• Publications
  • Influence
Dynamic Motion Planning for Mobile Robots Using Potential Field Method
  • S. Ge, Y. Cui
  • Computer Science
    Auton. Robots
  • 1 November 2002
A new potential field method for motion planning of mobile robots in a dynamic environment where the target and the obstacles are moving is proposed and the problem of local minima is discussed.
Adaptive Neural Network Control of Robotic Manipulators
The text has been tailored to give a comprehensive study of robot dynamics, present structured network models for robots, and provide systematic approaches for neural network based adaptive controller design for rigid robots, flexible joint Robots, and robots in constraint motion.
Adaptive dynamic surface control of nonlinear systems with unknown dead zone in pure feedback form
It is proved that the proposed design method is able to guarantee semi-global uniform ultimate boundedness of all signals in the closed-loop system, with arbitrary small tracking error by appropriately choosing design constants.
Direct adaptive NN control of a class of nonlinear systems
  • S. Ge, Cong Wang
  • Mathematics, Computer Science
    IEEE Trans. Neural Networks
  • 2002
In this paper, direct adaptive neural-network (NN) control is presented for a class of affine nonlinear systems in the strict-feedback form with unknown nonlinearities. By utilizing a special
Adaptive neural control of uncertain MIMO nonlinear systems
  • S. Ge, Cong Wang
  • Mathematics, Medicine
    IEEE Transactions on Neural Networks
  • 1 May 2004
Adapt neural control schemes are proposed for two classes of uncertain multi-input/multi-output (MIMO) nonlinear systems in block-triangular forms that avoid the controller singularity problem completely without using projection algorithms.
Stable Adaptive Neural Network Control
While neural network control has been successfully applied in various practical applications, many important issues, such as stability, robustness, and performance, have not been extensively
Adaptive neural network control for strict-feedback nonlinear systems using backstepping design
A smooth and singularity-free adaptive controller is designed for a first-order plant and an extension is made to high-order nonlinear systems using neural network approximation and adaptive backstepping techniques, guaranteeing the uniform ultimate boundedness of the closed-loop adaptive systems.
Adaptive Neural Control for Output Feedback Nonlinear Systems Using a Barrier Lyapunov Function
A barrier Lyapunov function (BLF) is introduced to address two open and challenging problems in the neuro-control area: for any initial compact set, how to determine a priori the compact superset on which NN approximation is valid; and how to ensure that the arguments of the unknown functions remain within the specified compact supersets.