Adaptive Robust Backstepping (ARB) Control for Quadrotor Robot in Presence of Payload Variation and Unknown Disturbances

2016 ◽  
Vol 9 (3) ◽  
pp. 417-434 ◽  
Author(s):  
Wang Chen ◽  
Song Bifeng ◽  
Meyer Nahon
Keyword(s):  
Unknown Disturbances ◽  
Quadrotor Robot ◽  
Payload Variation

scholarly journals Finite-Time Adaptive Higher-Order SMC for the Nonlinear Five DOF Active Magnetic Bearing System

Electronics ◽  
2021 ◽  
Vol 10 (11) ◽  
pp. 1333
Author(s):  
Sudipta Saha ◽  
Syed Muhammad Amrr ◽  
Abdelaziz Salah Saidi ◽  
Arunava Banerjee ◽  
M. Nabi
Keyword(s):  
Sliding Mode Control ◽  
Finite Time ◽  
Sliding Mode ◽  
A Priori ◽  
Magnetic Bearing ◽  
Active Magnetic Bearing ◽  
Mode Control ◽  
Adaptive Laws ◽  
Effective Performance ◽  
Unknown Disturbances

The active magnetic bearings (AMB) play an essential role in supporting the shaft of fast rotating machines and controlling the displacements in the rotors due to the deviation in the shaft. In this paper, an adaptive integral third-order sliding mode control (AITOSMC) is proposed. The controller suppresses the deviations in the rotor and rejects the system uncertainties and unknown disturbances present in the five DOF AMB system. The application of AITOSMC alleviates the problem of high-frequency switching called chattering, which would otherwise restrict the practical application of sliding mode control (SMC). Moreover, adaptive laws are also incorporated in the proposed approach for estimating the controller gains. Further, it also prevents the problem of overestimation and avoids the use of a priori assumption about the upper bound knowledge of total disturbance. The Lyapunov and homogeneity theories are exploited for the stability proof, which guarantees the finite-time convergence of closed-loop and output signals. The numerical analysis of the proposed strategy illustrates the effective performance. Furthermore, the comparative analysis with the existing control schemes demonstrates the efficacy of the proposed controller.

scholarly journals Implementation and intelligent gain tuning feedback–based optimal torque control of a rotary parallel robot

2021 ◽  
pp. 107754632110191
Author(s):  
Farzam Tajdari ◽  
Naeim Ebrahimi Toulkani
Keyword(s):  
Real Time ◽  
Tracking Error ◽  
Parallel Robot ◽  
Torque Control ◽  
Test Bed ◽  
Linear Quadratic ◽  
Control Effort ◽  
The Real ◽  
Unknown Disturbances ◽  
Quadratic Integral

Aiming at operating optimally minimizing error of tracking and designing control effort, this study presents a novel generalizable methodology of an optimal torque control for a 6-degree-of-freedom Stewart platform with rotary actuators. In the proposed approach, a linear quadratic integral regulator with the least sensitivity to controller parameter choices is designed, associated with an online artificial neural network gain tuning. The nonlinear system is implemented in ADAMS, and the controller is formulated in MATLAB to minimize the real-time tracking error robustly. To validate the controller performance, MATLAB and ADAMS are linked together and the performance of the controller on the simulated system is validated as real time. Practically, the Stewart robot is fabricated and the proposed controller is implemented. The method is assessed by simulation experiments, exhibiting the viability of the developed methodology and highlighting an improvement of 45% averagely, from the optimum and zero-error convergence points of view. Consequently, the experiment results allow demonstrating the robustness of the controller method, in the presence of the motor torque saturation, the uncertainties, and unknown disturbances such as intrinsic properties of the real test bed.

