Control Synthesis for Discrete-Time T–S Fuzzy Systems Based on Membership Function-Dependent $H_{\infty }$ Performance

Dong, Jie; Hou, Qinghua; Ren, Mingming

doi:10.1109/tfuzz.2019.2950879

Cited by 52 publications

(23 citation statements)

References 31 publications

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“…The aim of this article is to discuss the effect of data quantization using uniform quantizer for the MFAC algorithm (3)- (5). Moreover, if data quantization makes the tracking performance worse, how to design an improved MFAC algorithm to reduce the effect of data quantization.…”

Section: Theorem 1 ([9]mentioning

confidence: 99%

“…[1][2][3] For instance, neural network-based control and fuzzy control belongs to the intelligent control method that is not based on mathematical model. [4][5][6][7][8] It is worth noting that data-driven control (DDC), which can use the system I/O data to achieve the design of the controller, has attracted a lot of attention in recent years. According to the different ways of using data, DDC can be classified into online data-based methods mainly represented by model-free adaptive control (MFAC), [9][10][11][12][13] offline data-based methods mainly represented by virtual reference feedback tuning (VRFT), 14,15 and iterative feedback tuning (IFT), 16,17 and online and offline data-based DDC method mainly represented by iterative learning control (ILC) [18][19][20] and adaptive dynamic programming (ADP).…”

Section: Introductionmentioning

confidence: 99%

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Model‐free adaptive control for a class of nonlinear systems with uniform quantizer

Hou

et al. 2020

Intl J Robust & Nonlinear

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The problem of model-free adaptive control (MFAC) design for a class of nonlinear systems with data quantization is considered in this article. Consider the case that the system output signal should be quantized before being transmitted to the controller. A MFAC algorithm with uniform quantizer is first proposed. As a result, the proposed MFAC algorithm cannot obtain a zero-tracking error because of the reduction of available information due to data quantization. To suppress the influence of data quantization, an improved MFAC algorithm with encoding and decoding mechanism is proposed. The improved design first encodes the system information and then transmits it through the network. Then, the controller receives the information and then decodes it to construct the MFAC algorithm. Theoretical result shows that the improved MFAC algorithm with encoding and decoding mechanism can obtain the aim of zero-tracking error. Finally, there have two examples to verify the effectiveness and applicability of the quantized MFAC algorithm design.

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Section: Theorem 1 ([9]mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Model‐free adaptive control for a class of nonlinear systems with uniform quantizer

Hou

et al. 2020

Intl J Robust & Nonlinear

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“…The fuzzy set theory originates from Zadeh [20] in 1965, which quantifies the uncertain parameter by the degree of occurrence. Though the fuzzy set theory applies to describing uncertain parameters, scholars usually focus on the control field's fuzzy logic theory [21]- [24], i.e., Takagi-Sugeno fuzzy control [25]- [28] and Mamdani-type fuzzy control [29], [30]. These if-then rules-based controls are designed based on the fuzzy logic reasoning, the fuzzy linguistic form of expert knowledge, and fuzzy mathematics.…”

Section: Introductionmentioning

confidence: 99%

Designing Robust Control for Permanent Magnet Synchronous Motor: Fuzzy Based and Multivariable Optimization Approach

et al. 2021

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This paper proposed a novel optimal robust control design approach to solve the uncertain parameter problem in the permanent magnet synchronous motor (PMSM) system by integrating the fuzzy set theory and system control theory. First, we adopt the prescribed fuzzy number to describe the timevarying but bounded uncertain parameter in the PMSM system. Second, based on the estimated bound of the uncertain parameter and the nominal dynamical model, we design a deterministic robust control scheme characterized by model-based control and feedback control. The control objective is to guarantee the PMSM system with uncertain parameters satisfies the desired motion equation with deterministic system performance: uniform boundedness (UB) and uniform ultimate boundedness (UUB). Third, using the fuzzy uncertain parameter, a performance index relevant to the steady-state performance and control cost is constructed. A parameters design optimization problem is formulated by minimizing the performance index, whose analytical solution is successfully solved and proved to always exists and is unique. As a result, the resulting PMSM system under the proposed control approach has two-level system performance: the guaranteed deterministic performance and optimal performance. Finally, the results of numerical simulation and experiment are presented for demonstration.

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“…The approximation function is obtained based on the intelligent controllers such as fuzzy logic and neural network [34,35] in which the uncertainty caused by both internal and external disturbances are approximated to either neural network or fuzzy logic without changing the mathematical model. Out of these, fuzzy logic needs rules for the estimation of disturbances caused by the system [36,37,38,39]. The complex rules of fuzzy logic create problems while updating the weight update law.…”

Section: Introductionmentioning

confidence: 99%

Real-Time Implementation of Adaptive Neuro Backstepping Controller for Maximum Power Point Tracking in Photo Voltaic Systems

et al. 2021

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The efficiency of the low-cost renewable energy source i.e. solar is very poor due to inadequate extraction of maximum power. By employing the proper maximum power point tracking algorithm, the efficiency can be increased. An innovative adaptive backstepping neural network controller is proposed in this paper to extract the maximum power from the solar panels by tracking the desired photovoltaic array voltage in real-time. The maximum power is extracted indirectly by tuning the PV voltage to the desired PV voltage where the maximum power is attained at the desired PV voltage point. The desired photovoltaic array voltage is obtained from the linear regression method. The change in photovoltaic current caused by varying irradiance and temperature is approximated using the Chebyshev polynomials. The quicker steady-state and transient responses are accomplished and the computational burden of the photovoltaic system control law is reduced because of the orthogonal property of Chebyshev polynomials. The asymptotically stable system is obtained by tuning the weights of the neurons in accordance with the Lyapunov stability analysis. Also, Lyapunov control function of the backstepping control design procedure finds a control law by an innovative cubic equation interpretation, instead of resolving the first derivative of the control law, that diminishes the ripples in the duty cycle to make its appropriateness in real-time. A prototype is developed to validate the robustness of this controller in maximum power extraction at a faster time and the results confirm that adaptive backstepping neural network controller surpasses the performances of conventional backstepping controller and constant voltage PID controller.

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Control Synthesis for Discrete-Time T–S Fuzzy Systems Based on Membership Function-Dependent $H_{\infty }$ Performance

Cited by 52 publications

References 31 publications

Model‐free adaptive control for a class of nonlinear systems with uniform quantizer

Model‐free adaptive control for a class of nonlinear systems with uniform quantizer

Designing Robust Control for Permanent Magnet Synchronous Motor: Fuzzy Based and Multivariable Optimization Approach

Real-Time Implementation of Adaptive Neuro Backstepping Controller for Maximum Power Point Tracking in Photo Voltaic Systems

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