An adaptive filter for speckle suppression in synthetic aperture radar images

Synthetic aperture radar images are generally corrupted by speckle noise. This arises due to the coherent nature of radar echoes used in the image formation and it is often necessary to enhance the image by speckle suppression before data can be used in various applications. To suppress speckle and improve the radar image interpretability a simple filtering technique has been proposed. The filter is adaptive to the variance of pixel intensity in a sliding window and accordingly decides the number of nearest neighbours to the central pixel to replace its intensity with the average intensity of those nearest neighbours. The performance of the filter has been studied for speckle removal in the homogeneous areas and its edge retention capability and compared with some of the widely known speckle filters. The results show that the proposed filter retains edges, removes speckle noise and compares well with other known filters in the literature.     

An adaptive control algorithm for linear systems

A modified gradient procedure is presented for adjusting parameters in a linear control system in the absence of complete knowledge of the plant dynamic characteristics. The algorithm operates to make discrete-time changes in the adjustable parameters during the normal course of system operation and incorporates the best available information on the unknown quantities. Sufficient conditions for the error corrective properties of the algorithm are derived, and the results of a simulation study are discussed.     

An all-coefficient adaptive control method for a class of nonlinear time-varying systems

In this paper, the extension of the all-coefficient adaptive control method to nonlinear time-varying systems is studied. A novel discretizing method is first proposed to derive the discrete-time model for a class of nonlinear time-varying systems. The characteristics of the coefficients of the discrete-time model are derived by this method, based on which the all-coefficient adaptive control method is given for the class of nonlinear time-varying systems. Sufficient conditions on the closed-loop stability ...     

An integrated framework for card-based production control systems

Although card-based control systems, such as Kanban and CONWIP, for production processes have been successfully employed, the design discipline does not seem to be clear yet. Therefore, the superiority of one control over the other is controversial. This paper proposes a novel design discipline for card-based control of production processes, by developing the theory of token transaction systems. The theory shows how the three indices represented in Little??s law are decided by the structure of a production process with control cards and deployment of work-in-process (WIP). That is, the relation of WIP, cycle time and throughput on specific sub-network of a production process is clarified. We show how Little??s law should be used in the design of card-based production control systems. As an application of the theory, we resolve complicated result of comparison between Kanban and CONWIP. In doing so, this theory does not restrict the target of analysis to serial production lines, but any shaped processes can be analyzed.     

An adaptive homogeneous domination method to time-varying control of nonlinear systems

The adaptive control issue of uncertain nonlinear system which has complicated polynomial growing condition is studied. Utilizing the dynamic-gain transformation, we transform the considered system into the time-varying system. By using a recursively design for its nominal system, a controller is skillfully constructed first. Subsequently, for the original system, by flexibly utilizing the dynamic gain and presenting an adaptive homogeneous domination method, a new time-varying adaptive controller is successfully obtained to ensure that the equilibrium point is globally asymptotically stable. An extended robust adaptive controller is also provided. Finally, we discuss two examples to verify the proposed approach.     

A robust adaptive control method for Wiener nonlinear systems

This paper proposes a new robust adaptive control method for Wiener nonlinear systems with uncertain parameters. The considered Wiener systems are different from the previous ones in the sense that we consider nonlinear block approximation error, process noise, and measurement noise. The parameterization model is obtained based on the inverse of the nonlinear function block. The adaptive control method is derived from a modified criterion function that can overcome non‐minimum phase property of the linear subsystem. The parameter adaptation is performed by using a robust recursive least squares algorithm with a deadzone weighted factor. The control law compensates the model error by incorporating the unmodeled dynamics estimation. Theoretical analysis indicates that the closed‐loop system stability can be guaranteed under mild conditions. Numerical examples including an industrial problem are studied to validate the results. Copyright © 2016 John Wiley & Sons, Ltd.     

An intelligent control and decision approach for adaptive systems

An intelligent control and decision-making (ICD) approach that integrates expert systems technology with adaptive algorithms is presented. The controller parameters of adaptive systems can be determined using human expertise and knowledge, and they can also be adjusted based on active monitoring and identification. Decision-making, fine tuning and inexact reasoning provide the end-user and the control engineers with a natural and integrated methodology for use with intelligent control systems. Computer simulation results demonstrate the utility of the proposed technique that is an effective intelligent control and decision approach. The ICD system is implemented using a Lisp based expert system shell on an IBM PC.     

An approach to model-referenced adaptive control systems

This paper considers the model-referenced adaptive control problem. An adaption technique that is extremely simple to implement is derived analytically. The simplicity of this technique gives it a distinct advantage over other techniques that have been described in the literature and makes it attractive for practical applications. A direct approach to the problem is taken, employing the state-space point of view. By solving the differential equations of the reference model and the adaptive control system, an expression is obtained showing the explicit functional dependence of the performance error on the adaptive parameters. Manipulation of the expression for performance error yields the adaption equations which are subsequently shown to be very simple to implement. To illustrate the theory developed in this paper, a simple example is discussed and a stability analysis employing Lyapunov's second method is undertaken.     

