Constraint mapping in a feedback linearization/MPC scheme for trajectory tracking of underactuated multibody systems

This work presents three different approaches in inverse dynamics for the solution of trajectory tracking problems in underactuated multibody systems. Such systems are characterized by less control inputs than degrees of freedom. The first approach uses an extension of the equations of motion by geometric and control constraints. This results in index-five differential-algebraic equations. A projection method is used to reduce the systems index and the resulting equations are solved numerically. The second method is a flatness-based feedforward control design. Input and state variables can be parameterized by the flat outputs and their time derivatives up to a certain order. The third approach uses an optimal control algorithm which is based on the minimization of a cost functional including system outputs and desired trajectory. It has to be distinguished between direct and indirect methods. These specific methods are applied to an underactuated planar crane and a three-dimensional rotary crane.

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Trajectory-Tracking Control of Mobile Robot via Feedback Linearization

Advances in Intelligent Systems and Computing - Proceedings of the Second International Scientific Conference “Intelligent Information Technologies for Industry” (IITI’17) ◽

10.1007/978-3-319-68324-9_4 ◽

2017 ◽

pp. 32-41

Author(s):

Aleksey A. Kabanov ◽

Svilen Stoyanov ◽

Ekaterina N. Kabanova

Keyword(s):

Mobile Robot ◽

Trajectory Tracking ◽

Tracking Control ◽

Feedback Linearization ◽

Trajectory Tracking Control

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Motion Planning and Coordinated Control of Underwater Vehicle-Manipulator Systems with Inertial Delay Control and Fuzzy Compensator

Applied Sciences ◽

10.3390/app10113944 ◽

2020 ◽

Vol 10 (11) ◽

pp. 3944

Author(s):

Han Han ◽

Yanhui Wei ◽

Xiufen Ye ◽

Wenzhi Liu

Keyword(s):

Motion Planning ◽

Trajectory Tracking ◽

Feedback Linearization ◽

Nonlinear Function ◽

Coordinated Control ◽

Underwater Vehicle ◽

Redundancy Resolution ◽

Tracking Errors ◽

Delay Control ◽

Fuzzy Compensator

This paper presents new motion planning and robust coordinated control schemes for trajectory tracking of the underwater vehicle-manipulator system (UVMS) subjected to model uncertainties, time-varying external disturbances, payload and sensory noises. A redundancy resolution technique with a new secondary task and nonlinear function is proposed to generate trajectories for the vehicle and manipulator. In this way, the vehicle attitude and manipulator position are aligned in such a way that the interactive forces are reduced. To resist sensory measurement noises, an extended Kalman filter (EKF) is utilized to estimate the UVMS states. Using these estimates, a tracking controller based on feedback Linearization with both the joint-space and task-space tracking errors is proposed. Moreover, the inertial delay control (IDC) is incorporated in the proposed control scheme to estimate the lumped uncertainties and disturbances. In addition, a fuzzy compensator based on these estimates via IDC is introduced for reducing the undesired effects of perturbations. Trajectory tracking tasks on a five-degrees-of-freedom (5-DOF) underwater vehicle equipped with a 3-DOF manipulator are numerically simulated. The comparative results demonstrate the performance of the proposed controller in terms of tracking errors, energy consumption and robustness against uncertainties and disturbances.

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Singularity-Free Neural Control for the Exponential Trajectory Tracking in Multiple-Input Uncertain Systems with Unknown Deadzone Nonlinearities

The Scientific World JOURNAL ◽

10.1155/2014/951983 ◽

2014 ◽

Vol 2014 ◽

pp. 1-10 ◽

Cited By ~ 2

Author(s):

J. Humberto Pérez-Cruz ◽

José de Jesús Rubio ◽

Rodrigo Encinas ◽

Ricardo Balcazar

Keyword(s):

Neural Network ◽

Trajectory Tracking ◽

Feedback Linearization ◽

Neural Control ◽

Tracking Error ◽

Uncertain System ◽

Reference Trajectory ◽

System A ◽

Multiple Input ◽

Feedback Linearization Control

The trajectory tracking for a class of uncertain nonlinear systems in which the number of possible states is equal to the number of inputs and each input is preceded by an unknown symmetric deadzone is considered. The unknown dynamics is identified by means of a continuous time recurrent neural network in which the control singularity is conveniently avoided by guaranteeing the invertibility of the coupling matrix. Given this neural network-based mathematical model of the uncertain system, a singularity-free feedback linearization control law is developed in order to compel the system state to follow a reference trajectory. By means of Lyapunov-like analysis, the exponential convergence of the tracking error to a bounded zone can be proven. Likewise, the boundedness of all closed-loop signals can be guaranteed.

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Adaptive trajectory tracking control of a differential drive wheeled mobile robot

Robotica ◽

10.1017/s0263574710000202 ◽

2010 ◽

Vol 29 (3) ◽

pp. 391-402 ◽

Cited By ~ 39

Author(s):

Khoshnam Shojaei ◽

Alireza Mohammad Shahri ◽

Ahmadreza Tarakameh ◽

Behzad Tabibian

Keyword(s):

Mobile Robot ◽

Trajectory Tracking ◽

Feedback Linearization ◽

Dynamic Models ◽

Parametric Uncertainty ◽

Adaptive Controller ◽

Wheeled Mobile Robot ◽

Trajectory Tracking Control ◽

Actuator Dynamics ◽

Simulation Results

SUMMARYThis paper presents an adaptive trajectory tracking controller for a non-holonomic wheeled mobile robot (WMR) in the presence of parametric uncertainty in the kinematic and dynamic models of the WMR and actuator dynamics. The adaptive non-linear control law is designed based on input–output feedback linearization technique to get asymptotically exact cancellation for the uncertainty in the given system parameters. In order to evaluate the performance of the proposed controller, a non-adaptive controller is compared with the adaptive controller via computer simulation results. The results show satisfactory trajectory tracking performance by virtue of SPR-Lyapunov design approach. In order to verify the simulation results, a set of experiments have been carried out on a commercial mobile robot. The experimental results also show the effectiveness of the proposed controller.

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