In this paper, a neural network based on the projection and contraction method is employed to compute the minimum infinity-norm joint torques of redundant manipulators, which explicitly takes into account the joint torque limits. While the desired accelerations of the end-effector for a specified task are fed into the network, a driving joint torque vector which has the maximum component in magnitude being minimized and is never exceeding the joint torque limits is generated as the neural network output. The proposed neural torque control scheme is shown to be capable of effectively generating the bounded minimum infinity-norm driving joint torques of redundant manipulators.

This paper presents an improved neural computation where scheme for kinematic control of redundant manipulators based on infinity-norm joint velocity minimization. Compared with a previous neural network approach to minimum infinity-non kinematic control, the present approach is less complex in terms of cost of architecture. The recurrent neural network explicitly minimizes the maximum component of the joint velocity vector while tracking a desired end-effector trajectory. The end-effector velocity vector for a given task is fed into the neural network from its input and the minimum infinity-norm joint velocity vector is generated at its output instantaneously. Analytical results are given to substantiate the asymptotic stability of the recurrent neural network. The simulation results of a four-degree-of-freedom planar robot arm and a seven-degree-of-freedom industrial robot are presented to show the proposed neural network can effectively compute the minimum infinity-norm solution to redundant manipulators.

A discrete-time recurrent neural network is presented for solving convex quadratic programs. It is the discrete-time version of its continuous-time counterpart which was developed by J. Wang and H. Li (1994). Sharing the same characteristic with its continuous-time counterpart, the proposed discrete-time neural network could compute the exact optimal solution to a quadratic program without using any penalty parameter. However, the discrete-time version is more desirable in practical realization in view of the availability of digital hardware and the good compatibility to computer. The condition for the neural network globally converging to the optimal solution of a quadratic program is given. The neural network is applied to FIR filter synthesis for illustrating its effectiveness.

A discrete-time recurrent neural network which is called the discrete-time Lagrangian network is proposed in this letter for solving convex quadratic programs. It is developed based on the classical Lagrange optimization method and solves quadratic programs without using any penalty parameter. The condition for the neural network to globally converge to the optimal solution of the quadratic program is given. Simulation results are presented to illustrate its performance.

This paper presents two neural network approaches to real-time joint torque optimization for kinematically redundant manipulators. Two recurrent neural networks are proposed for determining the minimum driving joint torques of redundant manipulators for the eases without and with taking the joint torque limits into consideration, respectively. The first neural network is called the Lagrangian network and the second one is called the primal-dual network. In both neural-network-based computation schemes, while the desired accelerations of the end-effector for a specific task are given to the neural networks as their inputs, the signals of the minimum driving joint torques are generated as their outputs to drive the manipulator arm. Both proposed recurrent neural networks are shown to be capable of generating minimum stable driving joint torques. In addition, the driving joint torques computed by the primal-dual network are shown never exceeding the joint torque limits.

This paper presents an improved neural computation scheme for kinematic control of redundant manipulators based on infinity-norm joint velocity minimization. Compared with a preview neural network approach to minimum infinity-norm kinematic control, the presented approach has a less complex architecture. The recurrent neural network explicitly minimizes the maximum component of the joint velocity vector while tracking a desired end-effector trajectory. The end-effector velocity vector for a given task is fed into the neural network from its input and the minimum infinity-norm joint velocity vector is generated at its output instantaneously. Analytical results are given to substantiate the asymptotic stability of the recurrent neural network. The simulation results of a four degree-of-freedom planar robot arm are presented to show the proposed neural network can effectively compute the minimum infinity-norm solution to redundant manipulators in real-time.

A recurrent neural network for kinematic control of redundant robot manipulators with torque minimization is presented. The proposed recurrent neural network is composed of two bidirectionally connected layers of neuron arrays. While the command signals of desired acceleration of the end-effector are fed into the input layer, the output layer generates the joint acceleration vector of the manipulator with joint torques being minimized. The proposed recurrent neural network is shown to be capable of asymptotic tracking of trajectory for the redundant manipulators with minimized joint torques.