Constant Force PID Control for Robotic Manipulator Based on Fuzzy Neural Network Algorithm

Zhu, Dachang; Du, B. X.; Puchen, Zhu; Chen, Shouyan

doi:10.1155/2020/3491845

Cited by 22 publications

(13 citation statements)

References 39 publications

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“…Apart from that, uncertainties in the system model, such as external disturbances, parameter uncertainty, and nonlinear frictions, constantly exist and cause the unstable performance of the robotic system [3]. In the literature, several approaches have been proposed for controlling the robotic systems, such as sliding mode control (SMC) [4] , H-infinity (H ∞ ) control [5], optimal control [6], PID control [7], adaptive control [8], model predictive VOLUME 4, 2016 control (MPC) [9], and other nonlinear controls reported in [10]- [12]. One of the main issues impeding the fast-tracking behavior of robotic manipulators is friction, resulting in steady-state tracking inaccuracy [13].…”

Section: Introductionmentioning

confidence: 99%

Adaptive FIT-SMC Approach for an Anthropomorphic Manipulator With Robust Exact Differentiator and Neural Network-Based Friction Compensation

et al. 2022

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In robotic manipulators, feedback control of nonlinear systems with fast finite-time convergence is desirable. However, because of the parametric and model uncertainties, the robust control and tuning of the robotic manipulators pose many challenges related to the trajectory tracking of the robotic system. This research proposes a state-of-the-art control algorithm, which is the combination of fast integral terminal sliding mode control (FIT-SMC), robust exact differentiator (RED) observer, and feedforward neural network (FFNN) based estimator. Firstly, the dynamic model of the robotic manipulator is established for the n degrees of freedom (DoFs) system by taking into account the dynamic LuGre friction model. Then, a FIT-SMC with friction compensation-based nonlinear control has been proposed for the robotic manipulator. In addition, a RED observer is developed to get the estimates of robotic manipulator joints' velocities. Since the dynamic friction state of the LuGre friction model is unmeasurable, FFNN is established for training and estimating the friction torque. The Lyapunov method is presented to demonstrate the finite-time sliding mode enforcement and state convergence for a robotic manipulator. The proposed control approach has been simulated in the MATLAB/Simulink environment and compared with the system with no observer to characterize the control performance. Simulation results obtained with the proposed control strategy affirm its effectiveness for a multi-DoF robotic system with model-based friction compensation having an overshoot and a settling time less than 1.5% and 0.2950 seconds, respectively, for all the joints of the robotic manipulator.INDEX TERMS robotic manipulator, robust exact differentiator, feedforward neural network, fast integral sliding mode control, LuGre friction model, autonomous articulated robotic educational platform.

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Section: Introductionmentioning

confidence: 99%

Adaptive FIT-SMC Approach for an Anthropomorphic Manipulator With Robust Exact Differentiator and Neural Network-Based Friction Compensation

et al. 2022

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“…e classical PID controller is employed for the majority of industrial applications. But the position response offered for nonlinear systems is not optimum [3]. For example, in the case of motor speed control, employing a PID controller increases noise and consequently impacts its application in industries.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy PI Control of Trapezoidal Back EMF Brushless DC Motor Drive Based on the Position Control Optimization Technique

Mary¹,

Kumar²,

Joseph

et al. 2022

Mathematical Problems in Engineering

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In this article, a novel methodology has been created based on optimized fuzzy PI controller using position control optimization (PCO). This proposed PCO algorithm ensures better control of brushless direct current motor (BLDCM) in order to reduce steady-state error and oscillation as well as improve the system response by optimizing the speed and position of rotor. The suitability of this technique has been simulated in different stages, so as to infer the optimization in results. The main motivation of this paper is to get better efficiency and optimum response from BLDCM, by comparing its performance characteristics with existing methods. The detailed investigation research through simulation is carried out using MATLAB/Simulink. However, in order to obtain well-defined results, the PCO technique was applied and the performance obtained was highly optimized than previously applied techniques. Through the simulations, the proposed approach can outperform the existing approaches such as PSO and moth-flame algorithm (MFA).

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“…Combination of fuzzy logic-based ensemble with multi neural networks belongs to one of useful application of fuzzy neural networks. Moreover, there exist numerous applications with the advantage of FNNs: force control of robot manipulators [14], biomedical computing [15], road lane prediction [16], and so on [17]- [20]. Therefore, FNNs have been received a lot of attentions during last decades.…”

Section: Introductionmentioning

confidence: 99%

Affine Memory Control for Synchronization of Delayed Fuzzy Neural Networks

et al. 2021

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This paper deals with the synchronization of fuzzy neural networks (FNNs) with time-varying delays. FNNs are more complicated form of neural networks incorporated with fuzzy logics, which provide more powerful performances. Especially, the problem of delayed FNNs's synchronization is of importance in the existence of the network communication. For the synchronization of FNNs with time-varying delays, a novel form of control structure is proposed employing affinely transformed membership functions with memory element. In accordance with affine memory control, appropriate Lyapunov-Krasovskii functional is chosen to design control gain, guaranteeing stability of the systems with delays. Exploiting the more general type of control attributed by affine transformation and memory-type, a novel criterion is derived in forms of linear matrix inequalities (LMIs). As a results, the effectiveness of the proposed control is shown through numerical examples by comparisons with others.

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Constant Force PID Control for Robotic Manipulator Based on Fuzzy Neural Network Algorithm

Cited by 22 publications

References 39 publications

Adaptive FIT-SMC Approach for an Anthropomorphic Manipulator With Robust Exact Differentiator and Neural Network-Based Friction Compensation

Adaptive FIT-SMC Approach for an Anthropomorphic Manipulator With Robust Exact Differentiator and Neural Network-Based Friction Compensation

Fuzzy PI Control of Trapezoidal Back EMF Brushless DC Motor Drive Based on the Position Control Optimization Technique

Affine Memory Control for Synchronization of Delayed Fuzzy Neural Networks

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