Backstepping Control Design of a Single-Link Flexible Robotic Manipulator

Huang, Jhih-Wei; Lin, Jung-Shan

doi:10.3182/20080706-5-kr-1001.01994

Cited by 20 publications

(9 citation statements)

References 16 publications

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“…Since the control problem of single-link robotic manipulator widely exist in industry and engineering fields, the investigation on single-link robotic manipulator has caused quite a lot of attention during the past two decades. So far, various interesting control approaches have been developed, such as sliding control [1]- [4], backstepping technique [5], [6] and intelligent control method [7]- [16]. Backstepping design method has been considered as one of the most popular and effective control methods for non-linear systems with strict feedback form, i.e.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Neural Command Filtered Tracking Control for Flexible Robotic Manipulator With Input Dead-Zone

Wang

Kang

2019

IEEE Access

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In this paper, an adaptive neural network (NN) command filtered tracking control method is developed for a flexible robotic manipulator with dead-zone input. To deal with the input dead-zone nonlinearity, it is viewed as a combination of a linear part and bounded disturbance-like term. The Neural networks (NNs) are used to estimate the uncertain nonlinearities appeared in the control system. By using the command filter technique, the problem of 'explosion of complexity' is overcome. The proposed controller guarantees that all the closed-loop signals are bounded and the system output can track the given reference signal. The simulation results are provided to demonstrate the effectiveness of the proposed controller. INDEX TERMS Adaptive neural network control, robotic manipulator, dead-zone, command-filter technique, backstepping.

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Section: Introductionmentioning

confidence: 99%

Adaptive Neural Command Filtered Tracking Control for Flexible Robotic Manipulator With Input Dead-Zone

Wang

Kang

2019

IEEE Access

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“…In the past few decades, lots of efforts have been made on the research of FJR systems. Based on the model of FJR presented in [1], multifarious nonlinear control methods are presented such as backstepping method [2][3][4], sliding-mode control [5][6][7][8], switching control [9], fuzzy control [10], and neural network control [11,12]. In consideration of the problem caused by the inherent structure of FJR under practical circumstance, such as friction, time delay, and variable stiffness, some researchers proposed effective strategies to solve such problem [13][14][15].…”

Section: Introductionmentioning

confidence: 99%

Neural Learning Control of Flexible Joint Manipulator with Predefined Tracking Performance and Application to Baxter Robot

Wang

2017

Complexity

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This paper focuses on neural learning from adaptive neural control (ANC) for a class of flexible joint manipulator under the output tracking constraint. To facilitate the design, a new transformed function is introduced to convert the constrained tracking error into unconstrained error variable. Then, a novel adaptive neural dynamic surface control scheme is proposed by combining the neural universal approximation. The proposed control scheme not only decreases the dimension of neural inputs but also reduces the number of neural approximators. Moreover, it can be verified that all the closed-loop signals are uniformly ultimately bounded and the constrained tracking error converges to a small neighborhood around zero in a finite time. Particularly, the reduction of the number of neural input variables simplifies the verification of persistent excitation (PE) condition for neural networks (NNs). Subsequently, the proposed ANC scheme is verified recursively to be capable of acquiring and storing knowledge of unknown system dynamics in constant neural weights. By reusing the stored knowledge, a neural learning controller is developed for better control performance. Simulation results on a single-link flexible joint manipulator and experiment results on Baxter robot are given to illustrate the effectiveness of the proposed scheme.

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“…Due to the robust performance, integrator backstepping control has been applied to many nonlinear systems successfully, such as single link flexible manipulator [2] and multiple link rigid manipulator [3], etc. In addition, in [4], the backstepping control scheme has been used for the control of work platform of folding-boom aerial platform vehicle effectively.…”

Section: I、introductionmentioning

confidence: 99%

“…Therefore, in the literature [1], the beams are seen as flexible and the dynamics equations of the arm system of folding-boom aerial platform vehicle are set up based on flexible multi-body dynamics theory and Lagrange's equation. The establishment of the model lays foundation of the research of steady movement and accurate positioning of work platform.Due to the robust performance, integrator backstepping control has been applied to many nonlinear systems successfully, such as single link flexible manipulator [2] and multiple link rigid manipulator [3], etc. In addition, in [4], the backstepping control scheme has been used for the control of work platform of folding-boom aerial platform vehicle effectively.…”

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confidence: 99%

Design of Neural Network-based Backstepping Controller for the Folding-Boom Aerial Platform Vehicle

Hu¹,

Ren²,

Liu³

2015

Proceedings of the 2015 International Conference on Mechatronics, Electronic, Industrial and Control Engineering

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Abstract-In this paper, the robust trajectory tracking problem is addressed for the work platform of folding-boom aerial platform vehicle in the presence of uncertainties and disturbances. The control objective is to make the work platform move along a desired reference trajectory and make the vibration inhibit at the same time. Since neural network system can approximate any nonlinear function with arbitrary accuracy over a compact set in the light of the universal approximation theorem, a neural network-based backstepping controller, which composed of backstepping control and neural network, is proposed for the trajectory tracking control of the work platform in the case of modeling uncertainties and disturbances. According to Lyapunov stability theorem, the stability and convergence of the overall system can be guaranteed by the derived control law. In addition, simulation results demonstrate that the proposed controller is effective for suppressing the vibration and reducing trajectory tracking error of the work platform. Keywords-aerial platform vehicle; model uncertainties; trajectory tracking control; backstepping controller; neural network I、INTRODUCTIONFolding-boom aerial platform vehicle is a kind of device which can lift people to the height for installation or maintenance [1], the scheme of which is shown in Figure.1.As it requires vey high safety, both the stability of movement and the accuracy of positioning of work platform should be guaranteed.Since the light-long beam is widely used in the arm system, the influence of elastic deformations of beam should be considered. Therefore, in the literature [1], the beams are seen as flexible and the dynamics equations of the arm system of folding-boom aerial platform vehicle are set up based on flexible multi-body dynamics theory and Lagrange's equation. The establishment of the model lays foundation of the research of steady movement and accurate positioning of work platform.Due to the robust performance, integrator backstepping control has been applied to many nonlinear systems successfully, such as single link flexible manipulator [2] and multiple link rigid manipulator [3], etc. In addition, in [4], the backstepping control scheme has been used for the control of work platform of folding-boom aerial platform vehicle effectively. However, this method can only be used for the accurate dynamic model of the arm system of folding boom aerial platform vehicle.In fact, there exist various uncertainties due to external disturbances and approximation of the modeling [5,6]. As a result, a robust adaptive control scheme is developed for a class of uncertain nonlinear systems by the combination of backstepping control method and fuzzy control method in [7]. Moreover, as neural network can be used to approximate any nonlinear function over a compact set with arbitrary accuracy [8], it has attracted a wide spread attention. In [9,10], the combination of backstepping design and neural network has been used in the control of uncertain nonlinear systems.In this paper,...

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Backstepping Control Design of a Single-Link Flexible Robotic Manipulator

Cited by 20 publications

References 16 publications

Adaptive Neural Command Filtered Tracking Control for Flexible Robotic Manipulator With Input Dead-Zone

Adaptive Neural Command Filtered Tracking Control for Flexible Robotic Manipulator With Input Dead-Zone

Neural Learning Control of Flexible Joint Manipulator with Predefined Tracking Performance and Application to Baxter Robot

Design of Neural Network-based Backstepping Controller for the Folding-Boom Aerial Platform Vehicle

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