Nonlinear system compound inverse control method

A compound neural network is utilized to identify the dynamic nonlinear system. This network is composed of two parts： one is a linear neural network, and the other is a recurrent neural network. Based on the inverse theory a compound inverse control method is proposed. The controller has also two parts： a linear controller and a nonlinear neural network controller. The stability condition of the closed-loop neural network-based compound inverse control system is demonstrated .based on the Lyapunov theory. Simulation studies have shown that this scheme is simple and has good control accuracy and robustness.     

Nonlinear system PID-type multi-step predictive control

A compound neural network was constructed during the process of identification and multi-step prediction. Under the PID-type long-range predictive cost function, the control signal was calculated based on gradient algorithm. The nonlinear controller‘ s structure was similar to the conventional PID controller. The parameters of this controller were tuned by using a local recurrent neural network on-line. The controller has a better effect than the conventional PID controller. Sinmlation study shows the effectiveness and good performance.     

A Nonlinear Flow Control Scheme Under Capacity Constraints

We present a nonlinear flow control scheme based on a buffer management model with physical constraints. It extends previous result of Pitsillides et al. in [6] by improving the queue length regulation for better service of network traffics. Besides a single node system, we also address the decentralized control of many cascaded nodes. The proposed discontinuous controller asymptot- ically regulates the buffer queue length at the output port of a router/switch to a constant reference value, under unknown time varying interfering traffics and saturation constraints on control input and states. Its continuous approximation achieves practical regulation with an ultimate bound on the regulation error tunable by a design parameter.     

Nonlinear Dynamical Shape Priors for Level Set Segmentation

The introduction of statistical shape knowledge into level set based segmentation methods was shown to improve the segmentation of familiar structures in the presence of noise, clutter or partial occlusions. While most work has been focused on shape priors which are constant in time, it is clear that when tracking deformable shapes certain silhouettes may become more or less likely over time. In fact, the deformations of familiar objects such as the silhouettes of a walking person are often characterized by pronounced temporal correlations. In this paper, we propose a nonlinear dynamical shape prior for level set based image segmentation. Specifically, we propose to approximate the temporal evolution of the eigenmodes of the level set function by means of a mixture of autoregressive models. We detail how such shape priors “with memory” can be integrated into a variational framework for level set segmentation. As an application, we experimentally validate that the nonlinear dynamical prior drastically improves the tracking of a person walking in different directions, despite large amounts of clutter and noise.     

《Nonlinear Control Systems: an Introduction》介绍

《Nonlinear Control Systems:an Introduction》由Springer-Veclag出版公司1985年出版.作者A.Isidori教授是近二十多年来一直活跃在控制理论研究前沿领域的国际著名学者。 这部专著是控制理论专业研究生课程“非线性多变量反馈系统”的教科书,大部分内容取材于作者在美国华盛顿大学)圣路易斯市)系统科学与数学系讲授类似课程的教材。书中概括了用微分几何作为工具研究非线性多变量控制系统的一般方法和近年来的研究     

Two-Level Newton’s Method for Nonlinear Elliptic PDEs

A combination method of Newton’s method and two-level piecewise linear finite element algorithm is applied for solving second-order nonlinear elliptic partial differential equations numerically. Newton’s method is to find a finite element solution by solving $m$ Newton equations on a fine mesh. The two-level Newton’s method solves $m-1$ Newton equations on a coarse mesh and processes one Newton iteration on a fine mesh. Moreover, the optimal error estimates of Newton’s method and the two-level Newton’s method are provided to justify the efficiency of the two-level Newton’s method. If we choose $H$ such that $h=O(|\log h|^{1-2/{p}}H^2)$ for the $W^{1,p}(\Omega )$ -error estimates, the two-level Newton’s method is asymptotically as accurate as Newton’s method on the fine mesh. Meanwhile, the numerical investigations provided a sufficient support for the theoretical analysis. Finally, these investigations also proved that the proposed method is efficient for solving the nonlinear elliptic problems.     

Nonlinear inversion-based output tracking control of a boiler-turbine unit

The capability to perform fast load-following has been an important issue in the power industry. An output tracking control system of a boiler-turbine unit is developed. The system is composed of stable inversion and feedback controller. The stable inversion is implemented as a feedforward controller to improve the load-following capability, and the feedback controller is utilized to guarantee the stability and robustness of the whole system. Loop-shaping H∞ method is used to design the feedback controller and the final controller is reduced to a multivariable PI form. The output tracking control system takes account of the multivariable, nonlinear and coupling behavior of boiler-turbine system, and the simulation tests show that the control system works well and can be widely applied.     

