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 Direct Adaptive Tracking Control for a Class of Pure-Feedback Stochastic Nonlinear Systems Based on Fuzzy-Approximation

2014 ◽  
Vol 2014 ◽  
pp. 1-10
Author(s):  
Huanqing Wang ◽  
Xiaoping Liu ◽  
Qi Zhou ◽  
Hamid Reza Karimi
Keyword(s):  
Nonlinear Systems ◽  
Tracking Control ◽  
Controller Design ◽  
Tracking Error ◽  
Small Neighborhood ◽  
Adaptive Controller ◽  
Stochastic Nonlinear Systems ◽  
Fuzzy Logic Systems ◽  
Adaptive Tracking Control ◽  
Adaptive Tracking

The problem of fuzzy-based direct adaptive tracking control is considered for a class of pure-feedback stochastic nonlinear systems. During the controller design, fuzzy logic systems are used to approximate the packaged unknown nonlinearities, and then a novel direct adaptive controller is constructed via backstepping technique. It is shown that the proposed controller guarantees that all the signals in the closed-loop system are bounded in probability and the tracking error eventually converges to a small neighborhood around the origin in the sense of mean quartic value. The main advantages lie in that the proposed controller structure is simpler and only one adaptive parameter needs to be updated online. Simulation results are used to illustrate the effectiveness of the proposed approach.

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.

Experimental Verification of Adaptive Dominant Type Hybrid Adaptive and Learning Controller for Trajectory Tracking of Robot Manipulators

2013 ◽  
Vol 25 (4) ◽  
pp. 737-747 ◽  
Author(s):  
Munadi ◽  
◽  
Tomohide Naniwa ◽  
Keyword(s):  
Adaptive Control ◽  
Motion Control ◽  
Trajectory Tracking ◽  
Robot Manipulator ◽  
Robot Manipulators ◽  
Tracking Error ◽  
Control Input ◽  
Dominant Type ◽  
Dynamical Parameters ◽  
Small Robot

This paper presents an experimental study to verify an adaptive dominant type hybrid adaptive and learning controller for acquiring an accurate trajectory tracking of periodic desired trajectory of robot manipulators. The proposed controller is developed based on combining the model-based adaptive control (MBAC), repetitive learning control (RLC) and proportionalderivative (PD) control in which the MBAC input becomes dominant than other inputs. Dominance of adaptive control input gives the advantage that the proposed controller could adjust the feed-forward motion control input immediately after changing the desired motion or load of the manipulator. In motion control law, the proposed controller uses only one vector to estimate the unknown dynamical parameters. It makes the proposed controller as a simpler hybrid adaptive and learning controller which does not need much computational power and also is easily be implemented for real applications of robot manipulators. The proposed controller is verified through experiments on a four-link small robot manipulator as representation of a scale robot manipulator to ensure this controller can be applied in the real applications of robot manipulators. The experimental results show the effectiveness of the proposed controller by indicating the position tracking error approaches to zero.

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.

Adaptive tracking control of stochastic nonlinear systems with unknown time-varying powers

2021 ◽  
pp. 014233122098223
Author(s):  
Huijuan Li ◽  
Wuquan Li ◽  
Jianzhong Gu
Keyword(s):  
Nonlinear Systems ◽  
Design Method ◽  
Tracking Error ◽  
Small Neighborhood ◽  
Adaptive Controller ◽  
Upper And Lower Bounds ◽  
Stochastic Nonlinear Systems ◽  
Time Varying ◽  
Adaptive Tracking Control ◽  
Output Tracking

This paper investigates the adaptive output tracking problem for a class of high-order stochastic nonlinear systems with unknown time-varying powers and nonlinear parameterized uncertainties. By using the parameter separation technique and adding a power integrator design method, an adaptive controller with upper and lower bounds of the unknown time-varying power is successfully designed to guarantee that all the states of the closed-loop system are bounded in probability and the output tracking error can be regulated into a small neighborhood of the origin in probability. Finally, a simulation example is provided to illustrate the effectiveness of the designed controllers.

Modeling and Adaptive Tracking Control of a Quadrotor UAV

2012 ◽  
Vol 2 (4) ◽  
pp. 58-81 ◽  
Author(s):  
Mostafa Mohammadi ◽  
Alireza Mohammad Shahri ◽  
Zahra Boroujeni
Keyword(s):  
Trajectory Tracking ◽  
Attitude Control ◽  
Movement Control ◽  
Adaptive Controller ◽  
Inverse Kinematic ◽  
Physical Parameters ◽  
Outer Loop ◽  
Control Loops ◽  
Adaptive Tracking Control ◽  
Translational Movement

The dynamics of UAV’s have special features that can complicate the process of designing a trajectory tracking controller. In this paper, after modelling the quadrotor as a VTOL UAV, a nonlinear adaptive controller is designed to solve trajectory tracking problem in the presence of parametric and nonparametric uncertainties. This controller doesn’t need knowing any physical parameters of the quadrotor, and there isn’t need to retune the controller for various payloads. In this approach, the control of a quadrotor is performed by using decentralized adaptive controllers in the inner (attitude control) and outer (translational movement control) loops. The outer loop generates the instantaneous desired angles for inner loop. The inner loop stabilizes the orientation of the vehicle. Inverse kinematic of robot is used to convert outputs of the outer loop to inputs of the inner loop. The controller needs some unknown physical parameter to generate control signals. A robust parameter identifier estimates the required parameters for the outer control loops. Simulations are carried out to illustrate the robustness and tracking performance of the controllers.

