An improved cerebellar model articulation controller based on the compound algorithms of credit assignment and optimized smoothness for a three-axis inertially stabilized platform

Zhou, Xiangyang; Li, Yating; Yue, Haixiao; Yuan, Jia; Zhao, Libo; Zhu, Zhuangsheng

doi:10.1016/j.mechatronics.2018.06.001

Cited by 9 publications

(9 citation statements)

References 30 publications

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“…A new fuzzy hypercube is generated in the structural learning scheme. The firing strength in Layer 3 is used as the degree measure after a product operation: 7The maximum degree measure Smax is determined as (8) where N is the current number of fuzzy hypercube cells. Here, the parameter of a prespecified threshold is defined.…”

Section: Structure Learning Schemementioning

confidence: 99%

“…A corresponding relationship is present between the input and output of the mapping. The CMAC has a highly standardized computational structure, fast network learning, local generalization, and fast convergence [5][6][7][8][9]. However, because of its constant response and quantified receptive fields, the approximation capacity of the CMAC model is limited.…”

Section: Introductionmentioning

confidence: 99%

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Dynamic System Identification and Prediction Using a Self-Evolving Takagi–Sugeno–Kang-Type Fuzzy CMAC Network

Lin

Jhang

2020

Electronics

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This study proposes a Self-evolving Takagi-Sugeno-Kang-type Fuzzy Cerebellar Model Articulation Controller (STFCMAC) for solving identification and prediction problems. The proposed STFCMAC model uses the hypercube firing strength for generating external loops and internal feedback. A differentiable Gaussian function is used in the fuzzy hypercube cell of the proposed model, and a linear combination function of the model inputs is used as the output of the proposed model. The learning process of the STFCMAC is initiated using an empty hypercube base. Fuzzy hypercube cells are generated through structure learning, and the related parameters are adjusted by a gradient descent algorithm. The proposed STFCMAC network has some advantages that are summarized as follows: (1) the model automatically selects the parameters of the memory structure, (2) it requires few fuzzy hypercube cells, and (3) it performs identification and prediction adaptively and effectively.

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Section: Structure Learning Schemementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Dynamic System Identification and Prediction Using a Self-Evolving Takagi–Sugeno–Kang-Type Fuzzy CMAC Network

Lin

Jhang

2020

Electronics

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“…At present, the commonly used friction models include Coulomb friction, viscous friction and Stribeck friction [1][2][3][4][5][6][7], which are not accurate enough to describe the friction process. The LuGre friction model [8][9][10][11][12][13] is a perfect dynamic friction model proposed by Cannudas et al, which can more accurately describe the complex dynamic and static characteristics in the friction process, such as sliding displacement, friction hysteresis, variable static friction, creep and Stribeck effect, etc. It is widely used in servo systems, automobile industry, etc.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy neural network control for mechanical arm based on adaptive friction compensation

Yang¹,

Sai-yan²,

Huang³

2020

J. vibroeng.

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When tracking the trajectory of the mechanical arm in a joint space, the system is affected by friction non-linearity, unknown dynamic parameters and external disturbances that makes it difficult to improve the control accuracy of the mechanical arm. To solve the above problems, this paper introduces LuGre friction model and designs a new joint space trajectory tracking controller based on the adaptive fuzzy neural network. The controller is capable to make the adaptive adjustment of the center and width of the basis function, can approach the nonlinear link having the LuGre friction on line, and uses the sliding mode control term to reduce the approximation error. The introduction of LuGre model into the mechanical arm system can more truly simulate the friction link of the system, which is of great significance to the high precision control of the mechanical arm. The Lyapunov method is used to prove the stability of the closedloop system. The simulation results show that the designed adaptive fuzzy neural network can effectively compensate the non-linear links including friction without precise system parameters, and the controller has strong robustness to load changes, thus realizing high-precision trajectory tracking of the mechanical arm in joint space.

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“…To complete the disturbance estimation and input saturation analysis, it is also necessary to design robust controllers to complete the control of the system. Many controllers are designed for the stabilized platform, such as proporitional integral derivative(PID) controller [35], continuous finite-time sliding mode controller (FTSMC) [36], and improved cerebellar model articulation controller [37]. In recent years, because of its better performance, such as rapid response, insensitiveness of the disturbance, the sliding mode controller has been widely used in many applications.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Nonlinear Control for the Stabilized Platform With Disturbance and Input Saturation

Zhang

Guan

et al. 2020

IEEE Access

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This paper proposes a control strategy including super-twisting extended state observer (STESO) and sliding mode controller based on backstepping technique for the stabilized platform with disturbance and input saturation. Firstly, the system is modelled to obtain a non-matched nonlinear model. Secondly, two STESOs are designed to estimate the disturbances in the matched channel and the unmatched channel. And then, multi-sliding mode controller based on backstepping technique is designed for non-matched nonlinear system; and the first-order auxiliary system is introduced to alleviate the effect of the input saturation. Furthermore, Lyapunov function is used to prove the stability of the closed system with the proposed controller and finite-time convergence proof is given in this paper. Finally, comparative simulations are carried out and the results show the effectiveness of the proposed controller. It is proved that the control accuracy is improved effectively and the proposed controller can guarantee the consistent tracking error of the system.

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An improved cerebellar model articulation controller based on the compound algorithms of credit assignment and optimized smoothness for a three-axis inertially stabilized platform

Cited by 9 publications

References 30 publications

Dynamic System Identification and Prediction Using a Self-Evolving Takagi–Sugeno–Kang-Type Fuzzy CMAC Network

Dynamic System Identification and Prediction Using a Self-Evolving Takagi–Sugeno–Kang-Type Fuzzy CMAC Network

Fuzzy neural network control for mechanical arm based on adaptive friction compensation

Adaptive Nonlinear Control for the Stabilized Platform With Disturbance and Input Saturation

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