A singularity‐free online neural network‐based sliding mode control of the fixed‐wing unmanned aerial vehicle optimal perching maneuver

Song, Yansui; Tang, Yong; Ma, Bin; Xu, Bin

doi:10.1002/oca.2873

Cited by 6 publications

(9 citation statements)

References 24 publications

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“…Assumptions 1 and 2 imply that the perturbation w d (t) and nonlinear dynamic f (y) are dependent on the position and velocity states q(t), q(t) of the cart-pendulum system and an upper bound. The condition in Assumption 1 indicates that the perturbation may be constituted by internal uncertainties of the system and exogenous disturbances, which is in accordance with practical cases of a robot according to (1). While the condition in Assumption 2 is proposed based on the formulation of ( 13) and Assumption 1.…”

Section: Adaptive Sliding Mode Controller Designmentioning

confidence: 77%

“…Remark Note that there are some advantages of the proposed sliding mode surface in () as follows. Compared with traditional sliding mode surface proposed in References 1‐3,18‐21, the sliding mode surface developed in this paper can deal with the fixed‐time stability problem of systems. Moreover, compared with fixed‐time sliding mode surface proposed in References 4,27,28, the proposed sliding mode surface reduce the usage of signum function which means the proposed method may save the computational resource and alleviate the chattering phenomenon of control inputs.…”

Section: Adaptive Sliding Mode Controller Designmentioning

confidence: 99%

“…In recent years, many kinds of advanced control methods have been developed to compensate for the effects of the imperfections. In terms of a large number of relevant stability and tracking results for various systems, numerous efficient control methods are developed based on the techniques of sliding-mode control, [1][2][3][4] adaptive control, [5][6][7] robust control, [8][9][10] observer-based control, 11,12 intelligent control, [13][14][15] model predictive control 16,17 and so forth.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Fixed‐time linear quadratic adaptive sliding mode control for a class of cart‐pendulum robots

Jin

Liu

et al. 2022

Optim Control Appl Methods

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This article is concerned with the fixed-time path-following control problem of a perturbed cart-pendulum robot with linear quadratic (LQ) performance optimization. In order to ensure the displacements of cart and pendulum of the robot following the desired paths within fixed-time, adaptive anti-perturbation sliding mode control schemes with a novel fixed-time sliding mode surface are developed to withstand the adverse influence of perturbations. Furthermore, an adaptive radial basis function neural network (RBFNN)-based sliding mode control algorithm is employed to approximate the continuous perturbations, so that the path-following LQ control problem can be further dealt with. The fixed-time stability and path-following results of the cart-pendulum robotic system are achieved by Lyapunov stability theory. A simulation example for a heavy material handling agricultural robot is proposed to verify the effectiveness of the developed method.

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Section: Adaptive Sliding Mode Controller Designmentioning

confidence: 77%

Section: Adaptive Sliding Mode Controller Designmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Fixed‐time linear quadratic adaptive sliding mode control for a class of cart‐pendulum robots

Jin

Liu

et al. 2022

Optim Control Appl Methods

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“…The decentralized control issues of nonlinear large‐scale systems are investigated via critic‐only adaptive dynamic programming learning methods 22 . A singularity‐free online neural network‐based sliding mode control method is proposed to realize the fixed‐wing perch maneuver 23 …”

mentioning

confidence: 99%

“…18 The problem of the post-stall pitching maneuver of an aircraft with lower deflection frequency of control actuator is studied by considering the unsteady aerodynamic disturbances. 19 The fourth group of papers [20][21][22][23] focuses on neural networks and deep neural networks learning methods for optimal control. An optimal tracking control problem for the injection flow front position arising in the filling process in the injection molding machine is considered, and an intelligent real-time optimal control method based on deep neural networks is developed for the online tracking of the flow front position to improve the efficient production process of the plastics.…”

