N-tuple algebra-based probabilistic logic

The concept of “probabilistic logic” known in artificial intelligence needs a more through substantiation. A new approach to constructing probabilistic logic based on the n-tuple algebra developed by the author is proposed. A brief introduction is given to the n-tuple algebra and its properties that provide efficient paralleling of algorithms for solving problems of logical analysis of systems in computer implementation are generalized. Methods for solving direct and inverse problems of probabilistic simulation of logical systems are considered.     

Decentralized adaptive neural control of nonlinear interconnected large-scale systems with unknown time delays and input saturation

In this paper, a novel decentralized adaptive neural control scheme is proposed for a class of interconnected large-scale uncertain nonlinear time-delay systems with input saturation. RBF neural networks (NNs) are used to tackle unknown nonlinear functions, then the decentralized adaptive NN tracking controller is constructed by combining Lyapunov–Krasovskii functions and the dynamic surface control (DSC) technique along with the minimal-learning-parameters (MLP) algorithm. The stability analysis subject to the effect of input saturation constrains are conducted with the help of an auxiliary design system based on the Lyapunov–Krasovskii method. The proposed controller guarantees uniform ultimate boundedness (UUB) of all the signals in the closed-loop large-scale system, while the tracking errors converge to a small neighborhood of the origin. An advantage of the proposed control scheme lies in that the number of adaptive parameters for each subsystem is reduced to one, and three problems of “computational explosion”, “dimension curse” and “controller singularity” are solved, respectively. Finally, a numerical simulation is presented to demonstrate the effectiveness and performance of the proposed scheme.     

Mathematical models of elastic wave processes in seismology and seismic prospecting: forward and inverse problems

This paper presents a simulation system based on the solution of forward and inverse problems of elastic wave propagation. Forward and inverse modeling have become a useful tool for interpretation in exploration geophysics and seismology. By 3D modeling, field observations can be simulated numerically, and computed results can be compared to field data. It is known that 3D seismic modeling requires the up-to-date high-performance multiprocessor computer systems which are not readily available for many geophysical firms in Russia. This situation makes us focus on the creation of efficient numerical–analytical algorithms which allow the solution of 3D forward and inverse seismic problems without supercomputers. This paper presents some algorithms and numerical experiments for different models of media. A special emphasis is given to “non-ray” waves which play an important role in seismic interpretation theory.     

A general dissipativity constraint for feedback system design,with emphasis on MPC

A “general dissipativity constraint” (GDC) is introduced to facilitate the design of stable feedback systems. A primary application is to MPC controllers when it is preferred to avoid the use of “stabilising ingredients” such as terminal constraint sets or long prediction horizons. Some very general convergence results are proved under mild conditions. The use of quadratic functions, replacing GDC by “quadratic dissipativity constraint” (QDC), is introduced to allow implementation using linear matrix inequalities. The use of QDC is illustrated for several scenarios: state feedback for a linear time‐invariant system, MPC of a linear system, MPC of an input‐affine system, and MPC with persistent disturbances. The stability that is guaranteed by GDC is weaker than Lyapunov stability, being “Lagrange stability plus convergence.” Input‐to‐state stability is obtained if the control law is continuous in the state. An example involving an open‐loop unstable helicopter illustrates the efficacy of the approach in practice.     

Non-linear anti-windup for manual flight control

In this paper we address the windup problem arising from actuator magnitude and rate saturation for the linearized longitudinal short-period dynamics of a tailless aircraft model around any trim flight condition. The proposed anti-windup scheme allows for more aggressive manoeuvres than the standard “command limiting” approach. Moreover, the compensation law guarantees stability of the controlled aircraft for any pilot command and enforces flight quality specifications whenever they are achievable within the given control constraints. “Pilot-in-the-loop” simulations confirm the effectiveness of the control strategy.     

Binary level set methods for topology and shape optimization of a two-density inhomogeneous drum

We optimize eigenvalues in optimal shape design using binary level set methods. The interfaces of subregions are represented implicitly by the discontinuities of binary level set functions taking two values 1 or ?1 at convergence. A binary constraint is added to the original model problems. We propose two variational algorithms to solve the constrained optimization problems. One is a hybrid type by coupling the Lagrange multiplier approach for the geometry constraint with the augmented Lagrangian method for the binary constraint. The other is devised using the Lagrange multiplier method for both constraints. The two iterative algorithms are both largely independent of the initial guess and can satisfy the geometry constraint very accurately during the iterations. Intensive numerical results are presented and compared with those obtained by level set methods, which demonstrate the effectiveness and robustness of our algorithms.     