Expert system-user integrated adaptive systems

Many expert systems operate in dynamic environments where various pertinent environmental variables and conditions vary with the passage of time. These environmental variables and conditions may affect both the set of conditions applied to input variables of expert systems and the set of recommendations provided by expert systems. For this reason, expert systems developed according to dynamic structure will generate timely recommendations. To incorporate dynamic characteristics into the structure of expert systems, it is necessary to develop expert systems as adaptive systems. This paper intends to integrate concepts of learning and adaptiveness into expert system technology.

Expert systems used to assist loan officers in improving the decision-making process of commercial loans are typical examples of expert systems that operate in dynamic environments. This paper illustrates that the quality of information provided to loan officers by expert systems may be improved when expert systems are designed as adaptive expert systems.     

An adaptive neuro-fuzzy tracking control for multi-input nonlinear dynamic systems

An adaptive neuro-fuzzy control design is suggested in this paper, for tracking of nonlinear affine in the control dynamic systems with unknown nonlinearities. The plant is described by a Takagi–Sugeno (T–S) fuzzy model, where the local submodels are realized through nonlinear dynamical input–output mappings. Our approach relies upon the effective approximation of certain terms that involve the derivative of the Lyapunov function and the unknown system nonlinearities. The above task is achieved locally, using linear in the weights neural networks. A novel resetting scheme is proposed that assures validity of the control input. Stability analysis provides the control law and the adaptation rules for the network weights, assuring uniform ultimate boundedness of the tracking and the signals appearing in the closed-loop configuration. Illustrative simulations highlight the approach.     

An ISS-modular approach for adaptive neural control of pure-feedback systems

Controlling non-affine non-linear systems is a challenging problem in control theory. In this paper, we consider adaptive neural control of a completely non-affine pure-feedback system using radial basis function (RBF) neural networks (NN). An ISS-modular approach is presented by combining adaptive neural design with the backstepping method, input-to-state stability (ISS) analysis and the small-gain theorem. The difficulty in controlling the non-affine pure-feedback system is overcome by achieving the so-called “ISS-modularity” of the controller-estimator. Specifically, a neural controller is designed to achieve ISS for the state error subsystem with respect to the neural weight estimation errors, and a neural weight estimator is designed to achieve ISS for the weight estimation subsystem with respect to the system state errors. The stability of the entire closed-loop system is guaranteed by the small-gain theorem. The ISS-modular approach provides an effective way for controlling non-affine non-linear systems. Simulation studies are included to demonstrate the effectiveness of the proposed approach.     

An integrated state space partition and optimal control method of multi-model for nonlinear systems based on hybrid systems

Multilinear model approach turns out to be an ideal candidate for dealing with nonlinear systems control problem. However, how to identify the optimal active state subspace of each linear subsystem is an open problem due to that the closed-loop performance of nonlinear systems interacts with these subspaces ranges. In this paper, a new systematic method of integrated state space partition and optimal control of multi-model for nonlinear systems based on hybrid systems is initially proposed, which can deal with the state space partition and associated optimal control simultaneously and guarantee an overall performance of nonlinear systems consequently. The proposed method is based on the framework of hybrid systems which synthesizes the multilinear model, produced by nonlinear systems, in a unified criterion and poses a two-level structure. At the upper level, the active state subspace of each linear subsystem is determined under the optimal control index of a hybrid system over infinite horizon, which is executed off-line. At the low level, the optimal control is implemented online via solving the optimal control of hybrid system over finite horizon. The finite horizon optimal control problem is numerically computed by simultaneous method for speeding up computation. Meanwhile, the model mismatch produced by simultaneous method is avoided by using the strategy of receding-horizon. Simulations on CSTR (Continuous Stirred Tank Reactor) confirm that a superior performance can be obtained by using the presented method.     

An adaptive disturbance rejection control scheme for multivariable nonlinear systems

An adaptive disturbance rejection control scheme is developed for uncertain multi-input multi-output nonlinear systems in the presence of unmatched input disturbances. The nominal output rejection scheme is first developed, for which the relative degree characterisation of the control and disturbance system models from multivariable nonlinear systems is specified as a key design condition for this disturbance output rejection design. The adaptive disturbance rejection control design is then completed by deriving an error model in terms of parameter errors and tracking error, and constructing adaptive parameter-updated laws and adaptive parameter projection algorithms. All closed-loop signals are guaranteed to be bounded and the plant output tracks a given reference output asymptotically despite the uncertainties of system and disturbance parameters. The developed adaptive disturbance rejection scheme is applied to turbulence compensation for aircraft fight control. Simulation results from a benchmark aircraft model verify the desired system performance.     

