Adaptive fault tolerant control for trajectory tracking of a quadrotor helicopter

2017 ◽  
Vol 40 (12) ◽  
pp. 3560-3569 ◽  
Author(s):  
Min Li ◽  
Zongyu Zuo ◽  
Hao Liu ◽  
Cunjia Liu ◽  
Bing Zhu
Keyword(s):  
Trajectory Tracking ◽  
Fault Tolerant ◽  
Tracking Error ◽  
Adaptive Controller ◽  
Reference Trajectory ◽  
Quadrotor Helicopter ◽  
Attitude Error ◽  
Inertial Parameters ◽  
Error Dynamics ◽  
Fast Adaptation

In this paper, an adaptive fault tolerant controller based on [Formula: see text] control is developed and applied to the trajectory tracking for a quadrotor helicopter. Both multiplicative and additive actuator faults are considered. The proposed design is based on nonlinear feed-forward compensations and a typical nonlinear quadrotor model with uncertain inertial parameters and external disturbances. The [Formula: see text] adaptive control design is slightly modified to adapt with the position and the attitude error dynamics. The proposed adaptive controller yields uniformly verifiable bounds on the transient and the steady-state tracking error for any designated bounded reference trajectory. In the presence of fast adaptation, the adaptive controller compensates for actuator fault and disturbances in a particular frequency range. Finally, simulation results are included to validate the effectiveness of the proposed design.

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

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
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.

Adaptive control of free-floating manipulator in presence of stochastic input disturbances and unknown dynamical parameters

2021 ◽  
pp. 107754632110105
Author(s):  
Masoud Seyed Sakha ◽  
Hamed Kharrati ◽  
Farhad Mehdifar
Keyword(s):  
Trajectory Tracking ◽  
Tracking Error ◽  
Small Neighborhood ◽  
Adaptive Controller ◽  
Physical Parameters ◽  
Power Spectral ◽  
Fixed Base ◽  
Random Disturbances ◽  
Dynamical Parameters ◽  
Stochastic Input

The trajectory tracking problem of a free-floating manipulator with dynamical uncertainties and stochastic input disturbances is solved in this study. First, the free-floating manipulator is mapped to a conventional fixed base dynamically equivalent manipulator. Then, by using the well-known properties of a revolute joint manipulator and taking into account the random disturbances with unknown power spectral density in control inputs, an adaptive controller scheme is developed. The proposed technique uses the exponential practical stability concept which guarantees that the tracking error and its derivative converge to an arbitrarily small neighborhood of zero by appropriate tuning of the controller’s parameters. It is noteworthy that the proposed controller does not need any physical parameters of the robot. Simulation studies demonstrate the effectiveness and capability of the proposed method for trajectory tracking in the presence of unknown stochastic input disturbances and dynamical uncertainties.

scholarly journals Fault Tolerant Control for Uncertain Time-Delay Systems with a Trajectory Tracking Approach

2015 ◽  
Vol 2015 ◽  
pp. 1-10 ◽  
Author(s):  
ShiLei Zhao ◽  
Hong Guo ◽  
YuPeng Liu
Keyword(s):  
Time Delay ◽  
Trajectory Tracking ◽  
Fault Tolerant ◽  
Estimation Error ◽  
Tracking Error ◽  
Fault Tolerant Control ◽  
Delay Systems ◽  
Descriptor System ◽  
Fault Estimation ◽  
Time Delay Systems

This paper studies the problem of fault tolerant control by trajectory tracking for a class of linear constant time-delay systems. The aim is to design a control law by considering the fault detected by the observer to make the faulty system track the reference model even if faults occur. By considering two kinds of actuator faults, one constant and another time-varying, the corresponding proportional integral observers and active FTC control laws are designed, respectively. State tracking error, state estimation error, output estimation error, and fault estimation error are combined into a descriptor system. Based on Lyapunov-Krasovskii functional approach stability problems of the descriptor system are easily solved in terms of the Linear Matrix Inequalities (LMI). Finally, a numerical example is considered to prove the effectiveness in both cases.

