2,501 research outputs found

    Nonlinear disturbance attenuation control of hydraulic robotics

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    This paper presents a novel nonlinear disturbance rejection control for hydraulic robots. This method requires two third-order filters as well as inverse dynamics in order to estimate the disturbances. All the parameters for the third-order filters are pre-defined. The proposed method is nonlinear, which does not require the linearization of the rigid body dynamics. The estimated disturbances are used by the nonlinear controller in order to achieve disturbance attenuation. The performance of the proposed approach is compared with existing approaches. Finally, the tracking performance and robustness of the proposed approach is validated extensively on real hardware by performing different tasks under either internal or both internal and external disturbances. The experimental results demonstrate the robustness and superior tracking performance of the proposed approach

    Accurate Tracking of Aggressive Quadrotor Trajectories using Incremental Nonlinear Dynamic Inversion and Differential Flatness

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    Autonomous unmanned aerial vehicles (UAVs) that can execute aggressive (i.e., high-speed and high-acceleration) maneuvers have attracted significant attention in the past few years. This paper focuses on accurate tracking of aggressive quadcopter trajectories. We propose a novel control law for tracking of position and yaw angle and their derivatives of up to fourth order, specifically, velocity, acceleration, jerk, and snap along with yaw rate and yaw acceleration. Jerk and snap are tracked using feedforward inputs for angular rate and angular acceleration based on the differential flatness of the quadcopter dynamics. Snap tracking requires direct control of body torque, which we achieve using closed-loop motor speed control based on measurements from optical encoders attached to the motors. The controller utilizes incremental nonlinear dynamic inversion (INDI) for robust tracking of linear and angular accelerations despite external disturbances, such as aerodynamic drag forces. Hence, prior modeling of aerodynamic effects is not required. We rigorously analyze the proposed control law through response analysis, and we demonstrate it in experiments. The controller enables a quadcopter UAV to track complex 3D trajectories, reaching speeds up to 12.9 m/s and accelerations up to 2.1g, while keeping the root-mean-square tracking error down to 6.6 cm, in a flight volume that is roughly 18 m by 7 m and 3 m tall. We also demonstrate the robustness of the controller by attaching a drag plate to the UAV in flight tests and by pulling on the UAV with a rope during hover.Comment: To be published in IEEE Transactions on Control Systems Technology. Revision: new set of experiments at increased speed (up to 12.9 m/s), updated controller design using quaternion representation, new video available at https://youtu.be/K15lNBAKDC

    SPECIFIED MOTION AND FEEDBACK CONTROL OF ENGINEERING STRUCTURES WITH DISTRIBUTED SENSORS AND ACTUATORS

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    This dissertation addresses the control of flexible structures using distributed sensors and actuators. The objective to determine the required distributed actuation inputs such that the desired output is obtained. Two interrelated facets of this problem are considered. First, we develop a dynamic-inversion solution method for determining the distributed actuation inputs, as a function of time, that yield a specified motion. The solution is shown to be useful for intelligent structure design, in particular, for sizing actuators and choosing their placement. Secondly, we develop a new feedback control method, which is based on dynamic inversion. In particular, filtered dynamic inversion combines dynamic inversion with a low-pass filter, resulting in a high-parameter-stabilizing controller, where the parameter gain is the filter cutoff frequency. For sufficiently large parameter gain, the controller stabilizes the closed-loop system and makes the L2-gain of the performance arbitrarily small, despite unknown-and-unmeasured disturbances. The controller is considered for both linear and nonlinear structural models

    Control strategies for robotic manipulators

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    This survey is aimed at presenting the major robust control strategies for rigid robot manipulators. The techniques discussed are feedback linearization/Computed torque control, Variable structure compensator, Passivity based approach and Disturbance observer based control. The first one is based on complete dynamic model of a robot. It results in simple linear control which offers guaranteed stability. Variable structure compensator uses a switching/relay action to overcome dynamic uncertainties and disturbances. Passivity based controller make use of passive structure of a robot. If passivity of a feedback system is proved, nonlinearities and uncertainties will not affect the stability. Disturbance observer based controllers estimate disturbances, which can be cancelled out to achieve a nominal model, for which a simple controller can then be designed. This paper, after explaining each control strategy in detail, finally compares these strategies for their pros and cons. Possible solutions to cope with the drawbacks have also been presented in tabular form. © 2012 IEEE

    Constrained Nonlinear Model Predictive Control of an MMA Polymerization Process via Evolutionary Optimization

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    In this work, a nonlinear model predictive controller is developed for a batch polymerization process. The physical model of the process is parameterized along a desired trajectory resulting in a trajectory linearized piecewise model (a multiple linear model bank) and the parameters are identified for an experimental polymerization reactor. Then, a multiple model adaptive predictive controller is designed for thermal trajectory tracking of the MMA polymerization. The input control signal to the process is constrained by the maximum thermal power provided by the heaters. The constrained optimization in the model predictive controller is solved via genetic algorithms to minimize a DMC cost function in each sampling interval.Comment: 12 pages, 9 figures, 28 reference

    Adaptive Predictive Control Using Neural Network for a Class of Pure-feedback Systems in Discrete-time

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    10.1109/TNN.2008.2000446IEEE Transactions on Neural Networks1991599-1614ITNN

    Ship Course Keeping Using Different Sliding Mode Controllers

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    This study addresses three sliding mode heading controllers for dealing with uncertain wave disturbances. A nonlinear steering model is derived, and the feedback linearization method is chosen to simplify the nonlinear system in this study. The adaptive method and disturbance observer technique are proposed for course keeping and ensuring robust performance of the time varying wave moment and actuator dynamics. Finally, the simulation results on a navy ship illustrate the effectiveness of the presented control algorithms for course keeping
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