An adaptive neuro-fuzzy sliding mode controller for MIMO systems with disturbance

This paper introduces the mathematical model of ammonia and urea reactors and suggested three methods for designing a special purpose controller. The first proposed method is Adaptive model predictive controller, the second is Adaptive Neural Network Model Predictive Control, and the third is Adaptive neuro-fuzzy sliding mode controller. These methods are applied to a multivariable nonlinear system as an ammonia–urea reactor system. The main target of these controllers is to achieve stabilization of the outlet concentration of ammonia and urea, a stable reaction rate, an increase in the conversion of carbon monoxide(CO) into carbon dioxide(CO_2) to reduce the pollution effect, and an increase in the ammonia and urea productions, keeping the NH_3/CO_2 ratio equal to 3 to reduce the unreacted CO_2 and NH_3, and the two reactors' temperature in the suitable operating ranges due to the change in reactor parameters or external disturbance. Simulation results of the three controllers are compared. Comparative analysis proves the effectiveness of the suggested Adaptive neurofuzzy sliding mode controller than the two other controllers according to external disturbance and the change of parameters. Moreover, the suggested methods when compared with other controllers in the literature show great success in overcoming the external disturbance and the change of parameters.     

Extreme learning machine-based receiver for MIMO LED communications

This work concerns receiver design for light-emitting diode (LED) multiple input multiple output (MIMO) communications where the LED nonlinearity can severely degrade the performance of communications. We firstly propose an extreme learning machine (ELM) based receiver to jointly handle the LED nonlinearity and cross-LED interference. Then, by taking advantage of the features of the ELM, we propose to use a circulant structure for the input weight matrix and the fast Fourier transform (FFT) for implementation, leading to significant computational complexity reduction. It is demonstrated that, the proposed ELM based receivers can handle the nonlinearity and interference much more effectively compared to conventional techniques, and the low complexity ELM-based receiver with circulant input matrix delivers almost the same performance as the receiver based on the conventional ELM.     

基于T-D猜想上MAI函数的构造

对涂自然等人提出的组合猜想上的构造方法及有关结论进行了改良推广,在假设更一般的组合猜想成立的前提下构造了一种具有最优代数免疫度的偶数元布尔函数f,同时还利用f构造了一种具有最优代数免疫度的平衡的偶数元布尔函数F。且这些函数也具有很高的代数次数和非线性度,对代数攻击具有较强的抵抗能力。     

具有线性结构的弹性函数非线性度的新上界

讨论了具有线性结构的弹性函数的两个指标:沃什谱和非线性度,得到了具有线性结构的布尔函数的一些性质.利用沃尔什变换和汉明重量的方法,发现了:如果V是n元布尔函数,f(x)的线性结构,那么得到f(x)的沃尔什变换在为零这一事实,同时得到了一个布尔函数没有k(k≥0)维线性结构的充分条件.最后,利用以上结果推出了具有线性结构的弹性函数的非线性度的上界表达式.     

Pseudo-decentralized adaptive stabilization of large-scale feedforward nonlinear systems

In this paper, we propose a pseudo-decentralized adaptive control scheme for a class of large-scale feedforward nonlinear systems with unknown nonlinear effects within subsystems and unknown nonlinear interactions among subsystems. The local controller of each subsystem takes a nested saturation feedback, using the state of its own subsystem, and the saturation levels are tuned online in a switching manner via a set of switching logics, which requires some binary flag communication among subsystems. Global asymptotic regulation of the closed-loop states is achieved.     

改进的非线性跟踪微分器设计

在对典型跟踪微分器深入研究的基础上,提出一种改进的非线性跟踪微分器.该跟踪微分器综合了线性跟踪微分器和非线性跟踪微分器的优点,不但无颤振现象,而且具有良好的动态响应和较强的滤波能力,兼顾了快速性和准确性的要求,可实现任意信号的跟踪和微分.该跟踪微分器形式简单、易于实现.仿真结果表明,改进型非线性跟踪微分器具有优良的性能.     

随机多智能体系统的多率协同控制

本文针对一类带有非线性动力学的随机多智能体系统研究了基于多率采样机制的一致性控制问题。首先,引入多率采样机制异步地对智能体的状态进行采样,并且使用缓存器来同步智能体不同分量的状态信息。然后,提出了一种基于边的事件触发传输机制将每个智能体的状态信息定向传输给邻居智能体,从而减少了通信资源的使用和控制器的更新频率。接着,本文根据所设计的事件触发传输机制设计了相应的控制协议使得随机多智能体系统可以实现均方一致性。因为每条边上的事件都是独立发生的,所以智能体的邻居之间不需要保证信息的时钟同步。最后,给出了数值仿真例子。通过将同步采样机制、异步采样机制和多率采样机制进行比较,说明了所提出的边事件触发传输机制在多率采样下可以获得更好的系统性能。     

Effect of process nonlinearity on linear quadratic regulator performance

In this paper, a new local measure of nonlinearity is introduced to quantify the sensitivity of controller performance for linear controllers operating on a nonlinear plant. The measure, called the performance sensitivity measure, quantifies the departures from optimality of locally linear quadratic regulators. A root mean squared measure, the RMSPSM, is proposed to summarize average nonlinearity effects over a region of interest. The measure applies to nonlinear systems that admit a controllable and observable linearization.     

The adaptive control using BP networks for a nonlinear servo-motor

The servo-motor possesses a strongly nonlinear property due to the effect of the stimulating input voltageload-torque and environmental operating conditions.So it is rather diffcult to derive a traditional mathematical model which is capable of expressing both its dynamics and steady-state characteristics.A neural network-based adaptive control strategy is proposed in this paper.In this method,two neural networks have been adopted for system identification(NNI)and control(NNC),respectively.Then,the commonly-used specialized learning has been modified,by taking the NNI output as the approximation output of the servo-motor during the weights training to get sensitivity information.Moreover,the rule for choosing the learning rate is given on the basis of the analysis of Lyapunov stability.Finally,an example of applying the proposed control strategy on a servo-motor is presented to show its effectiveness.     

The adaptive control using BP neural networks for a nonlinear servo-motor

The servo-motor possesses a strongly nonlinear property due to the effect of the stimulating input voltage, load-torque and environmental operating conditions. So it is rather difficult to derive a traditional mathematical model which is capable of expressing both its dynamics and steady-state characteristics. A neural network-based adaptive control strategy is proposed in this paper. In this method, two neural networks have been adopted for system identification （NNI） and control （NNC）, respectively. Then, the commonly-used specialized learning has been modified, by taking the NNI output as the approximation output of the servo-motor during the weights training to get sensitivity information. Moreover, the rule for choosing the learning rate is given on the basis of the analysis of Lyapunov stability. Finally, an example of applying the proposed control strategy on a servo-motor is presented to show its effectiveness.