Adaptive Tracking in Mobile Robots With Input-Output Linearization

2014 ◽  
Vol 136 (5) ◽  
Author(s):  
Cesáreo Raimúndez ◽  
Alejandro F. Villaverde ◽  
Antonio Barreiro
Keyword(s):  
Neural Network ◽  
Mobile Robots ◽  
Trajectory Tracking ◽  
Tracking Error ◽  
Adaptive Controller ◽  
Lyapunov Theory ◽  
Input Output ◽  
External Perturbations ◽  
Neural Network Controller ◽  
Input Output Linearization

This paper presents a neural network adaptive controller for trajectory tracking of nonholonomic mobile robots. By defining a point to follow (look-ahead control), the path-following problem is solved with input-output linearization. A computed torque plus derivative (PD) controller and a dynamic inversion neural network controller are responsible for reducing tracking error and adapting to unmodeled external perturbations. The adaptive controller is implemented through a hidden layer feed-forward neural network, with weights updated in real time. The stability of the whole system is analyzed using Lyapunov theory, and control errors are proven to be bounded. Simulation results demonstrate the good performance of the proposed controller for trajectory tracking under external perturbations.

A Control Design Method for Underactuated Mechanical Systems Using High-Frequency Inputs

2015 ◽  
Vol 137 (7) ◽  
Author(s):  
Sevak Tahmasian ◽  
Craig A. Woolsey
Keyword(s):  
Trajectory Tracking ◽  
High Frequency ◽  
Design Method ◽  
Tracking Error ◽  
Mechanical Systems ◽  
Control Design ◽  
Micro Air Vehicles ◽  
System Structure ◽  
Reference Trajectory ◽  
Underactuated Mechanical Systems

This paper presents a control design technique which enables approximate reference trajectory tracking for a class of underactuated mechanical systems. The control law comprises two terms. The first involves feedback of the trajectory tracking error in the actuated coordinates. Building on the concept of vibrational control, the second term imposes high-frequency periodic inputs that are modulated by the tracking error in the unactuated coordinates. Under appropriate conditions on the system structure and the commanded trajectory, and with sufficient separation between the time scales of the vibrational forcing and the commanded trajectory, the approach provides convergence in both the actuated and unactuated coordinates. The procedure is first described for a two degree-of-freedom (DOF) system with one input. Generalizing to higher-dimensional, underactuated systems, the approach is then applied to a 4DOF system with two inputs. A final example involves control of a rigid plate that is flapping in a uniform flow, a 3DOF system with one input. More general applications include biomimetic locomotion systems, such as underwater vehicles with articulating fins and flapping wing micro-air vehicles.

A Fault-Tolerant and Anti-Disturbance Control Based on Sliding Mode Iterative Learning Law for Attitude of Satellite

2013 ◽  
Vol 339 ◽  
pp. 10-15
Author(s):  
Jian Cheng LI ◽  
Tao Xi ◽  
Bo Wang
Keyword(s):  
Fault Tolerant ◽  
Sliding Mode ◽  
External Disturbance ◽  
Tracking Error ◽  
Iterative Learning ◽  
Reference Trajectory ◽  
Control Input ◽  
Orbit Control ◽  
On Line ◽  
Learning Law

To cope with the problem of the degradation of actuation effectiveness caused by actuator deflection or fault in the attitude and orbit control system (AOCS) of spacecraft on-orbit, an attitude fault-tolerant and anti-disturbance control scheme is proposed based on a sliding mode iterative learning law, in which the pseudo control input is applied to design the sliding mode controller to ensure the AOCS tracks a reference trajectory precisely after some fault occurred; By analyzing the Lyapunov stability, a novel adaptive iterative learning law is developed, which in term of the tracking error, determine some parameters in controller on-line to address the actuator failure and external disturbance. Numerical simulation experiments show that the fault-tolerant and anti-disturbance controller can ameliorate actuation malfunction and compensate the influence of external disturbance effectively.

scholarly journals Adaptive robust cross-coupling position synchronization control of a hydraulic press slider-leveling

2021 ◽  
Vol 104 (1) ◽  
pp. 003685042098703
Author(s):  
Chungeng Sun ◽  
Ruibo Yuan
Keyword(s):  
Control System ◽  
Robust Control ◽  
Trajectory Tracking ◽  
Tracking Error ◽  
Cross Coupling ◽  
Hydraulic Press ◽  
Synchronization Control ◽  
Reference Trajectory ◽  
Estimation Errors ◽  
The Stability