Nonlinear Masking and Iterative Learning Decryption for Secure Communications

Typical masking techniques adopted in the conventional secure communication schemes are the additive masking and modulation by multiplication. In order to enhance security, this paper presents a nonlinear masking methodology, applicable to the conventional schemes. In the proposed cryptographic scheme, the plaintext spans over a pre-specified finite-time interval, which is modulated through parameter modulation, and masked chaotically by a nonlinear mechanism. An efficient iterative learning algorithm is exploited for decryption, and the sufficient condition for convergence is derived, by which the learning gain can be chosen. Case studies are conducted to demonstrate the effectiveness of the proposed masking method.     

Nonlinear Dimensionality Reduction and Data Visualization： A Review

Dimensionality reduction and data visualization are useful and important processes in pattern recognition.Many techniques have been developed in the recent years.The self-organizing map (SOM) can be an efficient method for this purpose.This paper reviews recent advances in this area and related approaches such as multidimensional scaling (MDS),nonlinear PCA,principal manifolds,as well as the connections of the SOM and its recent variant,the visualization induced SOM (ViSOM),with these approaches. The SOM is shown to produce a quantized,qualitative scaling and while the ViSOM a quantitative or metric scaling and approximates principal curve/surface.The SOM can also be regarded as a generalized MDS to relate two metric spaces by forming a topological mapping between them.The relationships among various recently proposed techniques such as ViSOM,Isomap,LLE,and eigenmap are discussed and compared.     

Design of Nonlinear Decoupling Controller for Double-electromagnet Suspension System

Aiming at the coupling characteristic between the two groups of electromagnets embedded in the module of the maglev train, a nonlinear decoupling controller is designed. The module is modeled as a double-electromagnet system, and based on some reasonable assumptions its nonlinear mathematical model, a MIMO coupling system, is derived. To realize the linearization and decoupling from the input to the output, the model is linearized exactly by means of feedback linearization, and an equivalent linear decoupling model is obtained. Based on the linear model, a nonlinear suspension controller is designed using state feedback. Simulations and experiments show that the controller can effectually solve the coupling problem in double-electromagnet suspension system.     

Nonlinear control techniques for an atomic force microscope system

Two nonlinear control techniques are proposed for an atomic force microscope system. Initially, a learning-based control algorithm is developed for the microcantilever-sample system that achieves asymptotic cantilever tip tracking for periodic trajectories. Specifically, the control approach utilizes a learning-based feedforward term to compensate for periodic dynamics and high-gain terms to account for non-periodic dynamics. An adaptive control algorithm is then developed to achieve asymptotic cantilever tip tracking for bounded tip trajectories despite uncertainty throughout the system parameters. Simulation results are provided to illttstrate the efficacy and performance of the control strategies.     

Observer-Based Nonlinear Control of A Torque Motor with Perturbation Estimation

This paper presents an observer-based nonlinear control method that was developed and implemented to provide accurate tracking control of a limited angle torque motor following a 50Hz reference waveform. The method is based on a robust nonlinear observer, which is used to estimate system states and perturbations and then employ input-output feedback linearization to compensate for the system nonlinearities and uncertainties. The estimation of system states and perturbations allows input-output linearization of the nonlinear system without an accurate mathematical model of nominal plant. The simulation results show that the observer-based nonlinear control method is superior in comparison with the conventional model-based state feedback linearizing controller.     

Parallel Solutions for Large-Scale General Sparse Nonlinear Systems of Equations

In solving application problems,many large-scale nonlinear systems of equaions result in sparse Jacobian matrices.Such nonlinear systems are called sparse nonlinear systems.The irregularity of the locations of nonzrero elements of a general sparse matrix makes it very difficult to generally map sparse matrix computations to multiprocessors for parallel processing in a well balanced manner.To overcome this difficulty,we define a new storage scheme for general sparse matrices in this paper,With the new storage scheme,we develop parallel algorithms to solve large-scale general sparse systems of equations by interval Newton/Generalized bisection methods which reliably find all numerical solutions within a given domain.I n Section 1,we provide an introduction to the addressed problem and the interval Newton‘s methods.In Section 2,some currently used storage schemes for sparse systems are reviewed.In Section 3,new index schemes to store general sparse matrices are reported.In Section 4,we present a parallel algorithm to evaluate a general sparse Jacobian matrix.In Section 5,we present a parallel algorithm to solve the corresponding interval linear system by the all-row preconditioned scheme.Conclusions and future work are discussed in Section 6.     