An adaptive control scheme for systems with unknown actuator failures

A state feedback output tracking adaptive control scheme is developed for plants with actuator failures characterized by the failure pattern that some inputs are stuck at some unknown fixed values at unknown time instants. New controller parametrization and adaptive law are developed under some relaxed system conditions. All closed-loop signals are bounded and the plant output tracks a given reference output asymptotically, despite the uncertainties in actuator failures and plant parameters. Simulation results verify the desired adaptive control system performance in the presence of actuator failures.     

An improved stable adaptive fuzzy control method

Stable adaptive fuzzy control is a self-tuning concept for fuzzy controllers that uses a Lyapunov-based learning algorithm, thus guaranteeing stability of the system plant-controller-learning algorithm and convergence of the plant output to a given reference signal. In the paper, two new methods for stable adaptive fuzzy control are presented. The first method is an extension of an existing concept: it is shown that a major drawback of that concept, the necessity for new adaptation at every change of the reference signal, can be avoided by a simple modification. The main focus of the paper is on the presentation of a second method, which extends the applicability of stable adaptive fuzzy control to a broader class of nonlinear plants; this is achieved by an improved controller structure adopted from the neural network domain. Performance and limitations of the proposed methods, as well as some practical design aspects, are discussed and illustrated with simulation results     

An efficient adaptive trust-region method for systems of nonlinear equations

We present an adaptive trust-region algorithm to solve systems of nonlinear equations. Using the nonmonotone technique of Grippo, Lampariello and Lucidi, we introduce a new adaptive radius to decrease the total number of iterations and function evaluations. In contrast with the pervious methods, the new adaptive radius ensures that the size of radius is not too large or too small. We show that the sequence generated by the proposed adaptive radius is decreasing, so it prevents the production of too large radius as possible. Furthermore, it is shown that this sequence is reduced slowly, so it prevents the production of the intensely small radius. The global and quadratic convergence of the proposed approach are proved. Preliminary numerical results of our algorithm are also reported which indicate the promising behaviour of the new procedure to solve systems of nonlinear equations.     

An algorithm of the method of sections and program tools for constructing reachable sets of nonlinear control systems

The method of sections for constructing reachable sets of nonlinear control systems with constraints on the control and system state is considered. The proposed algorithms are based on the application of complexes of programs whose functional content contains methods for calculating an optimal control for various constraints on control and system coordinates. The problems of many-processor computing technology are discussed and the results of numerical experiments are presented.     

An adaptive order-statistics filter for SAR images

An algorithm based on local order statistics is proposed for adaptive reduction of speckle noise in synthetic aperture radar (SAR) images. A selective smoothing is obtained by replacing a pixel value belonging to either of the tails of the local histogram by its percentile, whose area is adaptively defined by a Gaussian function of the Local Variation Coefficient. The filter can fit the actual noise level and preserves structures, textures, and point targets, as well as the local mean without introducing any blur on the edges, mostly due to its closure property. Comparisons with algorithms suitable for speckle smoothing are performed on true SAR images and show selective signal-to-noise ratio (SNR) enhancements.     

一类含不匹配干扰的非线性系统非递归鲁棒/自适应一体化控制律设计

针对一类含不匹配干扰的非线性系统的控制问题, 基于递归化方案得到鲁棒或自适应控制律是常见的设计思路, 如反步法及其衍生控制策略等. 然而, 递归设计的控制律通常由含多偏微分项的多个虚拟控制器组成, 形式复杂的同时, 控制参数选取也较为困难, 易出现“复杂性爆炸”的问题, 因此较难得到广泛的工程应用. 同时, 因递归设计处理系统的非线性与不确定性的差异较大, 难以实现鲁棒/自适应控制的本质性融合. 本文从一个新颖的非递归控制角度出发, 提出了一个能够融合鲁棒/自适应控制策略的设计框架, 实现系统在不匹配受扰情形下的无静差跟踪. 仅通过一步坐标变换, 在等价的可镇定系统框架下, 根据实际工况来灵活切换合适的控制增益, 为工程师同时提供了两个可供选择的控制方案. 相较于已有算法, 本文所提控制器形式简洁易实现, 参数易调节, 适用范围广. 案例分析与实例仿真验证阐明了所提方法的简洁性及有效性, 并给出了一体化控制器工作模式的选取原则.     

An efficient authentication method for AMBTC compressed images using adaptive pixel pair matching

The existing image authentication methods for absolute moment block truncation coding (AMBTC) modify the bits of quantitation levels or bitmaps to embed the authentication code (AC). However, the modification of the bits in these methods is equivalent to the LSB replacement, which may introduce undesirable distortions. Besides, the modification of bitmap for embedding AC reduces the image quality significantly, especially at image edges. Moreover, the existing methods might not be able to detect some special modifications to the marked image. In this paper, we propose an efficient authentication method for the AMBTC compressed image. AC is obtained from the bitmap and the location information, and is embedded into the quantization levels using the adaptive pixel pair matching (APPM) technique. Since the bitmap is unchanged and the APPM embedment is efficient, a high image quality can be achieved. The experimental results reveal that the proposed method not only significantly reduces the distortion caused by embedding but also provides a better authentication result when compared to the prior state-of-art works.