Use of a recurrent neural network in discrete sliding-mode control

Fang, Yong; Chow, Tommy W. S.; Li, X.D.

doi:10.1049/ip-cta:19990376

Cited by 58 publications

(31 citation statements)

References 11 publications

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“…Earlier backpropogation NN [33,34] has been used with SMC. These NN are time consuming controllers and hence authors in [35][36][37][38] used radial bias neural network (RBNN) with SMC resulting in reduced chattering and tracking error and reduced execution time. Summarizing the above discussion:…”

Section: Introductionmentioning

confidence: 99%

Sliding Mode Control (SMC) of Robot Manipulator via Intelligent Controllers

Kapoor

Ohri

2016

J. Inst. Eng. India Ser. B

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Inspite of so much research, key technical problem, naming chattering of conventional, simple and robust SMC is still a challenge to the researchers and hence limits its practical application. However, newly developed soft computing based techniques can provide solution. In order to have advantages of conventional and heuristic soft computing based control techniques, in this paper various commonly used intelligent techniques, neural network, fuzzy logic and adaptive neuro fuzzy inference system (ANFIS) have been combined with sliding mode controller (SMC). For validation, proposed hybrid control schemes have been implemented for tracking a predefined trajectory by robotic manipulator, incorporating structured and unstructured uncertainties in the system. After reviewing numerous papers, all the commonly occurring uncertainties like continuous disturbance, uniform random white noise, static friction like coulomb friction and viscous friction, dynamic friction like Dhal friction and LuGre friction have been inserted in the system. Various performance indices like norm of tracking error, chattering in control input, norm of input torque, disturbance rejection, chattering rejection have been used. Comparative results show that with almost eliminated chattering the intelligent SMC controllers are found to be more efficient over simple SMC. It has also been observed from results that ANFIS based controller has the best tracking performance with the reduced burden on the system. No paper in the literature has found to have all these structured and unstructured uncertainties together for motion control of robotic manipulator.Keywords Non-linear system Á Sliding mode control (SMC) Á Heuristic intelligent controllers Á Neural network (NN) Á Fuzzy logic (FL) and adaptive neuro fuzzy inference system (ANFIS)

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Section: Introductionmentioning

confidence: 99%

Sliding Mode Control (SMC) of Robot Manipulator via Intelligent Controllers

Kapoor

Ohri

2016

J. Inst. Eng. India Ser. B

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“…Since sliding mode control was introduced in 1977, many useful approaches such as the transformation matrix method (Ütkin and Young, 1979), eigenstructure assignment method (Elghezawi et al, 1983), Lyapunov approach (Su et al, 1996), virtual eigenvalue method (Chang, 1999), fuzzy sliding surface (Lee et al, 1998), time-variant sliding surface (Kim et al, 1993), neural network method (Fang et al, 1999), and quadratic minimization method (Dorling and Zinober,1986) have been proposed to achieve effective sliding surfaces and switching law designs. Among these applications, only a very few, such as the quadratic minimization method proposed by Dorling and Zinober, are optimization based methods.…”

Section: Introductionmentioning

confidence: 99%

Discrete sliding mode controller design based on the LQR suboptimal approach with application on ac servo motor

Yu¹,

Wang

2006

Journal of the Chinese Institute of Engineers

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A systematic and simple discrete sliding mode controller design scheme based on the suboptimal approach is presented. The behaviors of the control system can be determined through a preferred performance index. The AC servomotor position control is obtained using only the q-axis voltage control loop. The proposed method is simulated and experimented with to verify the capability of this new sliding mode control algorithm. Properties such as easy implementation, fast response, and robustness with relation to external loads are demonstrated.

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“…In [19], the DTSMC task is studied for discrete time input-output models, and in [20], the design based on the Euler discretization is analyzed. Aside from the approaches analyzing the state space representations, a number of studies have demonstrated that NNs can successfully be used in DTSMC systems [21,22,23].…”

Section: Introductionmentioning

confidence: 99%

Discrete time neuro sliding mode control with a task-specific output error

Efe

2004

Neural Comput & Applic

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The problem of obtaining the error at the output of a neuro sliding mode controller is analyzed in this paper. The controller operates in discrete time and the method presented describes an error measure that can be used if the task to be achieved is to drive the system under control to a predefined sliding regime. Once the task-specific output error is calculated, the neurocontroller parameters can be tuned so that the task is achieved. The paper postulates the strategy for discrete time representation of uncertain nonlinear systems belonging to a particular class. The performance of the proposed technique has been clarified on a third order nonlinear system, and the parameters of the controller are adjusted by using the error backpropagation algorithm. It is observed that the prescribed behavior can be achieved with a simple network configuration.

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Use of a recurrent neural network in discrete sliding-mode control

Cited by 58 publications

References 11 publications

Sliding Mode Control (SMC) of Robot Manipulator via Intelligent Controllers

Sliding Mode Control (SMC) of Robot Manipulator via Intelligent Controllers

Discrete sliding mode controller design based on the LQR suboptimal approach with application on ac servo motor

Discrete time neuro sliding mode control with a task-specific output error

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