Stabilization of delayed fractional-order T-S fuzzy systems with input saturations and system uncertainties

By adopting the Takagi–Sugeno (T-S) fuzzy theory, this paper focuses on the adaptive control of fractional-order time-delay systems involving unknown parameters and input saturations. T-S fuzzy systems with “IF-THEN” rules are employed to describe fractional-order nonlinear systems. The influence of input saturation is handled by designing an auxiliary system. By using a norm conversion, the system's time-delay term is converted into a non-delayed form. Adaptive updating rules are devised to evaluate uncertainties of the system, and a new control approach is proposed. The devised controller can not only guarantee the boundedness of variables involved but also make state variables converge into a sufficiently small neighborhood of the origin, even in the presence of uncertainties, saturation functions, and system parameters. Finally, numerical studies are given to verify the correctness of the designed scheme.     

A Deterministic Algorithm for Reconstructing Images with Interacting Discontinuities

The most common approach for incorporating discontinuities in visual reconstruction problems makes use of Bayesian techniques, based on Markov random field models, coupled with stochastic relaxation and simulated annealing. Despite their convergence properties and flexibility in exploiting a priori knowledge on physical and geometric features of discontinuities, stochastic relaxation algorithms often present insurmountable computational complexity. Recently, considerable attention has been given to suboptimal deterministic algorithms, which can provide solutions with much lower computational costs. These algorithms consider the discontinuities implicitly rather than explicitly and have been mostly derived when there are no interactions between two or more discontinuities in the image model. In this paper we propose an algorithm that allows for interacting discontinuities, in order to exploit the constraint that discontinuities must be connected and thin. The algorithm, called E-GNC, can be considered an extension of the graduated nonconvexity (GNC), first proposed by Blake and Zisserman for noninteracting discontinuities. When applied to the problem of image reconstruction from sparse and noisy data, the method is shown to give satisfactory results with a low number of iterations.     

具有角速度和输入约束的航天器姿态协同控制

提出了基于有向图的航天器姿态协同控制算法, 并且系统的角速度和控制输入满足有界性的约束. 当外部扰动存在时, 设计了自适应算法估计扰动的上界, 采用滤波器补偿的方法处理控制输入饱和问题, 并且设计了新的自适应姿态协同控制算法. 对于所设计的控制算法, 给出了稳定性分析, 证明了系统具有几乎全局渐近稳定性. 进一步把控制算法推广到时变通信时滞情况, 当控制器参数满足一定条件时, 仍然能够保证编队系统的几乎全局渐近稳定性. 通过数值仿真, 验证了所提出的控制方案的有效性.     

LQ优化的并行化方法

本文提出了两种有效的并行ＬＱ优化算法，分别用于求解多输入及单输入情况下控制参数合成。这两种算法都可以用脉动阵列结构并行实现，通过对数据流时序以及控制器利用率的分析，说明了并行的有效性。     

考虑输入约束的半主动悬架非线性自适应控制

针对具有输入约束及参数不确定性问题的汽车半主动悬架系统,提出一种考虑输入饱和的非线性自适应Backstepping控制器.该方法引入一个辅助系统,通过设计新的误差变量,实现对控制饱和的补偿,解决控制输入的幅值约束问题.同时,考虑到悬架系统的参数不确定性问题,采用映射自适应算法设计自适应律,通过构造适当的Lyapunov函数,保证悬架系统的稳定性.仿真结果表明,所设计的控制器具有良好的隔振性能,而且能够有效降低输入约束和不确定参数对系统性能的影响.     

Nonlinear Robust Adaptive Deterministic Control for Flexible Hypersonic Vehicles in the Presence of Input Constraint

A nonlinear deterministic robust control scheme is developed for a flexible hypersonic vehicle with input saturation. Firstly, the model analysis is conducted for the hypersonic vehicle model via the input‐output linearized technique. Secondly, the sliding mode manifold is designed based on homogeneity theory. Then an adaptive high order sliding mode control scheme is proposed to achieve tracking for the step change in altitude and velocity for hypersonic vehicles where the uncertainty boundary is unknown. Furthermore, the control input constraint is investigated and another new adaptive law is proposed to estimate the uncertainties and to guarantee the stability of the system with input saturation. Finally, the simulation results are provided to demonstrate the effectiveness of the proposed method.     

