This paper describes a novel development of a lower limber exoskeleton for physical assistance and rehabilitation. The developed exoskeleton is a motorized leg device having a total of 4 DOF with hip, knee, and ankle ...
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In coking process, the production quality, equipment life, energy consumption, and process safety are all influenced by the pressure in gas collector pipe of coke oven, which is frequently influenced by *** main contr...
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In coking process, the production quality, equipment life, energy consumption, and process safety are all influenced by the pressure in gas collector pipe of coke oven, which is frequently influenced by *** main control objectives for the gas collector pressure system are keeping the pressures in collector pipes at appropriate operating point. In this paper, model predictive control(MPC) strategy is introduced to control the collector pressure system due to its ability to handle constraint and good control performance. Based on a method proposed to simplify the system model, an extended state space model predictive control is designed,which combines the feedforward strategy to eliminate the disturbance. The simulation results in a system with two coke ovens show the feasibility and effectiveness of the control scheme.
Online fault diagnosis has been a crucial task for industrial processes. Reconstruction-based fault diagnosis has been drawing special attentions as a good alternative to the traditional contribution plot. It identifi...
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Online fault diagnosis has been a crucial task for industrial processes. Reconstruction-based fault diagnosis has been drawing special attentions as a good alternative to the traditional contribution plot. It identifies the fault cause by finding the specific fault subspace that can well eliminate alarming signals from a bunch of alternatives that have been prepared based on historical fault data. However, in practice, the abnormality may result from the joint effects of multiple faults, which thus can not be well corrected by single fault subspace archived in the historical fault library. In the present work, an aggregative reconstruction-based fault diagnosis strategy is proposed to handle the case where multiple fault causes jointly contribute to the abnormal process behaviors. First, fault subspaces are extracted based on historical fault data in two different monitoring subspaces where analysis of relative changes is taken to enclose the major fault effects that are responsible for different alarming monitoring statistics. Then, a fault subspace selection strategy is developed to analyze the combinatorial fault nature which will sort and select the informative fault subspaces that are most likely to be responsible for the concerned abnormalities. Finally, an aggregative fault subspace is calculated by combining the selected fault subspaces which represents the joint effects from multiple faults and works as the final reconstruction model for online fault diagnosis. Theoretical support is framed and the related statistical characteristics are analyzed. Its feasibility and performance are illustrated with simulated multi-faults using data from the Tennessee Eastman(TE) benchmark process.
In this paper, we study the existence, uniqueness and stability of periodic solution for a wide class of memristor-based neural networks with time-varying delays. By employing the topological degree theory in set-valu...
In this paper, we study the existence, uniqueness and stability of periodic solution for a wide class of memristor-based neural networks with time-varying delays. By employing the topological degree theory in set-valued analysis, differential inclusions theory and a new Lyapunov function method, we prove that the neural network has a unique periodic solution, which is globally exponentially stable. Moreover, we prove the existence, uniqueness and global exponential stability of equilibrium point for time-varying delayed memristor-based neural networks with constant coefficients. The obtained results improve and extend previous works on memristor-based or usual neural network dynamical systems with continuous or discontinuous right-hand side. Finally, two numerical examples are provided to show the applicability and effectiveness of our main results.
In this paper, we study the coordinated obstacle avoidance algorithm of multi-agent systems when only a subset of agents has obstacle dynamic information, or every agent has local interaction. Each agent can get parti...
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In this paper, we study the coordinated obstacle avoidance algorithm of multi-agent systems when only a subset of agents has obstacle dynamic information, or every agent has local interaction. Each agent can get partial measuring states information from its neighboring agent and obstacle. Coordinated obstacle avoidance here represents not only the agents moving without collision with an obstacle, but also the agents bypassing and assembling at the opposite side of the obstacle collectively, where the opposite side is defined according to the initial relative position of the agents to the obstacle. We focus on the collective obstacle avoidance algorithms for both agents with first-order kinematics and agents with second-order dynamics. In the situation where only a fixed fraction of agents can sense obstacle information for agents with first-order kinematics, we propose a collective obstacle avoidance algorithm without velocity measurements. And then we extend the algorithm to the case in switched topology. We show that all agents can bypass an obstacle and converge together, and then assemble at the opposite side of the obstacle in finite time, if the agents׳ topology graph is connected and at least one agent can sense the obstacle. In the case where obstacle information is available to only a fixed fraction of agents with second-order kinematics, we propose two collective obstacle avoidance algorithms without measuring acceleration when the obstacle has varying velocity and constant velocity. The switched topology is considered and extended next. We show that agents can bypass the obstacle with their positions and velocities approaching consensus in finite time if the connectivity of switched topology is continuously maintained. Several simulation examples demonstrate the proposed algorithms.
