A case study in design space exploration: the Tosca environmentapplied to a telecommunication link controller

The concept of system design, or codesign, includes a variety of possible definitions according to the considered relevant aspects, the application field, and the system granularity of the analysis. The novelty of codesign with respect to the design of pure hardware and software, which are well-known subjects, arises from the tight integration between the two types of design and from the global scope of the design constraints. Since such applications strive for high volumes, there is a payoff for size, power, and speed optimization techniques. This article presents a system-level design methodology to specify, analyze, and explore different hardware/software solutions, whose benefits have been tested by redesigning a commercial device     

A dynamically-constructed fuzzy neural controller for direct model reference adaptive control of multi-input-multi-output nonlinear processes

 Conventional industrial control systems are in majority based on the single-input-single-output design principle with linearized models of the processes. However, most industrial processes are nonlinear and multivariable with strong mutual interactions between process variables that often results in large robustness margins, and in some cases, extremely poor performance of the controller. To improve control accuracy and robustness to disturbances and noise, new design strategies are necessary to overcome problems caused by nonlinearity and mutual interactions. We propose to use a dynamically-constructed, feedback fuzzy neural controller (DCF-FNC) from the input–output data of the process and a reference model, for direct model reference adaptive control (MRAC) to deal with such problems. The effectiveness of our approach is demonstrated by simulation results on a real-world example of cold mill thickness control and is compared with the performances of the conventional PID controller and the cascade correlation neural network (CCN). Exploiting the advantage of intelligent adaptive control, both the CCN and our DCF-FNC significantly increases the control precision and robustness, compared to the linear PID controller, with our DCF-FNC giving the best results in terms of both accuracy and compactness of the controller, as well as being less computationally intensive than the CCN. We argue that our DCF-FNC feedback controller with both structure and parameter learning can provide a computationally efficient solution to control of many real-world multivariable nonlinear processes in presence of disturbances and noise.     

A neural network controller for congestion control in ATM multiplexers

This paper presents and adaptive approach to the problem of congestion control arising at the User-to Network Interface (UNI) of an ATM multiplexer. We view the ATM multiplexer as a non-linear stochastic system whose dynamics are ill-defined. Real-time measurements of the arrival rate process and the queueing process, are used to identify, and minimize congestion episodes. The performance of the system is evaluated using a performance-index function which is a quantative measure of “how well” the system is performing. A three-layers backpropagation neural network controller generates a signal that attempts to minimize congestion without degrading the quality of the traffic. During periods of buffer over-load the control signal, adaptively, modulates the arrival process such that its peak-rate is throttled-down. As soon as congestion is terminated, the control signal is adjusted such that the coding rates are restored back to their original values. Adaptability is achieved by continuously adjusting the weights of the neural network controller such that the performance of the system, measured by its performance index function, is maximized over a certain optimization period. The performance index function is defined in terms of two main objectives: (1) to minimize the cell loss rate (CLR), i.e., minimize congestion episodes, and (2) to maintain the quality of the video/audio traffic by maintaining its original source coding rate. The neural network learning process can be viewed as a specialized form of reinforcement learning in the sense that the control signal is reinforced if it tends to maximize the performance index function. Performance evaluation results prove that this approach is effective in controlling congestion while maintaining the quality of the traffic.     

A robust integrator algorithm with genetic based fuzzy controller feedback for direct vector control

The voltage model used for direct vector control has in the flux calculation process an open integration problem, which is generally solved with a feedback loop. In this paper, a new design method is developed for the feedback loop of the integrator. The method, as apart from standards in the literature, uses a fuzzy controller. Fuzzy controllers are knowledge-based systems that include fuzzy rules and fuzzy membership functions to incorporate human knowledge into their knowledge base. The determination of these rules and membership functions is one the key problems when designing fuzzy controllers, and is generally affected by subjective decisions. In this study, a fuzzy controller with rules and membership functions determined by genetic algorithms (GAs) in this study is designed and tested on various motors of different power ratings. The proposed method is simulated by using MATLAB/SIMULINK and implemented on an experimental system using a TMS320C31 digital signal processor.     

