Wai Sum Tang

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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(More)
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(More)
In this paper, a recurrent neural network called the Lagrangian network is applied for obstacle avoidance in kinematically redundant manipulators. Conventional numerical methods implemented in digital computers for obstacle avoidance redundancy resolution calculation could only compute the solution in milliseconds while neural network realized by hardware(More)
In recent years, artificial neural networks have been applied to solve a variety of problems in numerous areas of manufacturing at both system and process levels. The manufacturing applications of neural networks comprise the design of manufacturing systems (including part-family and machine-cell formation for cellular manufacturing systems); modeling,(More)
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(More)
In this paper, a neural network based on the projection and contraction method is employed to compute the minimum in nity-norm joint torques of redundant manipulators, which explicitly takes into account the joint torque limits. While the desired accelerations of the end-e ector for a speci ed task are fed into the network, a driving joint torque vector(More)
A recurrent neural network is applied for minimizing the infinity-norm of joint torques in redundant manipulators. The recurrent neural network explicitly minimizes the maximum component of joint torques in magnitude while keeping the relation between the joint torque and the end-effector acceleration satisfied. The end-effector accelerations are given to(More)
In this paper, a primal-dual neural network is proposed for the joint torque optimization of redundant manipulators subject to torque limit constraints. The neural network generates the minimum driving joint torques which never exceed the hardware limits and keep the end-effector to track a desired trajectory. The consideration of physical limits prevents(More)
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