An Adaptive Takagi–Sugeno Fuzzy Model-Based Predictive Controller for Piezoelectric Actuators

Cheng, Long; Liu, Weichuan; Hou, Zeng-Guang; Huang, Tingwen; Yu, Junzhi; Tan, Min

doi:10.1109/tie.2016.2644603

Cited by 108 publications

(42 citation statements)

References 35 publications

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“…Thanks to the universal approximation properties of fuzzy logic, fuzzy logic control techniques have been widely used for controlling unknown nonlinear systems with backlash-like hysteresis. [28][29][30][31][32][33][34] Su et al 28 deal with adaptive control of nonlinear dynamic systems preceded by unknown backlash-like hysteresis nonlinearities. A stable adaptive fuzzy control algorithm was developed without using hysteresis model inverse.…”

Section: Introductionmentioning

confidence: 99%

“…32 In the work of Liu et al, 33 an active dynamic surface control, to suppress regenerative chatter in micromilling system, was developed using fuzzy neural networks. The work presented by Cheng et al 34 describes an adaptive fuzzy model-based predictive controller for PEAs where the T-S fuzzy model was used to approximate the dynamics of PEAs.…”

Section: Introductionmentioning

confidence: 99%

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Enhanced adaptive control for a benchmark piezoelectric‐actuated system via fuzzy approximation

Yousef

Hamdy

Saleem

et al. 2019

Adaptive Control & Signal

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This paper investigates the problem of the high precision tracking control of piezoelectric actuators (PEAs) without using the inverse of the uncertain hysteresis. Based on fuzzy system approximator and particle swarm optimization (PSO) algorithm, a proposed enhanced  adaptive controller is developed. The proposed controller provides fast and robust adaptation simultaneously with guaranteed desired transient performance. Moreover, it has a simple form and requires fewer adaptation parameters. The adaptation gain is determined via PSO algorithm. The proposed controller is tested on a lab-scale PEA system.Experimental results with comparative studies with different techniques have been developed. The simulation results reveal that the proposed controller outperforms the other controllers in terms of normalized root-mean-square and maximum tracking errors for different frequencies.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Enhanced adaptive control for a benchmark piezoelectric‐actuated system via fuzzy approximation

Yousef

Hamdy

Saleem

et al. 2019

Adaptive Control & Signal

View full text Add to dashboard Cite

show abstract

“…However, the focus is put 2 Complexity fuzzy systems and neural networks. Especially, the study of fault-tolerant control (FTC) based on T-S fuzzy model for a class of nonlinear processes with failure [19][20][21][22][23][24] has attracted a lot of interests over the last few decades. However, the effect of time delay is not fully considered in these references.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy Predictive Fault‐Tolerant Control for Time‐Delay Nonlinear Systems with Partial Actuator Failures

Shi

et al. 2019

Complexity

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A fuzzy predictive fault-tolerant control (FPFTC) scheme is proposed for a wide class of discrete-time nonlinear systems with uncertainties, interval time-varying delays, and partial actuator failures as well as unknown disturbances, in which the main opinions focus on the relevant theory of FPFTC based on Takagi-Sugeno (T-S) fuzzy model description of these systems. The T-S fuzzy model represents the discrete-time nonlinear system in the form of the discrete uncertain time-varying delay state space, which is firstly constructed by a set of local linear models and the nonlinear membership functions. The novel improved state space model can be further obtained by extending the output tracking error to the constructed model. Then the fuzzy predictive fault-tolerant control law based on this extended model is designed, which can increase more control degrees of freedom. Utilizing Lyapunov-Krasovskill theory, less conservative delay-range-dependent stable conditions in terms of linear matrix inequality (LMI) constraints are given to ensure the asymptotically robust stability of closed-loop system. Meanwhile, the optimized cost function and H-infinity performance index are introduced to the stable conditions to guarantee the robust performance and antidisturbance capability. The simulation results on the temperature control of a strong nonlinear continuous stirred tank reactor (CSTR) show that the proposed control scheme is feasible and effective. on the control methods based on Takagi-Sugeno (T-S) fuzzy model [9,10]. In T-S fuzzy model, a set of local linear models are weighted by nonlinear membership functions in terms of IF-THEN rules to approximate a large class of nonlinear processes well [11]. In virtue of this, many mature linear theories are able to be applied fully to the stability analysis and control synthesis of nonlinear processes [12][13][14][15][16], which will make great progress in the advanced control theory.Due to the increasing demands for industrial products, the scale of industrial production is growing rapidly, which makes the industrial equipment operated under more complex environments. With the long-running industrial production, the failure may occur. If a failure cannot be coped with instantaneously in such environments through the suitable corrective action, it will make the control performance deteriorate and even expose the equipment and personnel to serious damage. Thus, fault-tolerant systems [17] and advanced process control algorithms [18] were studied to deal with failure using two soft computing approaches, i.e., Hindawi Complexity

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“…1 Throughout the past decades, many adaptive control design methods that are based on approximation for the uncertain nonlinear systems have been developed. 2 For example, related works [3][4][5][6][7][8][9][10][11] developed nonlinear systems via adaptive fuzzy control and other works [12][13][14][15][16][17][18][19][20][21][22][23][24][25] used the adaptive neural networks (NNs) to deal with the issues. The reason why the fuzzy logic systems (FLSs) and NNs are exploited to reply the uncertainty problem is that they possess preferable approximation ability to the nonlinear smooth functions.…”

Section: Introductionmentioning

confidence: 99%

Adaptive control for switched uncertain nonlinear systems with time‐varying output constraint and input saturation

Chen

Tang

Liu

2019

Adaptive Control & Signal

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This paper is concerned with adaptive tracking control for switched uncertain nonlinear systems, which contain the time-varying output constraint (TVOC) and input asymmetric saturation characteristic. In response to the unknown functions, the fuzzy logic systems are adopted. The controller is constructed by the backstepping technique. Based on the Tangent Barrier Lyapunov Function (BLF-Tan), an adaptive switched control scheme is designed. It is demonstrated that all signals in the resulted system are semiglobally uniformly ultimately bounded with TVOC under arbitrary switchings. Furthermore, the effectiveness of presented control method is validated via the simulation example. KEYWORDS input asymmetric saturation, switched uncertain nonlinear systems, Tangent Barrier Lyapunov Function, time-varying output constraint 1344

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An Adaptive Takagi–Sugeno Fuzzy Model-Based Predictive Controller for Piezoelectric Actuators

Cited by 108 publications

References 35 publications

Enhanced adaptive control for a benchmark piezoelectric‐actuated system via fuzzy approximation

Enhanced adaptive control for a benchmark piezoelectric‐actuated system via fuzzy approximation

Fuzzy Predictive Fault‐Tolerant Control for Time‐Delay Nonlinear Systems with Partial Actuator Failures

Adaptive control for switched uncertain nonlinear systems with time‐varying output constraint and input saturation

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