In this paper, adaptive neural tracking control is proposed for a robotic manipulator with uncertainties in both manipulator dynamics and joint actuator dynamics. The manipulator joints are subject to inequality constraints, i.e., the joint angles are required to remain in some compact sets. Integral barrier Lyapunov functionals (iBLFs) are employed to address the joint space constraints directly without performing an additional mapping to the error space. Neural networks (NNs) are utilised to compensate for the unknown robot dynamics and external force. Adapting parameters are developed to estimate the unknown bounds on NN approximations. By the Lyapunov synthesis, the proposed control can guarantee the semi-global uniform ultimate boundedness of the closed-loop system, and the practical tracking of joint reference trajectory is achieved without the violation of predefined joint space constraints. Simulation results are given to validate the effectiveness of the proposed control scheme.

Adaptive neural control for an uncertain robotic manipulator with joint space constraints

Redundancy resolution is a critical problem in the control of manipulators. The dual neural network, as a special type of recurrent neural networks that are inherently parallel processing models, is widely investigated in past decades to control manipulators. However, to the best of our knowledge, existing dual neural networks require a full knowledge about manipulator parameters for efficient control. We make progress along this direction in this paper by proposing a novel model-free dual neural network, which is able to address the learning and control of manipulators simultaneously in a unified framework. Different from pure learning problems, the interplay of the control part and the learning part allows us to inject an additive noise into the control channel to increase the richness of signals for the purpose of efficient learning. Due to a deliberate design, the learning error is guaranteed for convergence to zero despite the existence of additive noise for stimulation. Theoretical analysis reveals the global stability of the proposed neural network control system. Simulation results verify the effectiveness of the proposed control scheme for redundancy resolution of a PUMA 560 manipulator.

A Dynamic Neural Network Approach for Efficient Control of Manipulators

In this study, a robust adaptive neural control is proposed for a fractional-order rotational mechanical system (FORMS) in the presence of system uncertainties and external unknown disturbances. System uncertainties of the FORMS are handled by the neural network (NN). To tackle unknown disturbances, a non-linear fractional-order disturbance observer (FODO) is explored for the FORMS. A robust adaptive control scheme is then developed by combining the NN with the designed FODO. Finally, numerical simulation results further demonstrate the effectiveness of the proposed tracking control scheme for the uncertain FORMS subject to external unknown disturbances.

https://ietresearch.onlinelibrary.wiley.com/doi/pdf/10.1049/iet-cta.2015.1054

Adaptive neural control for an uncertain fractional-order rotational mechanical system using disturbance observer

A new dynamic system is proposed and investigated for solving online simultaneous linear equations. Compared with the gradient-based dynamic system and the recently proposed Zhang dynamic system, the proposed dynamic system can achieve superior convergence performance (i.e. finite-time convergence) and thus is called the finite-time convergent dynamic system. In addition, the upper bound of the convergence time is derived analytically with the error bound being zero theoretically. Simulation results further indicate that the proposed dynamic system is much more efficient than the existing dynamic systems.

A finite-time convergent dynamic system for solving online simultaneous linear equations

Motion analysis and control of a pendulum-driven spherical robot (PDSR) on an inclined plane with a variable slope is investigated. Firstly, the mathematical model of a PDSR on a variable-slope inclined plane is deduced applying a Lagrangian formulation. Afterwards, in the presence of an unknown external disturbance, the terminal sliding mode control (TSMC) technique is employed to stabilize the robot on the inclined plane, while the plane is still moving. In other words, the terminal sliding mode disturbance observer is used to estimate the unknown disturbance. Based on the disturbance estimation, the TSMC scheme is established to control the single-input and single-output nonlinear system with control singularity and an unknown nonsymmetric control input saturation. In fact, a compound disturbance is defined and estimated, which includes the external disturbance, the control singularity and the unknown input saturation. Simulations are then conducted to validate the proposed approach for motion control of a PDSR on a variable-slope inclined plane with an unknown external disturbance and nonsymmetric input limits.

Mathematical modelling and control of a nonholonomic spherical robot on a variable-slope inclined plane using terminal sliding mode control

In this paper, an adaptive neural network (NN) switching control strategy is proposed for the trajectory tracking problem of robotic manipulators. The proposed system comprises an adaptive switching neural controller and the associated robust compensation control law. Based on the Lyapunov stability theorem and average dwell-time approach, it is shown that the proposed control scheme can guarantee tracking performance of the robotic manipulators system, in the sense that all variables of the closed-loop system are bounded and the effect due to the external disturbance and approximate error of radical basis function (RBF) NNs on the tracking error can be converged to zero in an infinite time. Finally, simulation results on a two-link robotic manipulator show the feasibility and validity of the proposed control scheme.

Lining Sun

Papers

An adaptive neural network switching control approach of robotic manipulators for trajectory tracking