ABSTRACT
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. Two scalar functions have been used in the design of the controller to underscore how different proposals for the desired potential energy can shape the behavior of the self-balancing robot in closed loop. Experimental results are presented in order to confirm the theoretical proposal and to illustrate the performance of the proposed control law in the regulation task and the practical robustness against external disturbances.
ABSTRACT
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. The effectiveness of the decentralized neural controller is validated on a robotic arm platform, of our own design and unknown parameters, which uses industrial servomotors to drive the joints.
ABSTRACT
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. The key aspect of the derivation of the controller is the definition of an output function that depends on the position and velocity errors. The internal and external dynamics are rigorously analyzed, thereby proving the uniform ultimate boundedness of the error trajectories. By using real-time experiments, the new scheme is compared with other control methodologies, therein demonstrating the improved performance of the proposed adaptive algorithm.
Subject(s)
Computer Simulation , Mechanical Phenomena , Neural Networks, Computer , Robotics , Algorithms , Models, Theoretical , Robotics/instrumentation , Robotics/methods , RotationABSTRACT
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. Experimental results show the feasibility of the proposed controller.