Neural network modelling and control of a plant exhibiting the Jump Phenomena

This paper focuses primarily on the modelling and control of nonlinear systems that exhibit gain discontinuities in their frequency plots. Structures and learning algorithms for neural network based nonlinear modelling are introduced and applied to the modelling and k-step ahead prediction of an example system. A nonlinear Internal Model Controller (IMC) is developed, based on the ability of the feedforward neural network to form nonlinear forward and inverse models. The results of simulation studies are given in each case.     

Fast Constructive-Covering Algorithm for neural networks and its implement in classification

Almost all current training algorithms for neural networks are based on gradient descending technique, which causes long training time. In this paper, we propose a novel fast training algorithm called Fast Constructive-Covering Algorithm (FCCA) for neural network construction based on geometrical expansion. Parameters are updated according to the geometrical location of the training samples in the input space, and each sample in the training set is learned only once. By doing this, FCCA is able to avoid iterative computing and much faster than traditional training algorithms. Given an input sequence in an arbitrary order, FCCA learns “easy” samples first and “confusing” samples are easily learned after these “easy” samples. This sample reordering process is done on the fly based on geometrical concept. In addition, FCCA begins with an empty hidden layer, and adds new hidden neurons when necessary. This constructive learning avoids blind selection of neural network structure. The experimental work for classification problems illustrates the advantages of FCCA, especially in learning speed.     

基于Popov定理的输入非线性广义预测控制系统的稳定性分析

对存在输入饱和约束和输入可逆静态非线性的系统,采用两步法广义预测控制策略. 首先用线性广义预测控制策略得到中间变量,代表期望的控制作用,然后用解方程方法补偿可逆 静态非线性并用解饱和方法满足饱和约束,得到实际的控制作用.两步法计算简单,特别适用于 快速控制的场合.将该控制系统闭环结构转化为静态非线性增益反馈结构,利用Popov定理分 析了该系统的闭环稳定性,得到了稳定的充分条件,并具体给出了有效的控制器参数确定算法使 得稳定性结论具备实用的价值.给出了算例验证了稳定条件.     

Stability and analysis of iterative learning system using frequency domain method

This paper adopts a reinforcement learning control using the frequency domain method for a SISO system with friction and input saturation process. The proposed method, which is based on two-dimensional system theory, investigates the robust stability criteria of the SISO iterative learning control system ({ILCS}) using frequency domain methods. The restrictive conditions for the stability of the {ILCS} are also derived. A design procedure for the {ILCS} is outlined. A numerical simulation example is given to demonstrate the utilization of obtained results.     

Performance divergence with data discrepancy: a review

With rapid increase in disease variety, the role of image segmentation has been crucial in image guided surgery. Despite having a lot of existing methods, the robustness of an algorithm remains a concern with respect to the input image variety. This paper presents a state of art segmentation algorithms of “MICCAI Grand Challenge and Conference 2007, 2008 and 2009”. These algorithms are reported to have tested on real datasets used in “MICCAI Grand Challenge 2007, 2008 and 2009”. Due to the page constraint, selected papers based on some criteria are included in this review. In this work, we have implemented and evaluated all these methods on a particular data. The objective of this paper is to exhibit the divergence in performance if the input data is varied.     

Adaptive Decentralized NN Control of Nonlinear Interconnected Time‐Delay Systems with Input Saturation

In this paper, a novel decentralized adaptive neural control scheme is proposed for a class of interconnected large‐scale uncertain nonlinear time‐delay systems with input saturation. Radial basis function (RBF) neural networks (NNs) are used to tackle unknown nonlinear functions. Then, the decentralized adaptive NN tracking controller is constructed by combining Lyapunov–Krasovskii functions and the dynamic surface control (DSC) technique, along with the minimal‐learning‐parameters (MLP) algorithm. The stability analysis subject to the effect of input saturation constraints are conducted with the help of an auxiliary design system based on the Lyapunov–Krasovskii method. The proposed controller guarantees uniform ultimate boundedness (UUB) of all of the signals in the closed‐loop large‐scale system, while the tracking errors converge to a small neighborhood around the origin. An advantage of the proposed control scheme lies in the number of adaptive parameters of the whole system being reduced to one and in the solution of the three problems of “computational explosion,” “dimension curse,” and “controller singularity”. Finally, simulation results along with comparisons are presented to demonstrate the advantages, effectiveness, and performance of the proposed scheme.