Trajectory tracking control for a quadrotor unmanned aerial vehicle based on dynamic surface active disturbance rejection control

Zhang, Yong; Chen, Zengqiang; Sun, Mingwei

doi:10.1177/0142331220909003

Cited by 45 publications

(28 citation statements)

References 23 publications

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“…Example 4: While LADRC is faced with step response, the instantaneous value of control signal tends to surge and exceed the tolerance limit of actuator due to large deviation between setting value and output value (Zheng and Goforth, 2010). In this paper, tracking differentiator (Zhang et al, 2020) is introduced to the composite controller to smooth the input signal. The input signal changes to a step signal that steps to 10 in the first second.…”

Section: Simulation Verification and Discussionmentioning

confidence: 99%

Linear active disturbance rejection control for hysteresis compensation based on backpropagation neural networks adaptive control

Liu

Zhao

Wu³

et al. 2020

Transactions of the Institute of Measurement and Control

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This paper proposes a compound control framework for non-affine nonlinear systems facing hysteresis disturbance. The controller consists of linear active disturbance rejection control (LADRC) and backpropagation (BP) neural networks adaptive control. BP neural networks are utilized to arbitrarily approximate the uncertainty nonlinear caused by the deviation of control parameter from its nominal value and LADRC is designed to real-time estimate and compensate the disturbance with vast matched and mismatched uncertainties including unknown internal system dynamic uncertainty and external hysteresis disturbance therein. Combining the adaptive neural networks design with LADRC design techniques, a new dual-channel composite controller scheme is developed herein whereby adaptive neural networks are used as feed-forward inverse control and LADRC as closed-loop feedback control. Furthermore, as compared with a traditional control algorithm, the proposed BP-LADRC dual-channel composite controller can guarantee that the desired signal can be tracked with a small domain of the origin and it is confirmed to be effective under Lyapunov stability theory and MATLAB simulations.

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Section: Simulation Verification and Discussionmentioning

confidence: 99%

Linear active disturbance rejection control for hysteresis compensation based on backpropagation neural networks adaptive control

Liu

Zhao

Wu³

et al. 2020

Transactions of the Institute of Measurement and Control

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“…In order to highlight the superiority of the proposed observer-controller with previous related works, the ESO (Talole et al, 2009; Zhang et al, 2020) is applied to the described robot manipulator. It is a model-free approach in which the lumped uncertainty is considered as an augmented state of system.…”

Section: Simulationmentioning

confidence: 99%

Observer-based adaptive control of robot manipulators using reinforcement learning and the Fourier series expansion

Khodamipour

Khorashadizadeh

Farshad

2021

Transactions of the Institute of Measurement and Control

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Designing observer-controller structures for nonlinear system with unknown dynamics such as robotic systems is among popular research fields in control engineering. The novelty of this paper is in presenting an observer-based model-free controller for robot manipulators using reinforcement learning (RL). The proposed controller calculates the desired motor voltages that fulfil a satisfactory tracking performance. Moreover, the uncertainties and nonlinearities in the observer model and RL controller are estimated and compensated for by using the Fourier series expansion. Simulation results and comparison with the previous related works (extended state observer and radial basis function neural networks) indicate the satisfactory performance of the proposed method.

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“…Therefore, the LADRC has been widely used as a control for a large class of processes (Zheng et al, 2007c). There are so many advantages of ADRC over the standard PID control, hence the ADRC has successfully implemented in applications like motion control (Goforth, 2004), MEMS gyroscope (Dong et al, 2008a(Dong et al, , 2008bQing and Zhiqiang, 2011;Zheng et al, 2007bZheng et al, , 2009b, chemical-process control Zheng et al (2007aZheng et al ( , 2009a, boiler-turbine unit (Guannan et al, 2011), alstom gasifier (Huang et al, 2013), power plant operation (Sun et al, 2019a), variable speed micro-hydro plants (Guo et al, 2019), electro-hydraulic servo systems (Yang, 2020), quadrotor trajectory tracking (Wang et al, 2020;Zhang et al, 2020), and so forth.…”

Section: Introductionmentioning

confidence: 99%

Predictive extended state observer-based robust control for uncertain linear systems with experimental validation

Pawar

Chile²,

Patre³

2021

Transactions of the Institute of Measurement and Control

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This paper describes a predictive extended state observer-based robust control for uncertain process control applications. The technique discussed in the article uses the extended state observer (ESO) that can estimate the dynamics of the system as well as total disturbance encountered in the system. The disturbances, parametric uncertainties associated with the processes are treated as an extended state variable to be estimated in real-time using ESO. With the implementation of a predictive algorithm with an ESO, the proposed control structure extends its applicability to time-delayed higher-order processes. The proposed control technique utilizes the simple first-order modified predictive ESO even in the case of higher-order processes. The novel predictive ESO is able to obtain a delay less estimation of total disturbance as compared with existing normal ESO. Also, novel predictive ESO maintains its stability margin in presence of time delay as well provides better response as compared with normal ESO. Numerical simulations show that the proposed scheme provides a significant improvement in transient response as compared with internal model control-based proportional-integral-derivative (IMC-PID) control. The proposed scheme requires less knowledge of the process as compared with the IMC-PID structure. The implementation of the proposed control is tested on a real-life single tank level control system. Because of its merit, the suggested technique can be used as automatic for online tuning, as it is less reliant on the process model.

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Trajectory tracking control for a quadrotor unmanned aerial vehicle based on dynamic surface active disturbance rejection control

Cited by 45 publications

References 23 publications

Linear active disturbance rejection control for hysteresis compensation based on backpropagation neural networks adaptive control

Linear active disturbance rejection control for hysteresis compensation based on backpropagation neural networks adaptive control

Observer-based adaptive control of robot manipulators using reinforcement learning and the Fourier series expansion

Predictive extended state observer-based robust control for uncertain linear systems with experimental validation

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