Adaptive neural network control for a nonlinear Euler‐Bernoulli beam in three‐dimensional space with unknown control direction

In this paper, an adaptive neural network control system is developed for a nonlinear three‐dimensional Euler‐Bernoulli beam with unknown control direction. The Euler‐Bernoulli beam is modeled as a combination of partial differential equations (PDEs) and ordinary differential equations (ODEs). Adaptive radial basis function–based neural network control laws are designed to determine approximation of disturbances. A projection mapping operator is adopted to realize bounded approximation of disturbances. A Nussbaum function is introduced to compensate for the unknown control direction. The goal of this study is to suppress the vibrations of the Euler‐Bernoulli beam in three‐dimensional space. In addition, unknown control direction problem and bounded disturbances are considered to guarantee that the signals of the system are uniformly bounded. Numerical simulations demonstrate the effectiveness of the proposed method.     

基于RBF神经网络控制的球杆系统位置控制实验研究*

球杆系统是一种典型的高阶非线性不稳定系统,针对PID跟踪控制精度不高及BP神经网络控制训练时间较长的问题,本文提出一种带有低通滤波器的RBF神经网络控制器(RBFC)动态补偿PID控制的球杆控制方法,控制系统由RBF神经网络控制及PID控制器组成。为提高参数辨识速度和避免局部最小值,采用梯度下降法更新隐含层参数,采用带有遗忘因子的最小二乘法更新输出层权值。实验结果表明,该控制方案相比PID控制具有更高的控制精度,比BP神经网络具有更快的学习速度,低通滤波器保证了RBFC的辨识精度和稳定的控制输出,具有良好的动静态特性和控制性能。     

A direct method for robust adaptive nonlinear control with guaranteed transient performance

In this paper, the adaptive control problem is studied for a class of nonlinear systems in the presence of bounded disturbances. By utilizing a nice property of the studied systems, a novel Lyapunov-based control structure is developed, which avoids the possible control singularity problem in adaptive nonlinear control. The transient bounds of output tracking error are shown to be explicit functions of initial conditions and design parameters, and the control performance of the closed-loop system is guaranteed by suitably choosing the design parameters. Simulation study is provided to verify the theoretical results.     

An integrated congestion control mechanism for optimized performance using two-step rate controller in optical burst switching networks

Optical burst switching (OBS) is a promising solution to implement the optical internet backbone. However, the lack of adequate congestion-control mechanisms may result in high burst loss. Schemes such as fiber delay line (FDL), wavelength conversion, and deflection routing to reduce burst collision are unable to prevent the network congestion effectively. To address this problem, we propose and investigate a global solution, called Integrated Congestion-Control Mechanism (ICCM), for OBS networks. ICCM, which combines congestion avoidance with recovery mechanism, restricts the amount of burst flows entering the network according to the feedback information from core routers to edge routers to prevent network congestion. Also, a flow-policing scheme is proposed to intentionally drop the overloaded traffic with a certain probability at a core router to support fairness among flows. Moreover, the transmission rate of each flow is controlled to achieve optimized performance such as maximizing throughput or minimizing loss probability using two-step rate controller at the edge router. Simulation results show that ICCM effectively eliminates congestion within the network and that, when combined with a flow-policing mechanism, the fairness for competing flows can be supported while maintaining effective network performance.     

Dynamics identification and control of nonlinear MIMO coupled plant using supervised neural gas and comparison with recurrent neural controller

The dynamics identification and subsequent control of a nonlinear system is not a trivial issue. The application of a neural gas network that is trained with a supervised batch version of the algorithm can produce identification models in a robust way. In this paper, the neural model identifies each local transfer function, demonstrating that the local linear approximation can be done. Moreover, other parameters are analyzed in order to obtain a correct modeling. Furthermore, the algorithm is applied to control a nonlinear multi-input multi-output system composed of tanks. In addition, this plant is a coupled system where the manipulated input variables are influencing all the output variables. The aim of the work is to demonstrate that the supervised neural gas algorithm is able to obtain linear models to be used in a state space design scenario to control nonlinear coupled systems and guarantee a robust control method. The results are compared with the common approach of using a recurrent neural controller trained with a dynamic backpropagation algorithm. Regarding the steady-state errors in disturbance rejection, reference tracking and sensitivity to simple process changes, the proposed approach shows an interesting application to control nonlinear plants.