Nonlinear Decoupling PID Control Using Neural Networks and Multiple Models

For a class of complex industrial processes with strong nonlinearity, serious coupling and uncertainty, a nonlinear decoupling proportional-integral-differential （PID） controller is proposed, which consists of a traditional PID controller, a decoupling compensator and a feedforward compensator for the unmodeled dynamics. The parameters of such controller is selected based on the generalized minimum variance control law. The unmodeled dynamics is estimated and compensated by neural networks, a switching mechanism is introduced to improve tracking performance, then a nonlinear decoupling PID control algorithm is proposed. All signals in such switching system are globally bounded and the tracking error is convergent. Simulations show effectiveness of the algorithm.     

Efficient Structure Selection of Polynomial Nonlinear Systems Using Evolutionary Programming

A new method of structure selection or which terms to include into a nonlinear autoregressive moving average with eXogenous inputs (NARMAX) model, based on Evolutionary Programming (EP) has been proposed. The algorithm uses a strategy similar to Elitism where the single best chromosome in a generation is retained and passed to the next. In addition to minimizing the Mean Square Error (MSE), the method introduces an internal term penalty (ITP) function to reject spurious terms under the effects of significant noise. By following an adaptive mutation rate and restricting this to vary within 50%, faster convergence is achieved. To further improve the convergence, a pruning strategy is followed where any insignificant terms are removed from the model by assigning them with a time-to-live parameter. The performance of the proposed method is illustrated considering several examples of nonlinear systems including nonlinear wave force models and has been found to be satisfactory.     

Hydraulic actuated automotive cooling systems—Nonlinear control and test

The replacement of traditional automotive mechanical cooling system components with computer controlled servo-motor driven actuators can improve temperature tracking and reduce parasitic losses. The integration of hydraulic actuators in the engine cooling circuit offers greater power density in a smaller package space when compared with electric actuators. In this paper, a comprehensive nonlinear backstepping robust control technique is developed to regulate the engine coolant temperature by controlling a hydraulic coolant pump and radiator fan. An experimental test bench has been assembled to investigate the hydraulic automotive thermal system performance. Representative numerical and experimental results are presented and discussed. Overall, the proposed controller was successful in tracking prescribed engine temperature profiles while harmoniously regulating the power consumption of the coolant pump and radiator fan.     

Nonlinear adaptive control of master–slave system in teleoperation

This paper is devoted to the nonlinear control design problem to achieve stability of master–slave manipulators in teleoperation system and its transparency in the sense of motion/force tracking. Nonlinear adaptive controllers are bilaterally designed for both master and slave sites to guarantee the stability of whole system and motion tracking performance. Global boundedness of the overall adaptive system and asymptotic motion (velocity/position) tracking are established. Especially, the concept of “virtual master manipulator” is introduced to increase degree of freedom of control design for force tracking performance. The resulting force tracking error depends only on the acceleration of the designed virtual master manipulator. Accurate dynamic parameters of manipulators, their acceleration information as well as models of human operator and environment are not required in the control design. Another important feature of our approach is the relaxation for the trade-off between motion and force tracking performances.     

Nonlinear technology: Changing the conception of extrinsic motivation?

Think-aloud and self-report data from 84 undergraduates were used to examine the relationship between intrinsic motivation, extrinsic motivation, and use of self-regulated learning (SRL) processes. Participants individually learned about the circulatory system with a hypermedia environment for 30 min. During this experimental session, three measures were used to examine the research questions guiding the study. Participants completed a self-report questionnaire that measured their extrinsic and intrinsic motivation. They also completed a pretest and posttest, which assessed learning outcomes. Lastly, think-aloud data were collected to determine the frequency in which participants used SRL process related to planning, monitoring, and strategy use. Results indicate that participants who had high extrinsic and high intrinsic motivation used significantly more planning and monitoring processes when compared to participants who had lower motivation scores for either the extrinsic or intrinsic category. Additionally, participants who had high extrinsic and high intrinsic motivation significantly outperformed those who had low extrinsic and low intrinsic motivation.