Six-link Robot Manipulator Research Articles

Performance analysis of gradient neural network exploited for online time-varying quadratic minimization and equality-constrained quadratic programming

In this paper, the performance of a gradient neural network (GNN), which was designed intrinsically for solving static problems, is investigated, analyzed and simulated in the situation of time-varying coefficients. It is theoretically proved that the gradient neural network for online solution of time-varying quadratic minimization (QM) and quadratic programming (QP) problems could only approximately approach the time-varying theoretical solution, instead of converging exactly. That is, the steady-state error between the GNN solution and the theoretical solution can not decrease to zero. In order to understand the situation better, the upper bound of such an error is estimated firstly, and then the global exponential convergence rate is investigated for such a GNN when approaching an error bound. Computer-simulation results, including those based on a six-link robot manipulator, further substantiate the performance analysis of the GNN exploited to solve online time-varying QM and QP problems.

Nonsingular Terminal Sliding Mode Control of Robot Manipulators Using Fuzzy Wavelet Networks

This paper presents an adaptive nonsingular terminal sliding mode (NTSM) tracking control design for robotic systems using fuzzy wavelet networks. Compared with linear hyperplane-based sliding control, terminal sliding mode controller can provide faster convergence and higher precision control. Therefore, a terminal sliding controller combined with the fuzzy wavelet network, which can accurately approximate unknown dynamics of robotic systems by using an adaptive learning algorithm, is an attractive control approach for robots. In addition, the proposed learning algorithm can on-line tune parameters of dilation and translation of fuzzy wavelet basis functions and hidden-to-output weights. Therefore, a robust control law is used to eliminate uncertainties including the inevitable approximation errors resulted from the finite number of fuzzy wavelet basis functions. The proposed controller requires no prior knowledge about the dynamics of the robot and no off-line learning phase. Moreover, both tracking performance and stability of the closed-loop robotic system can be guaranteed by Lyapunov theory. Finally, the effectiveness of the fuzzy wavelet network-based control approach is illustrated through comparative simulations on a six-link robot manipulator

Neural-net tuned PID control of a parallel type mechanism with force feedback for virtual reality applications

This paper explores a new type of a parallel platform human interface manipulator based on virtual reality (VR) for mechanism design applications. A motion control of a six-link robot manipulator actuated by three active joints is presented here. The main components of the system include a user interface, a software simulating the environment, and a VR control system. The model of the VR system is built based on a force feedback behavior that enables the operator to feel the actual force feedback from the virtual environment just as he/she would from the real environment. A primary stabilizing controller is used to develop a haptic interface device where realistic simulations of the dynamic interaction forces between a human operator and the simulated virtual object/mechanism is required. The stability and performance of the system are studied and analyzed based on the Nyquist stability criterion. Experiments on cutting virtual clay are used to validate the theoretical developments. It was shown that the experimental and theoretical results are in good agreement.

Adaptive tracking controller design for robotic systems using Gaussian wavelet networks

An adaptive tracking control design for robotic systems using Gaussian wavelet networks is proposed. A Gaussian wavelet network with accurate approximation capability is employed to approximate the unknown dynamics of robotic systems by using an adaptive learning algorithm that can learn the parameters of the dilation and translation of Gaussian wavelet functions. Depending on the finite number of wavelet basis functions which result in inevitable approximation errors, a robust control law is provided to guarantee the stability of the closed-loop robotic system that can be proved by Lyapunov theory. Finally, the effectiveness of the Gaussian wavelet network-based control approach is illustrated through comparative simulations on a six-link robot manipulator.

Feasibility study of manipulator inverse kinematics problems with applications of optimization principles

This paper studies three efficient methods for solving inverse kinematics problems relevant to a six-link robot manipulator. In a problem formulation, governing kinematic equations are transformed into an unconstrained optimization problem with an objective function defined by a sum of squares of the errors, and then two nonlinear models—i.e. the variable metric method and the least squares method-are applied to derive stable joint solutions together with a practical linearized model. The results of computer simulation show that these numerical solutions are highly reliable over a much wider application range as compared with a traditional method.

New approaches to manipulator arm solutions via unconstrained optimization theory

SUMMARYIn this study, highly practical and reliable methods are proposed to determine arm solutions for a six-link robot manipulator. Based on a typical mathematical structure of minimizing an objective function, the optimization theory is applied to solve a reduced system of kinematic equations. The performance tests show that three different approaches are superior to a conventional method and of sufficiently practical use. Especially, the use of an algorithm presented in a linear search is promising.