Compensatory model predictive control for post-impact trajectory tracking via active front steering and differential torque vectoring

2020 ◽  
pp. 095440702097908
Author(s):  
Mingcong Cao ◽  
Chuan Hu ◽  
Rongrong Wang ◽  
Jinxiang Wang ◽  
Nan Chen
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Trajectory Tracking ◽  
Tracking Error ◽  
Collision Probability ◽  
Critical Conditions ◽  
Physical Analysis ◽  
Compensatory Model ◽  
Torque Vectoring ◽  
Post Impact

This paper investigates the trajectory tracking control of independently actuated autonomous vehicles after the first impact, aiming to mitigate the secondary collision probability. An integrated predictive control strategy is proposed to mitigate the deteriorated state propagation and facilitate safety objective achievement in critical conditions after a collision. Three highlights can be concluded in this work: (1) A compensatory model predictive control (MPC) strategy is proposed to incorporate a feedforward-feedback compensation control (FCC) method. Based on the definite physical analysis, it is verified that adequate reverse steering and differential torque vectoring render more potentials and flexibility for vehicle post-impact control; (2) With compensatory portions, the deteriorated states after a collision are far beyond the traditional stability envelope. Hence it can be further manipulated in MPC by constraint transformation, rather than introducing soft constraints and decreasing the control efforts on tracking error; (3) Considering time-varying saturation on input, input rate, and slip ratio, the proposed FCC-MPC controller is developed to improve faster deviation attenuation both in lateral and yaw motions. Finally two high-fidelity simulation cases implemented on CarSim-Simulink conjoint platform have demonstrated that the proposed controller has the advanced capabilities of vehicle safety improvement and better control performance achievement after severe impacts.

Decentralized adaptive tracking control for high-order interconnected stochastic nonlinear time-varying delay systems with stochastic input-to-state stable inverse dynamics by neural networks

2019 ◽  
Vol 41 (13) ◽  
pp. 3612-3625 ◽  
Author(s):  
Wang Qian ◽  
Wang Qiangde ◽  
Wei Chunling ◽  
Zhang Zhengqiang
Keyword(s):  
Neural Networks ◽  
Tracking Control ◽  
Large Scale ◽  
Inverse Dynamics ◽  
Small Neighborhood ◽  
High Order ◽  
Interconnected Systems ◽  
Delay Systems ◽  
Rbf Neural Networks ◽  
Stochastic Input

The paper solves the problem of a decentralized adaptive state-feedback neural tracking control for a class of stochastic nonlinear high-order interconnected systems. Under the assumptions that the inverse dynamics of the subsystems are stochastic input-to-state stable (SISS) and for the controller design, Radial basis function (RBF) neural networks (NN) are used to cope with the packaged unknown system dynamics and stochastic uncertainties. Besides, the appropriate Lyapunov-Krosovskii functions and parameters are constructed for a class of large-scale high-order stochastic nonlinear strong interconnected systems with inverse dynamics. It has been proved that the actual controller can be designed so as to guarantee that all the signals in the closed-loop systems remain semi-globally uniformly ultimately bounded, and the tracking errors eventually converge in the small neighborhood of origin. Simulation example has been proposed to show the effectiveness of our results.

scholarly journals Adaptive PD Sliding Mode Control For 4 DOF SCARA Variant

2021 ◽  
Author(s):  
Manjeet Tummalapalli
Keyword(s):  
Sliding Mode Control ◽  
Trajectory Tracking ◽  
High Speed ◽  
Sliding Mode ◽  
Adaptive Controller ◽  
Degree Of Freedom ◽  
Tracking Performance ◽  
Advantages And Disadvantages ◽  
Model Free ◽  
Adaptive Law

This project proposes a new SCARA variant with 4 degree of freedom. The proposed variant is achieved by swapping joint 2 and joint 3 of the standard SCARA robots. An adaptive controller is defined based on the advantages and disadvantages of PD, and SMC controllers.The purpose of the project is to understand the dynamics of the variant and to track the performance for trajectories. Simulations for tracking performance are carried under linear and circular trajectories. The variant is studied over the three controllers; PD, PD-SMC and A-PD-SMC. The variant under the adaptive controller is most efficient in terms of tracking performance and the control inputs to the system. The system is simulated under high speed and with the influence of friction at the joints. The control gains are held constant for both the trajectories and hence the controller is able to perform good under changing trajectories. Due to the use of the adaptive law, the system is at the ease of implementation and since no priori knowledge if the system is needed, it is model free. Therefore, the proposed adaptive PD-SMC has proven to provide good, robust trajectory tracking.

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