This paper analyzed and calculated electromagnetic field and counterbalance force between the permanent magnet thrust plate for a novel permanent magnet axial thrust balance structure in canned motor pump using a nume...
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This paper analyzed and calculated electromagnetic field and counterbalance force between the permanent magnet thrust plate for a novel permanent magnet axial thrust balance structure in canned motor pump using a numerical method of Ansoft software. Then constructed permanent magnet axial thrust balance experiment device and detected the characteristics of repulsion and attraction which make up of axial balancing force between the axial thrust plates. The numerical simulation is very consistent with experimental results and this shows the validity and accuracy of the study. So the paper provided basically conditions for further researching, designing and developing novel permanent magnet axial thrust balance device of canned motor pomp.
Response to the demand of autonomous obstacle avoidance for mobile robot during operation, a obstacle avoidance system based on binocular stereo vision for mobile robot is developed. The system was developed with Matl...
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Response to the demand of autonomous obstacle avoidance for mobile robot during operation, a obstacle avoidance system based on binocular stereo vision for mobile robot is developed. The system was developed with Matlab and stereo vision software “the Point Grey's Triclops SDK library”. With the techniques of camera calibration, stereo image matching, three-dimensional reconstruction, the three-dimensional coordinates of obstacles was calculated, so the accurate positioning of obstacles can be obtained. The mobile robot can avoid obstacles autonomously based on binocular stereo vision. Experimental results show that the mobile robot equipped with the system can complete the tasks of obstacle avoidance and get a good effect.
In this paper, based on the recently proposed theory of deterministic learning, a new neural observer is proposed for a class of uncertain nonlinear systems undergoing periodic or recurrent motions. Firstly, it is sho...
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ISBN:
(纸本)9787894631046
In this paper, based on the recently proposed theory of deterministic learning, a new neural observer is proposed for a class of uncertain nonlinear systems undergoing periodic or recurrent motions. Firstly, it is shown that the state observation error converges to a small neighborhood of zero exponentially in finite time, and a partial persistent excitation (PE) condition is satisfied. Secondly, by imposing an auxiliary filter and constructing a new Lyapunov function, it is obtained that the neural weight estimation error also converges to a small neighborhood of zero. Thus, the system uncertain dynamics along the periodic trajectory can be locally-accurately identified by the radial basis function(RBF) neural networks(NNs) and then stored in a constant RBF NNs. Finally, a constant NNs observer is also implemented, with guaranteed stability and good performance. The proposed observer scheme does not require high-gain design, and the embedded localized NNs can learn (identify) the nonlinear uncertain dynamics along the estimated periodic or recurrent system trajectory.
Chaotic synchronization criteria for a class of dynamical networks with each node being RCL-shunted Josephson junction is proposed in this paper. The proposed algorithms, which are established in terms of linear matri...
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ISBN:
(纸本)9787894631046
Chaotic synchronization criteria for a class of dynamical networks with each node being RCL-shunted Josephson junction is proposed in this paper. The proposed algorithms, which are established in terms of linear matrix inequalities (LMIs), guarantee the synchronized states to be global asymptotically stable. In addition, an interesting conclusion is reached that the chaotic synchronization in the coupled whole 3N-dimensional networks can be converted into that of 3-dimensional space.
This paper studies the tracking control problem of mobile manipulators based on state observer. The proposed tracking controller ensures that the full state of the system can asymptotically track the given desired tra...
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A set membership identification method by pattern classification is proposed for nonlinear-in-parameter regression models with unknown but bounded (UBB) noises. Suppose that the points in the parameter space can be di...
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ISBN:
(纸本)9787894631046
A set membership identification method by pattern classification is proposed for nonlinear-in-parameter regression models with unknown but bounded (UBB) noises. Suppose that the points in the parameter space can be divided into two classes according to whether they are in the feasible solution set or not, the problem of set membership identification is to construct a pattern classifier to decide which class a point belongs to. The method has three steps. Firstly, the training data are selected uniformly in the parameter space, and are decided by equation error whether they are in the feasible solution set. Secondly, supervised Isomap (S-Isomap) is used to map the training data into low-dimensional space. Thirdly, k-nearest neighbor classifier (k-NNC) is trained on the mapped training data. This method not only can describe the feasible solution set approximately in the high-dimensional parameter space, but also can characterize it in the low-dimensional feature space. Simulation results show the effectiveness of the proposed method.
Using the known periodicity of the given trajectory, we develop a new dynamical linearization method by introducing a concept of PPD, then present a new model-free periodic adaptive control approach (MFPAC) and its ex...
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Using the known periodicity of the given trajectory, we develop a new dynamical linearization method by introducing a concept of PPD, then present a new model-free periodic adaptive control approach (MFPAC) and its extension of higher-order learning control algorithm for a class of general nonlinear and non-affine discrete-time systems. It is model-free in nature and the controller design and analysis only depends on the I/O data of the dynamical system. The proposed MFPAC updates the PPD estimate values and the control signals periodically in a pointwise manner using the I/O data obtained at the corresponding points in previous periods, in the sequel achieves an asymptotic tracking convergence. A simulation example illustrates the feasibility and effectiveness of the proposed method.
This paper presents a general form for the fast terminal sliding mode (FTSM). It is shown that the proposed form not only includes the conventional FTSM, but also provides new types of FTSM which can have a faster con...
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This paper presents a general form for the fast terminal sliding mode (FTSM). It is shown that the proposed form not only includes the conventional FTSM, but also provides new types of FTSM which can have a faster convergence rate than the conventional one. More importantly, the convergence time of the new FTSM models is bounded for any initial value, which means an upper bound of the convergence time that is determined only by parameters can be obtained. Therefore, when using the new FTSM models in the control of higher order systems, the systems can be stabilized in a certain time which can be designed and estimated out-line. Finally, the improved FTSM (IFTSM) models are applied in design of the sliding mode control for single-input single-output (SISO) nonlinear systems with uncertainty and external disturbance.
Aim at the hysteretic nonlinearity characteristic of the giant magnetostrictive actuator in control, an internal model control method based on support vector regression is presented in this paper. Its models are built...
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Aim at the hysteretic nonlinearity characteristic of the giant magnetostrictive actuator in control, an internal model control method based on support vector regression is presented in this paper. Its models are built by support vector regression using the input and output data of the actuator and the internal model control is achieved. The simulation results show the method in this paper has better control precision.
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