This paper deals with the global position control problem of robot manipulators in joint space, a new family of control schemes consisting of a suitable combination of hyperbolic functions is presented. The proposed control family includes a large class of bounded hyperbolic-type control schemes to drive both position error and derivative action terms plus gravity compensation. To ensure global asymptotic stability of closed-loop system equilibrium point, we propose an energy-shaping based strict Lyapunov function. To verify the efficiency of the proposed control algorithm, an experimental comparative analysis between the well known unbounded linear PD control and three hyperbolic-type control schemes of the proposed family on a three degrees of freedom direct-drive robot manipulator is analysed.
This paper presents a design tool of impedance controllers for robot manipulators, based on the formulation of Lyapunov functions. The proposed control approach addresses two cha\-llen\-ges: the regulation of the interaction forces, ensured by the impedance error converging to zero, while preserving a suitable path tracking despite constraints imposed by the environment. The asymptotic stability of an equilibrium point of the system, composed by full non\-li\-near robot dynamics and the impedance control, is demonstrated according to Lyapunov's direct method. The system's performance was tested through the real-time experimental implementation of an interaction task involving a two degree-of-freedom, direct-drive robot.
Intuitionistic fuzzy sets (IFSs) are generalization of fuzzy sets by adding an additional attribute parameter called non-membership degree. In this paper, a max-min intuitionistic fuzzy Hopfield neural network (IFHNN) is proposed by combining IFSs with Hopfield neural networks. The stability of IFHNN is investigated. It is shown that for any given weight matrix and any given initial intuitionistic fuzzy pattern, the iteration process of IFHNN converges to a limit cycle. Furthermore, under suitable extra conditions, it converges to a stable point within finite iterations. Finally, a kind of Lyapunov stability of the stable points of IFHNN is proved, which means that if the initial state of the network is close enough to a stable point, then the network states will remain in a small neighborhood of the stable point. These stability results indicate the convergence of memory process of IFHNN. A numerical example is also provided to show the effectiveness of the Lyapunov stability of IFHNN.