In practical research, nonlinear equation systems (NESs) are common mathematical models widely applied across various fields. Solving these nonlinear equation systems is crucial for addressing many engineering challen...
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ISBN:
(纸本)9789819771837;9789819771844
In practical research, nonlinear equation systems (NESs) are common mathematical models widely applied across various fields. Solving these nonlinear equation systems is crucial for addressing many engineering challenges. However, due to the inherent complexity and diverse solutions of nonlinear equation systems, traditional optimization algorithms and intelligent optimization algorithms have certain limitations. Neural network algorithms, which have gained significant popularity in recent years, excel in fitting nonlinear relationships. This research aims to explore different neural network models to develop efficient and accurate computational models for solving various types of nonlinear equation systems, thus overcoming some of the limitations of traditional and intelligent optimization algorithms. By leveraging the adaptability and generality of neural networks, we seek to enhance their performance in solving complex nonlinear equation systems. Furthermore, by integrating iterative algorithms and clustering algorithms, we aim to improve solution accuracy and effectively address the multiple roots problem associated with nonlinear equation systems.
This paper addresses the H∞ control problem for a networked Markovian jump system subject to hybrid attacks that occurred in both the sensor-controller (S-C) network and the controller-actuator (C-A) network. Conside...
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This paper introduces a novel deterministic learning (DL)based knowledge fusion neural control strategy tailored for unknown robot manipulators with predefined performance. For two different control training scenarios...
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ISBN:
(纸本)9789819607822;9789819607839
This paper introduces a novel deterministic learning (DL)based knowledge fusion neural control strategy tailored for unknown robot manipulators with predefined performance. For two different control training scenarios, online fusion and offline fusion control schemes are proposed respectively. In the online knowledge fusion scheme, a collaborative control methodology is embraced, integrating a mechanism for propagating weight update information into the neural network (NN) learning algorithm of DL. This integration facilitates the eventual convergence of system weights across all operational systems toward a shared optimal value. For the offline fusion control scheme, it transforms the fusion problem of multi-trajectory closed-loop dynamics knowledge learned by deterministic learning (DL) into the least squares solution problem of a system of linear equations. Moreover, leveraging the fused dynamic knowledge acquired through the aforementioned approaches, we construct a neural network (NN) learning controller based on integrated knowledge to realize a multi-task intelligent control for robotic manipulators in intricate scenarios. The simulation section provides empirical evidence of the efficacy of the proposed approach.
This paper focuses on the implementation of trajectory tracking control for a hex-rotor unmanned aerial vehicle. In order to design a high performance robust controller without loss of model information, the character...
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Traditionally data-plane measurements have been used to understand application performance and to detect specific impairments with high confidence. control plane effects on data-plane performance were often incidental...
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Model disturbances result from model uncertainties or external factors acting on the system. They usually affect the closed-loop performance in a control loop system. However, they are often unknown and cannot be then...
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ISBN:
(纸本)9798350328066
Model disturbances result from model uncertainties or external factors acting on the system. They usually affect the closed-loop performance in a control loop system. However, they are often unknown and cannot be then compensated. Therefore, it is crucial to develop estimation methods for the effective estimation of the disturbances which can be then considered appropriately in the control design. This paper proposes a hybrid method for the joint estimation of the state and the disturbance for a class of nonlinear systems in two steps. The approach consists in a neural network with time-varying weights used to approximate the disturbance term and a modulating function method for the finite-time estimation of the state and the weights. The modulating functions approach simplifies the estimation problem into solving an algebraic systems of equations. Both offline and online frameworks are presented and discussed. An example is presented to demonstrate the performance of the proposed algorithm.
This paper considers the finite-time H-infinity filtering problems for stochastic nonlinear systems with mode-dependent discrete-time switching under a dynamic event-triggered scheme. A finite-time H-infinity filter i...
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ISBN:
(纸本)9798350366907;9789887581581
This paper considers the finite-time H-infinity filtering problems for stochastic nonlinear systems with mode-dependent discrete-time switching under a dynamic event-triggered scheme. A finite-time H-infinity filter is proposed to generate the residual signal system and a dynamic event-triggered(DET) mechanism is utilized to monitor the signal transmission, which increases the data transmission efficiency of limited network bandwidth. By applying a novel Lyapunov-like function and mode-dependent average dwell time approach, sufficient conditions to guarantee that a DET error system is stochastic finite-time stable with the desired H-infinity performance are derived. The corresponding filtering parameters are calculated by employing the linear matrix inequality technique.
This paper investigates a novel dual-loop sliding-mode control (SMC) scheme to achieve trajectory tracking for a class of nonlinear systems with unknown uncertainties. The inner loop is control loop, which consists of...
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ISBN:
(纸本)9798350382662;9798350382655
This paper investigates a novel dual-loop sliding-mode control (SMC) scheme to achieve trajectory tracking for a class of nonlinear systems with unknown uncertainties. The inner loop is control loop, which consists of three controllers. An optimal feedback controller based on solving Hamilton-Jacobi-Bellman equation is proposed, which optimizes system performance for the nominal tracking system. A cerebellar model articulation control (CMAC) neural network is introduced for approximating the unknown uncertainties, and is embedded in the so-called CMAC-based sliding-mode controller. An adaptive compensator is used to dispel the negative effect from the approximated error of the CMAC. The outer loop is learning loop, which consists of the CMAC-based approximator and the quasi-sliding-model learning law. We formulate the learning problem of CMAC into a robust control framework of discrete-time nonlinear system. A new online learning law based on discrete-time quasi-SMC strategy is developed to ensure the global and fast convergence and eliminates the chattering. It can be shown that the explicit expressions of gain parameters of the dual-loop sliding-mode scheme are derived in terms of the established sufficient conditions. Our proposed techniques are applied to a salient permanent magnet synchronous motor with excellent performance.
The vertiport concept has spread widely as the future aerodrome that will allow Vertical Take-Off and Landing vehicles to operate in complex and congested scenarios, such as those foreseen within the Urban Air Mobilit...
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ISBN:
(纸本)9798350357899;9798350357882
The vertiport concept has spread widely as the future aerodrome that will allow Vertical Take-Off and Landing vehicles to operate in complex and congested scenarios, such as those foreseen within the Urban Air Mobility framework. Possible vertiport configurations have been proposed by many government agencies such as the European Union Aviation Safety Agency and the Federal Aviation Administration providing general design and development guidelines. This paper introduces an autonomous visual-aided navigation architecture able to estimate the aircraft state during approaches to vertiports exploiting visual observables gathered from multiple landing patterns. The implemented architecture exploits a Convolutional Neural network for landing patterns detection;it then performs their discrimination and, for each of them, keypoints detection and identification to feed a perspective-n-point solver. The resulting pose measurements are input to an Extended Kalman Filter, which also processes data from an Inertial Measurement Unit and a Global Navigation Satellite System receiver. The implemented architecture is tested on synthetic and real data, showing the validity of the pattern discrimination strategies and the performance of the visual-aided filter as a function of the number of detected patterns along different approach trajectories.
In this work, the consensus tracking control problem of leader-follower nonlinear multi-agent systems with actuator faults and unknown control directions is studied. Based on the application of neural network, the unc...
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