incorporated with selected input features and trained to predict the heating load as well as the desired supply water temperature in heating supply loop. In this manner, a complicated load calculation model can be replaced by simple but efficient data-driven scheme and the response time to outdoor temperature variation can be enhanced. Moreover, in order to handle the input and output constraints in valve opening degree control task to achieve desired supply water temperature, Barrier Lyapunov candidate function and axillary system technique are involved. An additional NN is employed to approximate the system transfer function with reliable accuracy. The stability of the system is guaranteed through rigorous mathematical analysis. The excellent performance of the novelly proposed control over traditional PID is demonstrated via extensive simulation study. A quantitative case study is also conducted to verify the flexibility and validity of proposed load prediction strategy.

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“…Therefore, in this work, the output tracking error constraints are novelly taken into consideration for the control design. Some of the existing researches offer good results such as the ones using artificial potential field [11], prescribed performance control [12,13], model predictive control [14] as well as reference governor [15]. Additionally, Barrier Lyapunov Function (BLF) is introduced in [16][17][18].…”

Section: Introductionmentioning

confidence: 99%

Central Heating System Constrained Control with Input Delay Based on Neural Networks

Wang

Guo

et al. 2018

Mathematical Problems in Engineering

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An output constrained control with input delay is proposed for a central heating system. Due to the delay of signal transmission and valves opening time, an input delay is considered into the system and an auxiliary system is employed to handle this issue by converting the delayed input into a delay-free one. Moreover, to ensure the output supply water temperature within a limited range, Barrier Lyapunov algorithm is involved to achieve desired control accuracy. Finally, external disturbance and model uncertainty are incorporated into the dynamic system and neural networks (NN) are trained in an online fashion for the compensation. The stability of the control system is guaranteed through rigorous Lyapunov analysis and the excellent control performance over traditional PID control is demonstrated via numerical simulation study.

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Fuzzy Adaptive Prescribed Performance Control for a Class of Uncertain Nonlinear Systems with Unknown Dead‐Zone Inputs

Cited by 5 publications

References 32 publications

Finite-time adaptive fuzzy backstepping control of drug dosage regimen in cancer treatment

Finite-time adaptive fuzzy backstepping control of drug dosage regimen in cancer treatment

Neural Network Based Central Heating System Load Prediction and Constrained Control

Central Heating System Constrained Control with Input Delay Based on Neural Networks

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