Neural network sliding mode robot control

Jezernik, K.; Rodič, M.; Šafarič, Riko; Curk, Boris

doi:10.1017/s0263574797000040

Cited by 55 publications

(15 citation statements)

References 18 publications

(10 reference statements)

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“…In [26] a neuro-sliding-mode control approach is presented where two neural networks (NN) in parallel are used to realize the equivalent control and the corrective control terms of the SMC. The use of an NN for the calculation of the equivalent control term is also proposed by Jezernik, Rodic, Safaric, and Curket [27]. Ramirez and Morles [28] propose a dynamical sliding mode control approach for robust adaptive learning in analog adaptive linear elements (Adalines).…”

Section: Fnnsmc Designmentioning

confidence: 99%

Fuzzy neural network sliding mode control for long delay time systems based on fuzzy prediction

2007

Neural Comput Applic

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Delay time, which may degrade the control performance, is frequently encountered in various control processes. The fuzzy neural network sliding mode controller (FNNSMC), which incorporates the fuzzy neural network (FNN) with the sliding mode controller (SMC), is developed to control the long delay system with unknown model based on fuzzy prediction algorithm in the paper. According to the characteristics of the long delay systems, we simulate the manual operating process and predict the delayed error and its derivative based on the information of the input and output variables of the process, and then feedback these prediction values to the FNN and train the FNN with the regulation function by the idea of sliding mode control until the better control results are obtained. The FNNSMC has more robustness due to the abilities of the learning and reasoning and can eliminate the drawbacks of the general SMC, namely the chattering in the control signal and the needing knowledge of the bounds of the disturbances and uncertainties. Simulation examples demonstrate the advantages of the proposed control scheme.

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Section: Fnnsmc Designmentioning

confidence: 99%

Fuzzy neural network sliding mode control for long delay time systems based on fuzzy prediction

2007

Neural Comput Applic

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“…SMC is a very effective approach for the solution of the problem due to its robustness to parameter variations, easy implementation, fast dynamic response, and disturbance rejection [18]. A comprehensive review of SMC was presented in [19][20][21]. It has been widely reported that SMC exhibits unwanted motion, called chattering.…”

Section: Introductionmentioning

confidence: 99%

Real-time control of an automated guided vehicle using a continuous mode of sliding mode control

Soysal¹

2014

Turk J Elec Eng & Comp Sci

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This paper proposes a tracking control method for a certain type of differential-drive wheeled mobile robot, called automated guided vehicles (AGVs), using a continuous mode approach of sliding mode control (SMC). The SMC applied produces a continuous signal in between the U and -U control signals instead of discrete ones. The controller is applied to control the velocity and direction angle of the vehicle in order to keep it on a desired path. The obtained algorithm is applied to the experimental system under load and disturbance to show its robustness. The experimental results are satisfactory and verify the performance of the control algorithm.

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“…NNs provide a good candidate for the computation of the equivalent control of such partly known or uncertain systems. In Kim and Oh (1995), Jezernik et al (1997), Ertugrul and Kaynak (2000) and Tsai et al (2004), neuro-sliding-mode-control methods were addressed. In those methods, NNs were used to calculate the equivalent control.…”

Section: Introductionmentioning

confidence: 99%

Neuro-hierarchical sliding mode control for a class of under-actuated systems

Qian

Liu

et al. 2011

IJMIC

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A neuro-hierarchical sliding mode controller is presented for a class of under-actuated systems with a stable equilibrium point. Such controller is combined with the concept of neural networks and the methodology of hierarchical sliding mode control. At first, the hierarchical sliding mode control law is designed for the class as follows. The system is divided into several subsystems and the sliding surface of every subsystem is defined. Then, the sliding surface of one subsystem is selected as the first layer sliding surface. The first layer sliding surface is then to construct the second layer sliding surface with the sliding surface of another subsystem. This process continues until the sliding surfaces of all the subsystems are included. The control law is derived from Lyapunov theorem. By aiming at unknown factors and uncertainties, the neural networks are designed to approximate the terms of the hierarchical sliding mode control law. The asymptotic stability of the entire sliding surfaces and the convergence of the network weights are proven theoretically. Simulation and physical experiment results show the controller's validity and robustness.

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Neural network sliding mode robot control

Cited by 55 publications

References 18 publications

Fuzzy neural network sliding mode control for long delay time systems based on fuzzy prediction

Fuzzy neural network sliding mode control for long delay time systems based on fuzzy prediction

Real-time control of an automated guided vehicle using a continuous mode of sliding mode control

Neuro-hierarchical sliding mode control for a class of under-actuated systems

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