mentioning

confidence: 99%

Data‐based learning control for optimization of nonlinear systems

Wei

Song

Zhang

et al. 2023

Optim Control Appl Methods

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With the development of science and technology, practical systems such as the power systems, traffic systems, robot manipulator systems, etc., have become more complex. Therefore, it is difficult to build practical systems by accurate models. Under the lack of accurate process models, using system data to improve system performance and learn optimal decisions becomes very important. Through the recent years, data-based learning control theories and technologies have widely been investigated, including adaptive dynamic programming, reinforcement learning, iterative learning control, and so on. Data-based methods require the system data instead of the accurate knowledge of system dynamics that can be considered as model-free learning control methods. The data-based methods are effective solutions for the optimal control of nonlinear systems, which motivate this special issue.This special issue aims to collect and present original research dealing with data-based learning and their applications for optimization and control problems. The first group of papers [1][2][3][4][5][6][7] focuses on data-based control theory, approaches, and applications. A fuzzy model predictive control approach is proposed for stick-slip type piezoelectric actuator to realize the precise control of the end effector. 1 A systematic online adaptive dynamic programming control framework is proposed for smart buildings control to ensure hard constraints to be satisfied. 2 A multi-verse optimizer tuned PI-type active disturbance rejection generalized predictive control method is described for the motion control problems of ships. 3 The sufficient optimality conditions for the optimal controls are established under some convexity assumptions. 4 A receding-horizon reinforcement learning algorithm is proposed for near-optimal control of continuous-time systems under control constraints. 5 In order to solve the interference compensation control problem of a class of nonlinear systems, a method based on memory data is introduced to suppress interference greatly. 6 A new controller design method is proposed for the trajectory tracking problem of robots with imprecise dynamic properties and interference. 7 The second group of papers 8-12 considers iterative learning identification and iterative learning control. An iterative learning control approach is proposed for linear parabolic distributed parameter systems with multiple actuators and multiple sensors. 8 The quantized data-based iterative learning tracking control problem is studied for nonlinear networked control systems with signals quantization and denial-of-service attacks. 9 The output tracking problem is considered for a class of nonlinear parabolic distributed parameter systems with moving boundaries. 10 A just-in-time learning based dual heuristic programming algorithm is proposed to optimize the control performance of autonomous wheeled mobile robots under faults or disturbances. 11 A novel optimal constraint-following controller is proposed for uncertain mechanical systems. 12 The third...

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Event-Based Guidance and Incremental Control with Application to Fixed-wing Unmanned Aerial Vehicle Perched Landing Maneuvers

Song,

Sun,

Tao

et al. 2024

J Intell Robot Syst

Self Cite

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Considering the nonlinearity and unknown dynamics of fixed-wing unmanned aerial vehicles in perched landing maneuvers, an event-based online guidance and incremental control scheme is proposed. The guidance trajectory for perched landing must be dynamically feasible therefore an event-based trapezoidal collocation point optimization method is proposed. Introduction of the triggering mechanism for the rational use of computing resources to improve PL accuracy. Furthermore, a filter-based incremental nonlinear dynamic inverse (F-INDI) control with state transformation is proposed to achieve robust trajectory tracking under high angle of attack (AOA). The F-INDI uses low-pass filters to obtain incremental dynamics of the system, which simplifies the design process. The state transformation strategy is to convert the flight-path angle, AOA and velocity into two composite dynamics, which avoids the sign reversal problem of control gain under high AOA. The stability analysis shows that the original states can be controlled only by controlling the composite state. Simulation results show that the proposed scheme achieves high perched landing accuracy and a reliable trajectory tracking control.

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A singularity‐free online neural network‐based sliding mode control of the fixed‐wing unmanned aerial vehicle optimal perching maneuver

Cited by 6 publications

References 24 publications

Fixed‐time linear quadratic adaptive sliding mode control for a class of cart‐pendulum robots

Fixed‐time linear quadratic adaptive sliding mode control for a class of cart‐pendulum robots

Data‐based learning control for optimization of nonlinear systems

Event-Based Guidance and Incremental Control with Application to Fixed-wing Unmanned Aerial Vehicle Perched Landing Maneuvers

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