To achieve a high performance synchronized motion trajectory tracking of the hydraulic press slider-leveling electrohydraulic control system, an adaptive robust cross-coupling control strategy that incorporates the cross-coupling approach into adaptive robust control (ARC) architecture has been proposed. The primary objective of this study was describe that the nonlinear ARC controller together with a cross-coupling control (CCC) controller was integrated to solve the slider-leveling synchronization control system using four axes. A discontinuous projection-based ARC controller was constructed. A robust control method with dynamic compensation type fast adaptation was introduced to attenuate the effects of parameter estimation errors, unmodeled dynamics and disturbances, and improved the transient tracking performance of the system. The stability of the controller was proven by Lyapunov theory and the trajectory tracking error asymptotically convergences to zero. The simulation of a desired reference trajectory was included. The max tracking error of the proposed ARC controller of single axis was kept within—0.06 mm. The trajectory tracking error asymptotically converges to zero, which guaranteed the system would possess good transient behavior and confirmed the stability performance of the control system. The four axes synchronous errors of reference trajectory with cross-coupling controller indicated the maximum synchronization error of the proposed ARC + CCC controller between axis was within ±0.1 mm. The ARC together with a CCC controller for four hydraulic cylinders used parameter adaptation to obtain estimates of model parameters for reducing the extent of parametric uncertainties, and used a robust control law to attenuate the effects of parameter estimation errors, unmodeled dynamics, and disturbances. This study result shows that the proposed cross-coupling synchronization control scheme, together with the ARC law, provides excellent synchronization motion performance in a control system with four axes.

scholarly journals Neural networks-based adaptive finite-time control of switched nonlinear systems under time-varying actuator failures

2019 ◽  
Vol 2019 (1) ◽  
Author(s):  
Xikui Liu ◽  
Xiurong Shi ◽  
Yan Li
Keyword(s):  
Neural Networks ◽  
Nonlinear Systems ◽  
Finite Time ◽  
Fault Tolerant ◽  
Tracking Error ◽  
Adaptive Controller ◽  
Time Varying ◽  
Switched Nonlinear Systems ◽  
Actuator Failures ◽  
Time Control

AbstractThis paper is dedicated to neural networks-based adaptive finite-time control design of switched nonlinear systems in the time-varying domain. More specifically, by employing the approximation ability of neural networks system, an integrated adaptive controller is constructed. The main aim is to make sure the closed-loop system in arbitrary switching signals is semi-global practical finite-time stable (SGPFS). A backstepping design with a common Lyapunov function is proposed. Unlike some existing control schemes with actuator failures, the key is dealing with the time-varying fault-tolerant job for the switched system. It is also proved that all signals in the system are bounded and the tracking error can converge in a small field of the origin in finite time. A practical example is presented to illustrate the validity of the theory.

scholarly journals Indirect Neural Adaptive Control for Wheeled Mobile Robot

2019 ◽  
Vol 9 (1) ◽  
pp. 2138-2145
Keyword(s):  
Mobile Robot ◽  
Tracking Error ◽  
Approximation Error ◽  
Uncertain System ◽  
Adaptive Controller ◽  
Wheeled Mobile Robot ◽  
Reference Trajectory ◽  
Neural Adaptive Control ◽  
Intelligent Controller ◽  
Weight Learning

For a precise trajectory tracking of a wheeled mobile robot, accurate control of the position along a reference trajectory is essential. Therefore, this paper proposes an indirect neural adaptive controller for a nonholonomic mobile robot based on its dynamical model. This controller takes into account the approximation error. The use of the Lyapunov stability theorem and dynamical neural networks is indeed for deriving respectively stable learning laws for control and identification of a complex nonlinear dynamics system. The global tracking error is incorporated to adjust the neural weight learning laws to ensure the robustness of the system against approximation inaccuracy. Hence, the designed intelligent controller guarantees the convergence of both tracking and identification errors to zero. Simulation results illustrate the ability of the intelligent controller to assure the asymptotic stability of the closed-loop nonlinear uncertain system.

Sign in / Sign up

Export Citation Format

Share Document