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Intelligent control of space manipulator with flexible-link and flexible-joint is discussed based on the singular perturbation method. Owing to the combined effects of the link and joint flexibilities, the dynamic model of this kind of manipulator becomes more complex and leads to a series of unsolved control system. To simplify the design of the control system, singular perturbation method is used...
This paper presents a discrete-time variable structure control based on neural networks for a planar robotic manipulator. Radial basis function neural networks are used to learn about uncertainties affecting the system. The learning algorithm combines the growth criterion of the resource allocating network technique with an adaptive extended Kalman filter to update all network parameters. The analysis...
A global sliding mode control scheme by neural networks is proposed for high performance drive systems of brushless DC motors with uncertain external disturbances and unknown loads. A global sliding mode manifold is designed in this approach, which guarantees that the system states can be on the sliding mode manifold at initial time and the system robustness is increased. A radial basis function neural...
A adaptive Radial basis function neural network (RBFNN) based fuzzy sliding mode control scheme for two link robot manipulator is proposed in this paper. In the scheme, RBFNN is used to approximate system dynamic, the weights of the RBFNN are changed according to adaptive algorithm to ensure the system state hitting the sliding surface and sliding along it. In order to guarantee the stability and...
The main problem of sliding mode controllers is that a whole knowledge system parameters is required to compute the equivalent control. Neural networks are used to compute the equivalent control. Standard two layer feedforward neural network training with the backpropagation algorithm and Radial Basis Function Neural Networks (RBFNN) are the most popular methods that used on robot control. This paper...
This paper is concerned with cooperative control for trajectory tracking of multiple biomimetic robotic fish using neural network based sliding mode control method. An experiment system is set up for multiple robotic fish cooperation, in which the information of robotic fish and the target points of the planned trajectory are sent to each robotic fish. Based on the received information, robotic fish...
In this paper, a trajectory tracking control for a nonholonomic mobile robot by the integration of a kinematic controller and neural dynamic controller is investigated, where the wheel actuator (e.g., dc motor) dynamics is integrated with mobile robot dynamics and kinematics so that the actuator input voltages are the control inputs. The proposed neural dynamic controller (PNDC), based on the sliding...
A neural sliding mode controller is presented for trajectory tracking control of multi-link robots with uncertain external disturbances and system model errors. This approach gives a new global sliding mode manifold for the second-order multi-link robots, which enable system trajectory to run on the sliding mode manifold at the initial states and eliminate the reaching phase of conventional sliding...
The purpose of this paper is to propose adaptive fuzzy sliding mode control (SMC) based on radial basis function neural network (RBFNN) for trajectory tracking problem of three link robot manipulator. A RBFNN is used to compute the equivalent control of sliding mode control. A Lyapunov function is selected for the design of the SMC and an adaptive algorithm is used for weight adaptation of the RBFNN...
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