In this document, the development and experimental validation of a nonlinear controller with an adaptive disturbance compensation system applied on a quadrotor are presented. The introduced scheme relies on a generalized regression neural network (GRNN). The proposed scheme has a structure consisting of an inner control loop inaccessible to the user (i.e., an embedded controller) and an outer control loop which generates commands for the inner control loop. The adaptive GRNN is applied in the outer control loop. The proposed approach lies in the aptitude of the GRNN to estimate the disturbances and unmodeled dynamic effects without requiring accurate knowledge of the quadrotor parameters. The adaptation laws are deduced from a rigorous convergence analysis ensuring asymptotic trajectory tracking. The proposed control scheme is implemented on the QBall 2 quadrotor. Comparisons with respect to a PD-based control, an adaptive model regressor-based scheme, and an adaptive neural-network controller are carried out. The experimental results validate the functionality of the novel control scheme and show a performance improvement since smaller tracking error values are produced.

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.

This paper deals with motion control of the inertia wheel pendulum. Specifically, we address a trajectory tracking problem. The proposed control algorithm is derived from feedback linearization, with the output of the system defined as the tracking error between a desired reference trajectory and the angular position of the inverted pendulum. The explicit expressions of the internal dynamics and zero-dynamics are obtained, where can be observed that the stability of the internal dynamics depends on the desired trajectory. Experimental evaluation of the proposed controller is presented, where we show that the trajectory tracking is accomplished for both upward and downward pendulum position, while the wheel velocity remains into the actuator constraints.

In this paper, a class of robot manipulators actuated by direct current (DC) motors and servo amplifiers in current mode is studied. For those robots, it is assumed that the control inputs to the manipulator are the servo amplifier input voltages. A model-based trajectory tracking controller is developed, which renders the closed-loop system to be passive from an auxiliary control input to a filtered error signal. Experimental results support theoretical results.

By using only position measurements, in this paper is discussed a new control algorithm for velocity field control of robot arms. A velocity field defines the robot desired velocity in the operational space as a function of its current position. The introduced algorithm is based on a hierarchical structure that result of using the kinematic control concept and a joint velocity controller

We introduce a control scheme based on using a trajectory tracking controller and an algorithm for on-line time-scaling of the reference trajectories. The reference trajectories are time-scaled according to the measured tracking errors and the detected torque/acceleration saturation. Simulations are presented to illustrate the advantages of the proposed approach

This paper concerns the velocity field control in operational space of robot manipulators. Velocity field control is a recent control formulation in robotics. A velocity field defines the desired robot velocity in the operational space as a function of its current position, thus the robot performs the desired motions. In this paper, a controller is proposed for operational space velocity field control. The proposed controller is based on a hierarchical structure that result of using the kinematic control concept and a joint velocity controller. Experimental results on a two degrees-of-freedom direct-drive robot arm illustrate the viability of the proposed scheme.

The velocity control of a direct-drive robot arm with high friction is considered in this paper. The friction at the robot joints is assumed to be captured by the Dahl model (1976), which has a bristle deflection interpretation. A joint velocity controller with friction compensation based on the Dahl model is introduced. Experiments show the superiority of the proposed scheme with respect to the velocity controller when friction is compensated using the classical viscous plus Coulomb model of friction.

This paper addresses the visual servoing of robot manipulators in fixed-camera configuration for considering a moving target. We propose a control scheme consisting of two loops: an inner loop which is a joint velocity controller; and an outer loop which is an image-based feedback loop. We present the stability analysis and the experimental evaluation on a two degrees of freedom direct-drive planar robot arm.