A Frequency-Domain Path-Following Method for Discrete Data-Based Paths

This paper presents a novel frequency-domain approach for path following problem, specifically designed to handle paths described by discrete data. The proposed algorithm utilizes the fast Fourier Transform (FFT) to process the discrete path data, enabling the construction of a non-singular guiding vector field. This vector field serves as a reference direction for the controlled robot, offering the ability to adapt to different levels of precision. Additionally, the frequency-domain nature of the vector field allows for the reduction of computational complexity and effective noise suppression. The efficacy of the proposed approach is demonstrated through a numerical simulation, and theoretical analysis provides an upper bound for the ultimate mean-square path-following error

Non-singular Cooperative Guiding Vector Field Under a Homotopy Equivalence Transformation

The present article advances the concept of a non-singular cooperative guiding vector field under a homotopy equivalence transformation. Firstly, the derivation of a guiding vector field, based on a non-singular vector field, is elaborated to navigate a transformed path from another frame. The existence of such vector fields is also deliberated herein. Subsequently, a coordination vector field derived from the guiding vector field is presented, incorporating an in-depth analysis concerning the impact of the vector field parameters. Lastly, the practical implementation of this novel vector field is demonstrated by its applications to 2-D and 3-D cooperative moving path following issues, establishing its efficacy.Comment: 12 pages, 12 figures, submitting to TAC at presen

Robust consensus control of uncertain multi-agent systems with input delay: a model reduction method

This paper addresses the robust consensus control design for input-delayed multi-agent systems subject to parametric uncertainties. To deal with the input delay, the Artstein model reduction method is employed by a state transformation. The input-dependent integral term that remains in the transformed system, due to the model uncertainties, is judiciously analysed. By carefully exploring certain features of the Laplacian matrix, sufficient conditions for the global consensus under directed communication topology are identified using Lyapunov-Krasovskii functionals in the time domain. The proposed control only relies on relative state information of the subsystems via network connections. The effectiveness and robustness of the proposed control design is demonstrated through a numerical simulation example

Consensus disturbance rejection for Lipschitz nonlinear multi-agent systems with input delay: a DOBC approach

In this paper, a new predictor-based consensus disturbance rejection method is proposed for high-order multi agent systems with Lipschitz nonlinearity and input delay. First, a distributed disturbance observer for consensus control is developed for each agent to estimate the disturbance under the delay constraint. Based on the conventional predictor feedback approach, a non-ideal predictor based control scheme is constructed for each agent by utilizing the estimate of the disturbance and the prediction of the relative state information. Then, rigorous analysis is carried out to ensure that the extra terms associated with disturbances and nonlinear functions are properly considered. Sufficient conditions for the consensus of the multi-agent systems with disturbance rejection are derived based on the analysis in the framework of Lyapunov-Krasovskii functionals. A simulation example is included to demonstrate the performance of the proposed control scheme. (C) 2016 The Franklin Institute. Published by Elsevier Ltd. All rights reserved.National Natural Science Foundation of China [61673034]SCI(E)ARTICLE1,SI298-31535

Control Lyapunov-barrier function based stochastic model predictive control for COVID-19 pandemic

In this paper, a stochastic model predictive control (MPC) is proposed to design a non-pharmacutical policy to control and prevent the COVID-19 pandemic. The system dynamics of COVID-19 is described by a stochastic SEIHR model subject to practical constraints, and the model is proved to be feedback linearizable. A stochastic Control Lyapunov-Barrier Function (CLBF) is constructed for the feedback linearizable system. Constraints on hospitalized individuals are regarded as the unsafe region to construct the corresponding stochastic CLBF. In the proposed stochastic MPC, the stochastic CLBF constraints are applied to improve the overall performance on controlling and preventing the epidemic. Both theoretical proof and simulation results imply that, with the CLBF-based stochastic MPC, the proposed policy is effective in controlling and preventing COVID-19 pandemic.The National Natural Science Foundation of China.https://www.sciencedirect.com/journal/ifac-papersonlineam2024Electrical, Electronic and Computer EngineeringSDG-09: Industry, innovation and infrastructur

Nonlinear robust control of tail-sitter aircrafts in flight mode transitions

© 2018 Elsevier Masson SAS In this paper, a nonlinear robust controller is proposed to deal with the flight mode transition control problem of tail-sitter aircrafts. During the mode transitions, the control problem is challenging due to the high nonlinearities and strong couplings. The tail-sitter aircraft model can be considered as a nominal part with uncertainties including nonlinear terms, parametric uncertainties, and external disturbances. The proposed controller consists of a nominal H∞controller and a nonlinear disturbance observer. The nominal H∞controller based on the nominal model is designed to achieve the desired trajectory tracking performance. The uncertainties are regarded as equivalent disturbances to restrain their influences by the nonlinear disturbance observer. Theoretical analysis and simulation results are given to show advantages of the proposed control method, compared with the standard H∞control approach

Adaptive trajectory tracking control design with command filtered compensation for a quadrotor

The design of a flight controller capable of not only stabilizing attitude but also tracking a trajectory accurately for a quadrotor aircraft in presence of parametric uncertainties and external disturbances is more challenging than that in the absence of uncertainties. In this paper we propose an adaptive trajectory tracking control algorithm, based on the relationship between attitude and linear acceleration, using online adaptive approximator to estimate unknown aerodynamic parameters and external disturbance upper bounds, and a linear tracking-differentiator to eliminate the timescale separation assumption between attitude and linear dynamics in control system design. The stability of the closed-loop control system is proven subsequently. Finally, the validity and the improvement of this proposed algorithm relative to the previous work are demonstrated through numerical simulations of tracking a circular trajectory under several conditions, including basic parametric uncertainty, exogenous wind disturbance and control input oscillation eliminating via a hyperbolic tangent function instead of a sign function. </jats:p