A Proportional-Integral-Derivative type regulation scheme with non-Lipschitz control actions for mechanical systems with constrained inputs.

Motivated by the benefits that recent finite-time continuous control approaches have proven to give rise, this work aims to design a proportional-integral-derivative (PID) type control scheme for the global regulation of constrained-input mechanical systems that incorporates design features characteristic of such finite-time continuous algorithms. This is proven to be achieved through a more general PID type control structure that incorporates exponential weights on the P and D type terms, through which such control actions are permitted to loose Lipschitz-continuity at the desired equilibrium values. This entails an important challenge consisting on the introduction of an appropriate analytical framework and the development of a suitable closed-loop analysis through which the resulting design is properly supported.

Control of a self-balancing robot with two degrees of freedom via IDA-PBC.

This paper presents an interconnection and damping assignment passivity-based control (IDA-PBC) to drive a self-balancing robot restricted to two degrees of freedom including the dynamics of the actuators. The design of the control law, stability analysis and estimation of the domain of attraction are shown in detail. A convenient change of variables and an appropriate handling of the matching equations have been key to design the control law and to get the asymptotic stability analysis, respectively.

Adaptive Neural Network Control for the Trajectory Tracking of the Furuta Pendulum.

The purpose of this paper is to introduce a novel adaptive neural network-based control scheme for the Furuta pendulum, which is a two degree-of-freedom underactuated system. Adaptation laws for the input and output weights are also provided. The proposed controller is able to guarantee tracking of a reference signal for the arm while the pendulum remains in the upright position.

Real-Time Decentralized Neural Control via Backstepping for a Robotic Arm Powered by Industrial Servomotors.

This paper presents a continuous-time decentralized neural control scheme for trajectory tracking of a two degrees of freedom direct drive vertical robotic arm. A decentralized recurrent high-order neural network (RHONN) structure is proposed to identify online, in a series-parallel configuration and using the filtered error learning law, the dynamics of the plant. Based on the RHONN subsystems, a local neural controller is derived via backstepping approach.

Global asymptotic stability of a tracking sectorial fuzzy controller for robot manipulators.

This paper shows that fuzzy control systems satisfying sectorial properties are effective for motion tracking control of robot manipulators. We propose a controller whose structure is composed by a sectorial fuzzy controller plus a full nonlinear robot dynamics compensation, in such a way that this structure leads to a very simple closed-loop system represented by an autonomous nonlinear differential equation. We demonstrate via Lyapunov theory, that the closed-loop system is globally asymptotically stable.