Robust adaptive multiple models based fuzzy control of nonlinear systems

Sofianos, Nikolaos A.; Boutalis, Yiannis S.

doi:10.1016/j.neucom.2015.09.047

Cited by 64 publications

(21 citation statements)

References 36 publications

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“…Based on Equations (2), (11), (16), (21) and 22, we can get the transform function of the main power consumption of the machine as…”

Section: Model Of Drive Systemmentioning

confidence: 99%

“…A traditional PID controller with the fixed-control gains cannot achieve the optimal control performance and is not adaptive to environment variations for the cold milling machine power control featured with a nonlinear and uncertain system [10,11]. There are several alternative control methods, such as neural network control [12][13][14][15], fuzzy control [16][17][18], robust control [19][20][21][22], and adaptive control [17][18][19][20]. The neural network control has strong self-study ability to control a nonlinear system, but it needs lots of computation.…”

Section: Introductionmentioning

confidence: 99%

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Practical Control of a Cold Milling Machine using an Adaptive PID Controller

Meng

Peng-yu

et al. 2020

Applied Sciences

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This paper presents a supervised Hebb learning single neuron adaptive proportional-integral-derivative (PID) controller for the power control of a cold milling machine. The proposed controller aims to overcome the deficiency of the current power control algorithm, and to achieve as high an output power as possible for the cold milling machine. The control process and system model are established and presented to provide the insight and guidance to the controller design and analysis. The adaptive PID controller is developed using a supervised Hebb learning single neuron method with detailed algorithm and structure analysis. The field test is performed to validate the proposed single neuron adaptive PID control for the power control. In the test, the 8 cm-depth milling is conducted on a cement concrete pavement in which the cement is not well-distributed. The test results show that when the machine speed is adjusted by the machine itself or manually without the adaptive power control system, the machine is often overloaded or underloaded, and the average work speed is 2.4m/min. However, when the adaptive control system is implemented on the machine, it works very close to its rated work condition during its work process. With the developed controller, the machine work speed is adjusted in time to the load variation and uncertain dynamics. The average machine work speed can reach up to 2.766 m/min, which is 15.25% higher than the wok speed of the machine without an adaptive power control system.

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“…Based on Equations (2), (11), (16), (21) and 22, we can get the transform function of the main power consumption of the machine as…”

Section: Model Of Drive Systemmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Practical Control of a Cold Milling Machine using an Adaptive PID Controller

Meng

Peng-yu

et al. 2020

Applied Sciences

View full text Add to dashboard Cite

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“…However, the above documents only solve the control problem in unrestricted‐time convergence. In the view of some engineering applications, 36‐43 several control systems need to satisfy finite‐time performance 41‐43 …”

Section: Introductionmentioning

confidence: 99%

Adaptive fuzzy finite‐time fault‐tolerant control of nonlinear systems with state constraints and input quantization

Pan

Yang

et al. 2020

Adaptive Control & Signal

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Summary This article concentrates on an adaptive finite‐time fault‐tolerant fuzzy tracking control problem for nonstrict feedback nonlinear systems with input quantization and full‐state constraints. By utilizing the fuzzy logic systems and less adjustable parameters method, the unknown nonlinear functions are addressed in each step process. In addition, a dynamic surface control technique combined with fuzzy control is introduced to tackle the variable separation problem. The problem for the effect of quantization and unlimited number of actuator faults is tackled by a damping term with smooth function in the intermediate control law. Finite‐time stability is achieved by combining barrier Lyapunov functions and backstepping method. The finite‐time controller is designed such that all the responses of the systems are semiglobal practical finite‐time stable and ensured to remain in the predefined compact sets while tracking error converges to a small neighborhood of the origin in finite time. Finally, simulation examples are utilized to testify the validity of the investigated strategy.

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“…On account of the extensively practical applications, studies on adaptive trajectory tracking control design for uncertain nonlinear systems have made a major breakthrough, such as adaptive control, sliding mode control, robust control, fault tolerant control, and so on. In addition, backstepping‐based adaptive neural control and fuzzy control for nonlinear systems have been well studied in the past few years, in which fuzzy logic systems (FLSs) or neural networks (NNs) were viewed as universal approximators to identify the uncertain system nonlinearities, and then, an adaptive controller was designed by combining with the backstepping technique.…”

Section: Introductionmentioning

confidence: 99%

Asymptotic tracking control for constrained nonstrict‐feedback MIMO nonlinear systems via parameter compensations

Pan

Chadli

et al. 2020

Intl J Robust & Nonlinear

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This paper studies the problem of adaptive fuzzy asymptotic tracking control for multiple input multiple output nonlinear systems in nonstrict-feedback form.Full state constraints, input quantization, and unknown control direction are simultaneously considered in the systems. By using the fuzzy logic systems, the unknown nonlinear functions are identified. A modified partition of variables is introduced to handle the difficulty caused by nonstrict-feedback structure. In each step of the backstepping design, the symmetric barrier Lyapunov functions are designed to avoid the breach of the state constraints, and the issues of overparametrization and unknown control direction are settled via introducing two compensation functions and the property of Nussbaum function, respectively. Furthermore, an adaptive fuzzy asymptotic tracking control strategy is raised.Based on Lyapunov stability analysis, the developed control strategy can effectually ensure that all the system variables are bounded, and the tracking errors asymptotically converge to zero. Eventually, simulation results are supplied to verify the feasibility of the proposed scheme. K E Y W O R D Sadaptive fuzzy control, asymptotic tracking control, nonstrict-feedback system, state constraints, unknown control direction INTRODUCTIONOn account of the extensively practical applications, studies on adaptive trajectory tracking control design for uncertain nonlinear systems have made a major breakthrough, such as adaptive control, 1-3 sliding mode control, 4-6 robust control, 7-9 fault tolerant control, 10,11 and so on. In addition, backstepping-based adaptive neural control 12-18 and fuzzy control [19][20][21][22][23][24][25][26][27][28][29][30] for nonlinear systems have been well studied in the past few years, in which fuzzy logic systems (FLSs) or neural networks (NNs) were viewed as universal approximators to identify the uncertain system nonlinearities, and then, an adaptive controller was designed by combining with the backstepping technique. Cite several examples, based on FLS's online approximation capability, a new adaptive fuzzy control scheme was presented in Reference 20. Tong et al citebib21 established a fuzzy state observer and proposed a decentralized adaptive fuzzy optimal control approach for nonlinear large-scale systems. In Reference 22, the fuzzy adaptive output feedback controller was constructed for strict-feedback nonlinear systems, where the problem of virtual control coefficients was well handled by using a convex combination technique. Int J Robust Nonlinear Control. 2020;30:3365-3381. wileyonlinelibrary.com/journal/rnc

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Robust adaptive multiple models based fuzzy control of nonlinear systems

Cited by 64 publications

References 36 publications

Practical Control of a Cold Milling Machine using an Adaptive PID Controller

Practical Control of a Cold Milling Machine using an Adaptive PID Controller

Adaptive fuzzy finite‐time fault‐tolerant control of nonlinear systems with state constraints and input quantization

Asymptotic tracking control for constrained nonstrict‐feedback MIMO nonlinear systems via parameter compensations

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