A computationally efficient adaptive robust control scheme for a quad-rotor transporting cable-suspended payloads

2021 ◽  
pp. 095441002110136
Author(s):  
Nasim Ullah ◽  
Irfan Sami ◽  
Wang Shaoping ◽  
Hamid Mukhtar ◽  
Xingjian Wang ◽  
...  
Keyword(s):  
Robust Control ◽  
Fractional Order ◽  
Sliding Mode ◽  
Adaptive Robust Control ◽  
Computationally Efficient ◽  
Control Scheme ◽  
Control Schemes ◽  
Adaptive Laws ◽  
Unknown Disturbances ◽  
The Stability

This article proposes a computationally efficient adaptive robust control scheme for a quad-rotor with cable-suspended payloads. Motion of payload introduces unknown disturbances that affect the performance of the quad-rotor controlled with conventional schemes, thus novel adaptive robust controllers with both integer- and fractional-order dynamics are proposed for the trajectory tracking of quad-rotor with cable-suspended payload. The disturbances acting on quad-rotor due to the payload motion are estimated by utilizing adaptive laws derived from integer- and fractional-order Lyapunov functions. The stability of the proposed control systems is guaranteed using integer- and fractional-order Lyapunov theorems. Overall, three variants of the control schemes, namely adaptive fractional-order sliding mode (AFSMC), adaptive sliding mode (ASMC), and classical Sliding mode controllers (SMC)s) are tested using processor in the loop experiments, and based on the two performance indicators, namely robustness and computational resource utilization, the best control scheme is evaluated. From the results presented, it is verified that ASMC scheme exhibits comparable robustness as of SMC and AFSMC, while it utilizes less sources as compared to AFSMC.

A Modified Algorithm for the Least Squares Identification

1983 ◽  
Vol 105 (1) ◽  
pp. 50-52
Author(s):  
C. Batur
Keyword(s):  
Least Squares ◽  
Least Squares Method ◽  
Mechanical Systems ◽  
Model Structure ◽  
Input Output ◽  
Unknown Parameters ◽  
Estimation Errors ◽  
Usual Practice ◽  
Unknown Disturbances ◽  
Modified Algorithm

To identify the dynamics of mechanical systems, the usual practice is to assume a certain model structure and try to estimate the unknown parameters of this model on the basis of input output observations. For mechanical systems operating under noisy industrial conditions, the number of unknowns of the problem exceeds the number of equations available. It is then inevitable that certain assumptions must be made on the unknown disturbances. This paper assumes that the only reliable feature of the disturbance is its independence of input. This yields a set of assumptions in excess of the minimal requirements and an endeavor has been made to exploit this excess to minimize the parameter estimation errors. Th resulting algorithm is similar to that of the Two Stage Least Squares method [1].

Derivative-dependent control of a fuel cell system with a safe implementation: An artificial delay approach

2021 ◽  
pp. 095965182110127
Author(s):  
Adrián Ramírez ◽  
Rifat Sipahi ◽  
César-Fernando Mendéz-Barrios ◽  
Jesús Leyva-Ramos
Keyword(s):  
Fuel Cell ◽  
Finite Differences ◽  
Clean Energy ◽  
Cell System ◽  
Fuel Cell System ◽  
Safe Operation ◽  
Control Activity ◽  
Renewable Energy Systems ◽  
Control Scheme ◽  
Unknown Disturbances

The growing demand for energy in recent decades has been followed by an increasing interest in clean energy sources as means to mitigate environmental pollution. Accordingly, renewable energy systems are required to not only guarantee safe operation but also have the ability to regulate their responses dynamically against operational variations and disturbances. Here, we propose a derivative-dependent controller to optimize this dynamic response in a fuel cell system. Since derivatives are in general difficult to measure or construct reliably, it is common practice to approximate them using finite-differences. This approximation, if not performed carefully, may produce undesired control activity and even instability. In this article, we propose to systematically engineer the finite-differences using artificial delays so as to avoid those undesired outcomes. This therefore guarantees a safe implementation of the control scheme. The objective of the proposed controller is to regulate the fuel cell’s output voltage while quickly compensating for parametric variations and unknown disturbances without the need of explicitly measuring or estimating them. Simulation results verify the advantages of the approach demonstrating that the controller with artificial delays is a preferable substitute for ideal derivative-dependent control implementations in fuel cell applications.

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