A Novel Multi-Agent Model-Free Adaptive Control Algorithm for a Class of Multivehicle Systems with Constraints

Wu, Lipu; Li, Zhen; Liu, Shida; Li, Zhijun; Sun, Dehui

doi:10.3390/sym15010168

Cited by 2 publications

(3 citation statements)

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“…Whether it is a quadrotor or a UAV operating with a specific task, there is a complex nonlinear partially known system, which induces challenges for control design to realize tracking control. Many scholars have paid attention to this problem and achieved productive research [4,6]. The motion of the quadrotor can be described by the rigid body model, where the Euclidean position in an inertia frame and Euler angle in a body-fixed frame are expressed by p = [p x , p y , p z ] T and v = .…”

Section: Simulation and Discussionmentioning

confidence: 99%

“…Conventional proportional integral control can hardly suppress the disturbance and dynamic drift to guarantee a robust tracking performance. To date, existing control methods to enhance the tracking performance for nonlinear systems can be roughly classified as sliding mode control [5], adaptive control [6,7], etc. The neural network and fuzzy logic system are two tools to make decisions for partially unknown systems, and their effectiveness can be verified in many areas, such as [6,[8][9][10].…”

Section: Introductionmentioning

confidence: 99%

“…To date, existing control methods to enhance the tracking performance for nonlinear systems can be roughly classified as sliding mode control [5], adaptive control [6,7], etc. The neural network and fuzzy logic system are two tools to make decisions for partially unknown systems, and their effectiveness can be verified in many areas, such as [6,[8][9][10]. There have been many studies devoted to improving the estimation performance of neural networks and fuzzy logic systems [11][12][13][14][15][16][17][18][19].…”

Section: Introductionmentioning

confidence: 99%

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Approximate Optimal Tracking Control for Partially Unknown Nonlinear Systems via an Adaptive Fixed-Time Observer

Gao

Liu

2023

Symmetry

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This paper investigates a novel adaptive fixed-time disturbance observer (AFXDO)-based approximate optimal tracking control architecture for nonlinear systems with partially unknown dynamic drift and perturbation under an adaptive dynamic programming (ADP) scheme. To attenuate the impact of disturbance, a novel AFXDO was designed based on the principle of a fixed-time stable system without prior information of disturbance, making disturbance observer errors converge to zero in a fixed time independent of initial estimation error. Additionally, approximate optimal control is conducted by incorporating the real-time estimation of AFXDO into a critic-only ADP framework to stabilize the dynamics of tracking errors and strike a balance between consumption and performance. In particular, to address the heavy calculation burden and oscillation phenomenon in the traditional actor–critic structure, an improved adaptive update law with a variable learning rate was developed to update the weight for adjusting the optimal cost function and optimal control policy simultaneously, avoiding the initial chattering phenomenon and achieving a prescribed convergence without resorting to dual networks. With the efforts of AFXDO and a weight law with a variable learning rate, the track errors were achieved with fast transient performance and low control consumptions in a fixed time. By revisiting Lyapunov stability, the tracking error and weight estimation error were proven to be uniformly ultimately bounded, and the designed control tended to optimal control. The simulations were carried out on quadrotor tracking to demonstrate the effectiveness of the developed control scheme, which achieves rapid convergence by lower control consumption in 4s, where the cost function is reduced by 19.13%.

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Section: Simulation and Discussionmentioning

confidence: 99%