Robust model-free control for redundant robotic manipulators based on zeroing neural networks activated by nonlinear functions

Tan, Ning; Yu, Peng

doi:10.1016/j.neucom.2021.01.093

Cited by 29 publications

(10 citation statements)

References 23 publications

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“…The dynamics of the ith follower (1) is in a non-affine form, and it is more general compared with the plant in [44]. In fact, the dynamics of the ith follower (1) represent many practical systems, for instance, robotic manipulators [45], hypersonic flight vehicles [46] and marine surface vessels [47]. Consequently, the control strategy for an MAS with follower (1) is not only for academic improvement but also for these systems in the real world.…”

Section: Remarkmentioning

confidence: 99%

Fixed-time Active Disturbance Rejection Consensus Tracking Control of A Multi-Agent System via Event-Triggered Mechanism

Yang

Zhang

2022

Preprint

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A fixed-time active disturbance rejection control (FTADRC) consensus tracking strategy is proposed for a class of non-affine nonlinear multi-agent systems with an event-trigger-based communication. Non-affine followers are transformed into affine ones by combining the implicit function theorem with the mean value theorem. A distributed event-triggered estimator is introduced based on its neighbor output information. It is for estimation of a leader’s signal for parts of followers, who are not able to access the leader signal in a direct manner. A distributed FTADRC control strategy is then developed via an event-triggered communication in the framework of backstepping technology. With the help of the fixed-time control, the settling time of an MAS is assignable and independent on initial conditions. Extended state observers and tracking differentiators are employed to compensate unknown dynamics of each follower in real time and estimate derivatives of virtual control laws, respectively. It is proven theoretically that the MAS achieves input-to-state practically stability and the consensus tracking error converges to a neighborhood around the origin in a fixed time. Also, Zeno behavior is excluded. Finally, two examples are performed to illustrate the effectiveness of the proposed strategy.

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Section: Remarkmentioning

confidence: 99%

Fixed-time Active Disturbance Rejection Consensus Tracking Control of A Multi-Agent System via Event-Triggered Mechanism

Yang

Zhang

2022

Preprint

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“…Define the error e 2 = x 2 − β 1 (5) where e 2 = e 21 e 22 e 23 T . In order to make error e 2 approach 0.…”

Section: Design Of Bs-rbfnn Signal Controllermentioning

confidence: 99%

“…For some parameters that change real-time, in order to make the robot system have better tracking effect, an adaptive control method was proposed [4]. With the development of science and technology, some intelligent control algorithms were applied to the control system, such as neural network [5], fuzzy [6], etc. The intelligent control algorithm further improves the control effect of the robot.…”

Section: Introductionmentioning

confidence: 99%

Cooperative Control of Backstepping Neural Network and Port-Controlled Hamiltonian for Robot System

Shang

et al. 2021

Proceedings of 2021 Chinese Intelligent Automation Conference

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It is difficult for a single control method to track the position quickly and accurately at the end of the robot. To solve this problem, a cooperative control strategy of backstepping adaptive neural network (BS-RBFNN) signal control and port controlled Hamiltonian (PCH) energy control is proposed. BS-RBFNN solves the problem of rapidity when the system is in dynamic state, PCH control solves the problem of accuracy when the system is in steady state. The cooperative function based on the error is designed, and the cooperative control of the robot joint system is realized by using this function. The robot joint position servo system can not only realize the rapid adjustment of the dynamic position, but also realize the high precision tracking control in the steady state. Simulation results show that the system achieves fast dynamic response and accurate steady-state position tracking by using cooperative control method.

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“…erefore, the control of trajectory motion is particularly important. At present, the main control methods used in the manipulator are masterslave control, position/force hybrid control, impedance control, adaptive control [11][12][13], neural network control [14][15][16], fuzzy control [17], and so on. In [18], the authors adopted a PD model compensation synovial control law to achieve dual joint trajectory tracking, but it was not suitable for a high degree of freedom obviously.…”

Section: Introductionmentioning

confidence: 99%

Optimization Control of Cooperative Trajectory towards Dual Arms Based on Time-Varying Constrained Output State

Zhang

Zha

et al. 2021

Complexity

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Aiming at the impact and disturbance of dual-arm robots in the process of coordinated transportation, a dual-arm cooperative trajectory optimization control based on time-varying constrained output state is proposed. According to the constraint relationship of the end-effector trajectory of the dual-arm coordinated transportation, the joint space trajectory mathematical model of the dual-arm coordinated transportation was established by using the master-slave construction method. Based on the time impact optimization index of joint trajectory, a multiobjective nonlinear equation is established. Using random probability distribution to extract the interpolation features of nonuniform quintic B-spline trajectory, the feature optimization target is selected, and the Newton numerical algorithm is used for iterative optimization. At the same time, it is combined with an elite retention genetic algorithm to further optimize the target. Based on the disturbance and tracking problem, a PD control method based on time-varying constrained output state is proposed, and the control law is designed. Its convergence is verified by establishing the Lyapunov function equation and asymmetric term. The trajectory optimization results show that the proposed trajectory optimization method can increase the individual diversity and enhance the individual local optimization, thus avoiding the premature impact of the elite retention genetic algorithm. Finally, the proposed control method is simulated on the platform of Gazebo; compared with the traditional PD control method, the results show that the proposed control algorithm has high robustness, and the rationality of the coordinated trajectory control method is verified by the double-arm handling experiment.

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Robust model-free control for redundant robotic manipulators based on zeroing neural networks activated by nonlinear functions

Cited by 29 publications

References 23 publications

Fixed-time Active Disturbance Rejection Consensus Tracking Control of A Multi-Agent System via Event-Triggered Mechanism

Fixed-time Active Disturbance Rejection Consensus Tracking Control of A Multi-Agent System via Event-Triggered Mechanism

Cooperative Control of Backstepping Neural Network and Port-Controlled Hamiltonian for Robot System

Optimization Control of Cooperative Trajectory towards Dual Arms Based on Time-Varying Constrained Output State

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