Null-space-based modulated reference trajectory generator for multi-robots formation in obstacle environment

Yao, Ping; Wei, Yadong; Zhao, Zhiyao

doi:10.1016/j.isatra.2021.05.033

Cited by 23 publications

(13 citation statements)

References 39 publications

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“…For the O-FFSG algorithm, introducing the forgetting factor can enhance the parameter estimation accuracy. The proposed parameter identification algorithm for feedback nonlinear stochastic systems in this article can be extended to other systems [92][93][94][95][96] and can be applied to other control and information processing systems [97][98][99][100][101][102][103][104][105] and so on.…”

Section: Discussionmentioning

confidence: 99%

Overall recursive least squares and overall stochastic gradient algorithms and their convergence for feedback nonlinear controlled autoregressive systems

Wei

Xiao

et al. 2022

Intl J Robust & Nonlinear

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This article deals with the problems of the parameter estimation for feedback nonlinear controlled autoregressive systems (i.e., feedback nonlinear equation‐error systems). The bilinear‐in‐parameter identification model is formulated to describe the feedback nonlinear system. An overall recursive least squares algorithm is developed to handle the difficulty of the bilinear‐in‐parameter. For the purpose of avoiding the heavy computational burden, an overall stochastic gradient algorithm is deduced and the forgetting factor is introduced to improve the convergence rate. Furthermore, the convergence analysis of the proposed algorithms are established by means of the stochastic process theory. The effectiveness of the proposed algorithms are illustrated by the simulation example.

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Section: Discussionmentioning

confidence: 99%

Overall recursive least squares and overall stochastic gradient algorithms and their convergence for feedback nonlinear controlled autoregressive systems

Wei

Xiao

et al. 2022

Intl J Robust & Nonlinear

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“…Among them, the formation control problem is associated with the design of stabilizing, regulating, and tracking controllers to make a team of vehicles or mobile robots track or reach desirable postures relative to one or more reference points. Three well-known formation control strategies for multiple WMRs are behavioral-based, 1,2 virtual structure 3,4 and leader-following 5,6 approaches. A sliding-mode formation control scheme has been proposed for cooperative autonomous mobile robots in Reference 7.…”

Section: Introductionmentioning

confidence: 99%

“…Recently, many interesting theoretical results on the formation control of mobile robots have been reported by the researchers in the literature 12–21 . Based on a deep literature review by the author including, 1–33 the following shortcomings are evident in the previous works: According to a careful review of the literature including, 1–33 the majority of the literature is devoted to the formation control of differentially driven wheeled mobile robots which is a particular case of nonholonomic type ( m , s ) WMRs, that is, type (2, 0) robot. Since design of the previously proposed controllers is dependent on robot's kinematic model, they could not be applied to other types of mobile robots with different steerability, mobility, and kinematic models.…”

Section: Introductionmentioning

confidence: 99%

A novel saturated PID‐type observer‐based controller for wheeled mobile robots with a guaranteed performance considering path curvature

Shojaei

2023

Intl J Robust & Nonlinear

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This article proposes a novel saturated PID‐type formation controller for a class of nonholonomic wheeled mobile robots (WMRs) of type (m, s) with mobility m and steerability s. Since the path curvature causes a severe deviation of the followers from the leaders' trajectory in the corners, a new definition of output equations is suggested to construct the coordinates of a virtual reference point in the front of each follower based on the concept of the extended look‐ahead control that utilizes a corrective angle to compensate the trajectory curvature efficiently. A nonlinear transformation and the robot's kinematic and dynamic equations are heuristically combined to develop a novel second‐order Euler–Lagrange formulation of unconstrained errors from constrained errors. Then, a novel saturated PID‐type controller is proposed for WMRs to build a desirable formation with a prescribed performance. An innovative nonlinear observer is presented to estimate followers' velocities. An efficient mixture of a radial basis function neural network (RBFNN) and an adaptive mechanism is proposed to compensate for model uncertainties, external disturbances, and network approximation errors. Lyapunov's direct method verifies semi‐global uniform ultimate boundedness stability by estimating a gain‐dependent region of attraction. Numerical simulations finally show the efficiency of the proposed controller.

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“…At the same time, multirotors are flexible in flight, with hovering and slow-flying functions, and are used in electric power inspection [6], border surveillance [7], modern agriculture [8], forest fire control [9], flood control, and disaster resistance [10], playing an increasingly important role in other fields too. However, the failures of multirotors such as communication faults, sensor failures, and power system anomalies are inevitable [11][12][13][14]. These failures may lead to mission interruption, multi-rotor crashes, and even casualties.…”

Section: Introductionmentioning

confidence: 99%

Real-Time Safety Decision-Making Method for Multirotor Flight Strategies Based on TOPSIS Model

2022

Self Cite

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Multirotors play an important role in electric power inspection, border control, modern agriculture, forest fire fighting, flood control, disaster prevention, etc. Multirotor failures, such as a communication fault, a sensor failure, or a power system anomaly, may well lead to mission interruption, multirotor crashes, and even casualties. To ensure flight safety, a multirotor decision module should be established to prevent or reduce the adverse effects caused by failure. Therefore, this paper proposes a real-time safety decision-making method for multirotor flight strategies based on the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS). Firstly, the flight of the multirotor was simulated based on the Rflysim UAV flight simulation platform, and a fault-injection module was constructed to simulate different types of faults, so as to realize real-time monitoring of the flight status of the multirotor, and to collect flight data under various faults to establish condition assessment information sources. Then, based on the random forest algorithm, a failure level classification model of the multirotor was constructed, the model was trained and verified by inputting flight data of three types of safety level failures, and the model effectively classified the failure levels of the multirotor. Under this framework, a real-time safety decision-making model for the multirotor based on the TOPSIS model was constructed to realize the flight safety decision-making of the multirotor under different faults. This method can effectively realize the real-time decision-making for the flight strategy of a multirotor. By comparison with other models, the classification accuracy of the failure level classification model is higher, and the consideration of flight decision-making is more comprehensive and accurate, thus effectively ensuring the flight safety of the multirotor.

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Null-space-based modulated reference trajectory generator for multi-robots formation in obstacle environment

Cited by 23 publications

References 39 publications

Overall recursive least squares and overall stochastic gradient algorithms and their convergence for feedback nonlinear controlled autoregressive systems

Overall recursive least squares and overall stochastic gradient algorithms and their convergence for feedback nonlinear controlled autoregressive systems

A novel saturated PID‐type observer‐based controller for wheeled mobile robots with a guaranteed performance considering path curvature

Real-Time Safety Decision-Making Method for Multirotor Flight Strategies Based on TOPSIS Model

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