Obstacle avoidance and active disturbance rejection control for a quadrotor

Chang, Kai; Xia, Yuanqing; Huang, Kaoli; Ma, Dailiang

doi:10.1016/j.neucom.2016.01.033

Cited by 68 publications

(25 citation statements)

References 23 publications

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“…In it, 1 , 2 are the system state and its first derivative, respectively; V is the control reference; ℎ 0 is the filtering factor; is the time ruler; sign is a sign function; and ℎ is a constructed nonlinear function shown in (2) [16,32].…”

Section: The Adrc Control Methodsmentioning

confidence: 99%

“…In it, is the error between the estimated state and system output; 1 , 2 , and 3 are the estimations of the system states ; 01 , 02 , and 03 are gain coefficients; is the control signal; is the system output; and are the parameters of function; and 1 are the outputs of function; is another constructed nonlinear function shown in (4) [16,32].…”

Section: The Adrc Control Methodsmentioning

confidence: 99%

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An ADRC Method for Noncascaded Integral Systems Based on Algebraic Substitution Method and Its Structure

Huang

Liu

et al. 2018

Mathematical Problems in Engineering

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The Active Disturbance Rejection Control (ADRC) prefers the cascaded integral system for a convenient design or better control effect and takes it as a typical form. However, the state variables of practical system do not necessarily have a cascaded integral relationship. Therefore, this paper proposes an algebraic substitution method and its structure, which can convert a noncascaded integral system of PID control into a cascaded integral form. The adjusting parameters of the ADRC controller are also demonstrated. Meanwhile, a numerical example and the oscillation control of a flexible arm are demonstrated to show the conversion, controller design, and control effect. The converted system is proved to be more suitable for a direct ADRC control. In addition, for the numerical example, its control effect for the converted system is compared with a PID controller under different disturbances. The result shows that the converted system can achieve a better control effect under the ADRC than that of a PID. The theory is a guide before practice. This converting method not only solves the ADRC control problem of some noncascaded integral systems in theory and simulation but also expands the application scope of the ADRC method.

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Section: The Adrc Control Methodsmentioning

confidence: 99%

Section: The Adrc Control Methodsmentioning

confidence: 99%

An ADRC Method for Noncascaded Integral Systems Based on Algebraic Substitution Method and Its Structure

Huang

Liu

et al. 2018

Mathematical Problems in Engineering

View full text Add to dashboard Cite

show abstract

“…Then, he constructed an approximated synthetic function fhan for the time-optimal control, 19 as shown in Equation (12). [19][20][21] This function can quickly track an input signal without overshoot and chattering and can produce a good differential signal, which can be expressed as…”

Section: The Td Methodsmentioning

confidence: 99%

A data‐driven approximate solution to the model‐free HJB equation

Huang

Zhang

et al. 2017

Optim Control Appl Methods

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Summary It is generally impossible to analytically solve the Hamilton‐Jacobi‐Bellman (HJB) equation of an optimal control system. With the coming of the big‐data era, this paper first derives a new data‐driven and model‐free Hamilton function for the HJB equation. Then, a data‐driven tracking differentiator method is proposed to solve the Hamilton function. Finally, the simulation for a classic example shows that the optimal control policy can be approximated with the proposed method. Thus, an online data‐driven model‐free approximate solution to the HJB equation is achieved. This method is only driven by the measured system states. All other variables and derivatives can be derived from the data‐driven model‐free Hamilton function and tracking differentiator. The method has a complete mathematical support and works like a controller. It does not need neural networks and has no training or iterative convergence problem. Thus, this paper adds an online data‐driven model‐free method to the existing literature on the approximate solution to the HJB equation.

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“…APFs have well known problems when dealing with robot navigation, among which is the presence of local minima in the field . Nonetheless, APFs have been widely used also for reactive obstacle avoidance of UAVs …”

Section: Autonomous Navigationmentioning

confidence: 99%

Post‐disaster assessment with unmanned aerial vehicles: A survey on practical implementations and research approaches

Recchiuto

Sgorbissa

2017

Journal of Field Robotics

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In this paper, we provide a review of the principal aspects related to search & rescue (SAR) with unmanned aerial vehicles (UAVs), with particular interest in the phase of post-disaster assessment (PDA). Some areas of interest related to this topic have been chosen for the analysis: the aerial platforms used in the field, multirobot software architectures, onboard sensors and simultaneous localization and mapping approaches, terrain coverage algorithms, autonomous navigation techniques, and human-swarm interfaces. All these aspects have been analyzed with respect to the state-of-the-art, and also in relation to the project PRISMA, which focuses on the development and deployment of robots and autonomous systems that can operate in emergency scenarios, with a specific reference to monitoring and real-time intervention

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Obstacle avoidance and active disturbance rejection control for a quadrotor

Cited by 68 publications

References 23 publications

An ADRC Method for Noncascaded Integral Systems Based on Algebraic Substitution Method and Its Structure

An ADRC Method for Noncascaded Integral Systems Based on Algebraic Substitution Method and Its Structure

A data‐driven approximate solution to the model‐free HJB equation

Post‐disaster assessment with unmanned aerial vehicles: A survey on practical implementations and research approaches

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