Adaptive generalized minimum variance congestion controller for dynamic TCP/AQM networks

Generic generalized minimum variance-based (GMV) controllers have been adopted as efficient control mechanisms especially in presence of measurement noise. However, such controllers exhibit degraded performance with change in process dynamics. To overcome this problem, a novel congestion controller based on active queue management (AQM) strategy for dynamically varying TCP/AQM networks known as adaptive generalized minimum variance (AGMV) is proposed. AGMV is the combination of the real-time parameter estimation and GMV. The performance of the proposed scheme is evaluated and compared with its adaptive minimum variance (AMV) counterpart under two distinct scenarios: TCP network with unknown parameters and TCP network with time varying parameters. Simulation results indicate that, in either case, AGMV is able to keep the queue length around the desired point. In addition, the superior performance of the proposed controller has been shown with regard to the PI controller, which is well-known in the AQM domain.     

Multivariable disturbance modelling for web processes

This paper considers suitable disturbance models for use in the design of control systems for web forming processes. It suggests a practical disturbance representation and, using spectral factorization, presents a multivariable aggregated model of the disturbance. The paper compares generalized minimum variance (GMV) controllers designed using uncorrelated and correlated disturbance models and demonstrates the improvement in performance of a multivariable GMV controller which is designed to accommodate the cross direction correlation in the disturbance compared to the GMV controller that is commonly used in practice, which is based on a model that ignores the effects of correlated disturbances.     

Stable adaptive neurocontrol for nonlinear discrete-time systems

This paper presents a novel approach in designing neural network based adaptive controllers for a class of nonlinear discrete-time systems. This type of controllers has its simplicity in parallelism to linear generalized minimum variance (GMV) controller design and efficiency to deal with complex nonlinear dynamics. A recurrent neural network is introduced as a bridge to compensation simplify controller design procedure and efficiently to deal with nonlinearity. The network weight adaptation law is derived from Lyapunov stability analysis and the connection between convergence of the network weight and the reconstruction error of the network is established. A theorem is presented for the conditions of the stability of the closed-loop systems. Two simulation examples are provided to demonstrate the efficiency of the approach.     

不确定非线性系统的模糊鲁棒跟踪控制

提出了一种基于T-S模糊型的鲁捧自适应跟踪控制方法.整个控制方案在结合所有 的局部线性状态反馈控制器的基础上,引入了基于自适应神经网络的鲁棒控制器.所提出的 模糊自适应鲁棒控制器设计方法不需要求取李亚普诺夫方程的公共解,不要求系统的不确定 性项满足任何匹配条件或约束条件所提出的带有补偿项的完全自适应RBF神经网络,通过 在线自适应调整RBF神经网络的权重、函数中心和宽度,提高了神经网络的学习能力,可以 有效地对消系统的未知不确定性的影响.同时通过自适应补偿项来在线估计神经网络的近似 误差边界,弥补了神经网络的不足.所提出的方案保证了闭环系统的稳定性,有效地提高了 系统的鲁棒性和跟踪性能.仿真实例表明了所提出方法的有效性.     

A new stable tracking control scheme for robotic manipulators

The paper considers tracking control of robots in joint space. A new control algorithm is proposed based on the well known computed torque method and a compensating controller. The compensating controller is realized by using a switch type structure and an RBF neural network. It is shown that stability of the closed loop system and better tracking performance can be established based on Lyapunov theory. Simulation results are also provided to support our analysis.     

非线性零阶接近有界多模型神经网络自适应控制器

针对一类单变量非线性离散时间系统,提出一种零阶接近有界的多模型神经网络自适应控制器。该控制器包含一个非线性鲁棒自适应控制器和一个非线性神经网络自适应控制器。当系统非线性项放宽到零阶接近有界时,这两个控制器分别用于保证系统的稳定性和提高系统的性能,系统的控制输入由切换机构在两个控制器之间进行切换产生。最后给出了稳定性和收敛性证明,并通过仿真实验验证了该控制器的有效性。     

Resource Allocation for Reliable Communication Between Controllers and Switches in SDN

In software-defined networking (SDN), the communication between controllers and switches is very important, for switch can only work by relying on flow tables received from its controller. Therefore, how to ensure the reliability of the communication between controllers and switches is a key problem in SDN. In this paper, we study this problem from two aspects: the controller placement and the resource backup aspect. Firstly, in order to implement the reliable communication and meet the required propagation delay between controllers and switches, a min-cover based controller placement approach is proposed. Then, in order to protect both controllers and control paths from regional failure, a backup method based on an exponential decay failure model is proposed, which considers the regional influence and the survivability of backup controllers and control paths. Simulations show that our controller placement approach can meet the reliability and delay requirement with appropriate controller allocation scheme, and our backup method can improve the survivability of backup controllers and control paths while ensuring the performance of control network.     

A suite of robust controllers for the manipulation of microscale objects.

A suite of novel robust controllers is introduced for the pickup operation of microscale objects in a microelectromechanical system (MEMS). In MEMS, adhesive, surface tension, friction, and van der Waals forces are dominant. Moreover, these forces are typically unknown. The proposed robust controller overcomes the unknown contact dynamics and ensures its performance in the presence of actuator constraints by assuming that the upper bounds on these forces are known. On the other hand, for the robust adaptive critic-based neural network (NN) controller, the unknown dynamic forces are estimated online. It consists of an action NN for compensating the unknown system dynamics and a critic NN for approximating a certain strategic utility function and tuning the action NN weights. By using the Lyapunov approach, the uniform ultimate boundedness of the closed-loop manipulation error is shown for all the controllers for the pickup task. To imitate a practical system, a few system states are considered to be unavailable due to the presence of measurement noise. An output feedback version of the adaptive NN controller is proposed by exploiting the separation principle through a high-gain observer design. The problem of measurement noise is also overcome by constructing a reference system. Simulation results are presented and compared to substantiate the theoretical conclusions.     

Feedback QoS control scheme for wireless network applications

Wireless LAN networking is an indispensable technology in an All-IP network architecture to satisfy the “anytime and anywhere” communication requirement of end users. This investigation proposes feedback controllers designing based on dynamic quality-of-service requirement for wireless LAN multimedia services. During the controllers design process, the time-domain is replaced by the s-domain, simplifying the calculation. This work presents three controllers namely proportional integral (PI), proportional derivative (PD) and proportional integral derivative (PID). Experimental results show that systems that employ the proposed controllers can quickly achieve the required system performance. Additionally, the PID controller has the best performance, and can improve delay performance by a rate 11.44% that without the feedback controller. The PI controller is superior to the PD controller. The delay when using the PD is 6.2% less than that achieved without the feedback controller.     

Design of the speed controller for sensorless electric drives based on AI techniques: a comparative study

The paper investigates applicability of different artificial intelligence (AI) techniques in the design of a speed controller for electric drives. A speed-sensorless drive system is considered. A controller structure consisting of a load torque observer, a speed estimator and a speed predictor is developed. Next, different AI based approaches to speed controller design are investigated. The speed controllers based on (1) feed-forward neural network, (2) neuro-fuzzy network, and (3) self-organising Takagi–Sugeno (TS) rule based model are designed. A comparative analysis of the drive behaviour with these three types of AI based speed controllers is performed. In addition, a comparison is made with respect to the drive performance obtained with a conventional optimised PI controller. A detailed simulation study of a number of transients indicates that the best performance, in terms of accuracy and computational complexity, is offered by the self-organising Takagi–Sugeno controller. The controllers are developed and tested for a plant comprising a variable-speed separately excited DC motor.