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In this paper, we study the finite-time consensus problem of networked nonlinear systems under directed fixed graph. A nonlinear system is considered as a controlled first-order differential equation with/without drift term commonly used to model autonomous systems. For multi-system formation under directed fixed graph, a protocol is proposed to solve consensus problems in finite time. Guided by finite-time...
In this paper, an adaptive output feedback tracking control scheme is proposed for spacecraft formation flying (SFF) in the presence of external disturbances, uncertain system parameters, input constraints and partial loss of control effectiveness. The proposed controller incorporates a pseudo-velocity filter to account for the unmeasured relative velocity, and the neural network (NN) technique is...
Classical schemes in system identification and adaptive control often rely on persistence of excitation to guarantee parameter convergence, which may be difficult to achieve with a single agent and a single input. Inspired by consensus systems, we extend classical parameter adaptation to the multi agent setting by combining an adaptive gradient law with consensus dynamics. The gradient law represents...
In this paper, we consider a decentralized wireless communication system with wireless energy transfer capability. We aim to minimize the total number of packets waiting at wireless nodes for the whole system. We first formulated the optimization problem as a decentralized partially observable Markov decision process (DEC-POMDP). To solve an optimization problem with constraints, we applied the Lagrangian...
In this paper, a new discrete time identification scheme for a singularly perturbed nonlinear system using recurrent high order multi-time scale neural network is presented. The high-order neural network (HONN) is known for its simple structure and powerful nonlinearity approximation property, which make it more suitable for modeling the singularly perturbed nonlinear systems than the multi-layer...
The optimal event-triggered control of nonlinear continuous-time systems by using input and output data is a challenging problem due to system uncertainties, non-availability of state vector and event-based sampled outputs between the plant and the controller. Therefore, a novel reinforcement learning-based approach is proposed to solve time-based near optimal event-triggered control of nonlinear...
In this paper, an adaptive neural network (NN) state-feedback controller for a class of nonlinear systems with mismatched uncertainties is presented. By using a radial basis (RBF) neural network, a bound of unknown nonlinear functions is approximated so that no information about the upper bound of mismatched uncertainties is required. The state-feedback is based on Lyapunov stability theory, and it...
In this paper, an actor critic neural network control is developed for a robotic manipulator. Both system uncertainties and unknown deadzone are considered in the tracking control design. Stability of the closed-loop system is analyzed via the Lyapunov's direct method. The critic neural network is used to estimate the cost-to-go and the actor neural network is used to make the cost-to-go converge...
This paper presents a distributed leader-following control approach for a group of uncertain Euler-Lagrange systems in the absence of the neighbors' velocity information under an undirected communication graph. We study the dynamic leader case. The Euler-Lagrange systems' unknown dynamics and external disturbances are compensated by the function approximation technique of neural networks (NN). Combining...
In this paper, an adaptive fault-tolerant attitude control problem is presented of rigid body using radial basis function neural network (RBF NN). The faults we considered are that the thrusters of the rigid might partially or totally lose power. The uncertainty of the system produced by the external disturbances, unknown inertia matrix and thrusters failures are approximated by RBF NN. It is proved...
The strategy profile dynamics of (networked) evolutionary games ((N)EGs) are investigated. The algebraic state space expression of strategy profile dynamics using semi-tensor product is presented. Based on this framework, the Lyapunov function for (N)EGs is proposed and is used to investigate the convergence of the strategy profile dynamics. The relationship between Lyapunov function and the potential...
The search method based on the way combined self-organizing competitive neural network with nearest neighbor algorithm was proposed. A data preprocessing method applying to the retrieval method was proposed in this paper. The implementation process of the search method based on the way combined self-organizing competitive neural network with nearest neighbor was showed. Eventually, through a practical...
This paper considers the finite-time control design for the nonholonomic mobile robots to construct leader-following formation. In the formation, only the pose of the leader robot can be obtained by the follower. Firstly, the leader-following formation is transformed into a special trajectory tracking problem and a finite-time observer is designed to estimate the leader's dynamics. Then, neural network-based...
Fish sauce is one of the signature condiments in various cuisines in many countries. In this study, fish sauces are successfully discriminated depending on their quality indicated by the level of Total Nitrogen (TN) content. We introduce an electronic nose (E-nose) technology to measure the odor of fish sauce. Feature extraction methods are also performed in order to obtain information relevant to...
This paper deals with the problem of designing an observer-based adaptive tracking controller for a class of uncertain nonlinear systems. A neural network-based observer estimates states of the system and a neural network-based controller is designed to approximate input control signal. The estimated states by the observer are inputs of the controller and two neural networks (NNs) interact together...
The paper investigates the balance between Quality of Service (QoS) and power consumption for a traffic queue. Automatic adaptation to the power of the packet processing engine is sought through a heuristics and an optimal control strategy. The study allows to track the behavior of the system over time, thus avoiding the optimization of the steady state behavior of the system, which is hardly applicable...
Direct Torque Control (DTC) is known to produce quick and robust response in AC drives. However, during steady state, torque, flux and current ripple occur. An improvement of the electric drive can be obtained using a DTC scheme based on the Space Vector Modulation (SVM) which reduces the torque and flux ripple. The proposed control scheme considers the rotor resistance variation. This paper also...
Nearest Neighbour Search in high-dimensional spaces is a common problem in Computer Vision. Although no algorithm better than linear search is known, approximate algorithms are commonly used to tackle this problem. The drawback of using such algorithms is that their performance depends highly on parameter tuning. While this process can be automated using standard empirical optimization techniques,...
In this paper, we present an overview of existing intrusion detection techniques for DDoS attacks. All these algorithms are described more or less on their own. Intrusion detection system is a very popular and computationally expensive task. We also explain the fundamentals of intrusion detection system. We describe today's approaches for intrusion detection system. From the broad variety of efficient...
This paper provides interesting findings for modeling of a challenging and critical pedagogical issue namely online learning assessment of Multiple Choice Questions (MCQs) analysis and evaluation. More precisely, in fulfillment of that issue's objective, this work suggests using a realistic Artificial Neural Network (ANN) model. That, explicitly, characterized by two learning paradigms: supervised...
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