This paper concentrates on the optimal scheduling of electric vehicle(EV). The EV is scheduled for both operating stage and non-operating stage. With full consideration of operating income, regulation revenue and the ...
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This paper concentrates on the optimal scheduling of electric vehicle(EV). The EV is scheduled for both operating stage and non-operating stage. With full consideration of operating income, regulation revenue and the owner's habit, the cost minimization problem is formulated as a convex programming with coupling constrains. Dual decomposition is utilized to obtain the global optimal solution. Then, a modified online approach is introduced to alleviate the impact of price prediction error. The simulation reveals that our algorithms can reduce the cost to a large extent. Furthermore, it is demonstrated that the online scheduling scheme can achieve a similar performance compared with the optimal scheduling scheme having full knowledge of real time prices(RTP).
In this work, inspired from this biological motivation that in living cells, the execution time of different biological processes is difficult to know precisely and very sensitive to environmental factors that might b...
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作者:
Liang, HailiSu, HoushengWang, XiaofanChen, Michael Z.Q.[a] Department of Automation
Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai 200240 China[b] School of Automation Key Laboratory of Ministry of Education for Image Processing and Intelligent Control Huazhong University of Science and Technology Luoyu Road 1037 Wuhan 430074 China[c] Department of Mechanical Engineering The University of Hong Kong Hong Kong
This paper investigates the problem of swarm aggregations of heterogeneous multi-agent systems. Comparing with the existing studies on swarm aggregations of homogeneous multi-agent systems, this paper is much more res...
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This paper investigates the problem of swarm aggregations of heterogeneous multi-agent systems. Comparing with the existing studies on swarm aggregations of homogeneous multi-agent systems, this paper is much more resembling the practical situations, where the agents have different dynamics. We show that the heterogeneous agents will gather with a certain error under some assumptions and conditions. The stability properties have been proven by theoretical analysis and verified via numerical simulation. The stability of the heterogeneous multi-agent systems has been achieved based on matrix theory and the Lyapunov stability theorem. Numerical simulation is given to demonstrate the effectiveness of the theoretical result. [PUBLICATION ABSTRACT]
This paper proposes two hybrid prediction models using for predicting the displacement of landslide, Genetic Algorithm-Radial Basis Function Neural Network (GA-RBFN) and Genetic Algorithm- Back Propagation Neural Netw...
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This paper proposes two hybrid prediction models using for predicting the displacement of landslide, Genetic Algorithm-Radial Basis Function Neural Network (GA-RBFN) and Genetic Algorithm- Back Propagation Neural Network (GA-BPNN). A case study of Yuhuangge landslide in the Three Gorges reservoir in China is used to illustrate the capability and merit of our schemes. In addition, the result shows that GP-BPNN get better accuracy than GA-RBFN in the same measurements.
This paper is considered with the H ∞ observer design problem for a class of nonlinear systems with the one-sided Lipschitz condition. The systems under consideration include the well-studied Lipschitz system as a sp...
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This paper is considered with the H ∞ observer design problem for a class of nonlinear systems with the one-sided Lipschitz condition. The systems under consideration include the well-studied Lipschitz system as a special case and possess inherent advantages with respect to conservativeness. For such systems in the presence of noises, we develop a Linear Matrix Inequality (LMI) based approach to design a nonlinear H ∞ observer by carefully dealing with the one-sided Lipschitz condition together with the quadratic inner-bounded condition. The resulting nonlinear H ∞ observer guarantees asymptotic stability of the estimation error dynamics with a prescribed H ∞ performance. Moreover, for the design purpose, the existence condition of the proposed nonlinear H ∞ observer is formulated in terms of LMIs by using a matrix generalized inverse technique. Finally, a simulation example is given to illustrate the effectiveness of the proposed design.
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