     

An analysis of stability with time-delay of link level power control in optical networks

We study the effects of time-delay on the stability of optical networks. A link level power control scheme adjusts the OSNR value of the signals toward channel OSNR optimization. We utilize the OSNR model from [Pavel, L. (2006). A noncooperative game approach to OSNR optimization in optical networks. IEEE Transactions on Automatic Control, 51(5), 848-852] along with its game-theoretic based control algorithm. Time-delay is incorporated into the closed loop system, for the general network case where every link has a unique time-delay. We derive sufficient conditions for stability under arbitrary time-delays and network configurations. The results are verified via extensive simulations.     

A performance optimization algorithm for controller reconfiguration in fault tolerant distributed model predictive control

This paper presents a performance optimization algorithm for controller reconfiguration in fault tolerant distributed model predictive control for large-scale systems. After the fault has been detected and diagnosed, several controller reconfigurations are proposed as candidate corrective actions for fault compensation. The solution of a set of constrained optimization problems with different actuator and setpoint reconfigurations is derived by means of an original approach, exploiting the information on the active constraints in the non-faulty subsystems. Thus, the global optimization problem is split into two optimization subproblems, which enable the online computational burden to be greatly reduced. Subsequently, the performances of different candidate controller reconfigurations are compared, and the better performing one is selected and then implemented to compensate the fault effects. Efficacy of the proposed approach has been shown by applying it to the benzene alkylation process, which is a benchmark process in distributed model predictive control.     

A model-based adaptive controller for chatter mitigation and productivity enhancement in CNC milling machines

Seeking a higher level of automation, according to Intelligent Manufacturing paradigm, an optimal process control for milling process has been developed, aiming at optimizing a multi-objective target function defined in order to mitigate vibration level and surface quality, while preserving production times and decreasing tool wear rate. The control architecture relies on a real-time process model able to capture the most significant phenomena ongoing during the machining, such as cutting forces and tool vibration (both forced and self-excited). For a given tool path and workpiece material, an optimal sequence of feedrate and spindle speed is calculated both for the initial setup of the machining process and for the continuous, in-process adaptation of process parameters to changes the current machining behavior. For the first time in the literature, following a Model-Predictive-Control (MPC) approach, the controller is able to adapt its actions taking into account process and axes dynamics on the basis of Optimal Control theory. The developed controller has been implemented in a commercial CNC of a 3-axes milling machine manufactured by Alesamonti; the effectiveness of the approach is demonstrated on a real industrial application and the performance enhancement is evaluated and discussed.     

A hysteresis functional link artificial neural network for identification and model predictive control of SMA actuator

In this paper, a modified Hysteresis Functional Link Artificial Neural Network (HFLANN) is proposed to identify and control a Shape Memory Alloy (SMA) actuator, which has an inherent hysteresis phenomenon. In this structure, a hysteresis operator combined with the Functional Link Artificial Neural Network (FLANN) to employ the hysteresis phenomenon and the dynamic of the SMA actuator. The hysteresis operator is introduced to capture the SMA hysteresis. And the FLANN is employed to approximate the dynamic of the system. In identification problem, the FLANN parameters are trained by Particle Swarm Optimization technique. For control problem, a Model Predictive Controller based HFLANN is derived to control the system. The identification results show that the HFLANN can employ for the SMA dynamic. The simulation and experimental results demonstrated the effectiveness of the proposed algorithm. The SMA hysteresis phenomenon is compensated completely by proposed controller.     

A study of robust controller and observer design method for a rolling object and beam system

This paper deals with a robust control problem for a ball and beam system with an uncertainty. At first, we clarify the effect of a measurement error in the beam rotational angle and its differential value. It is expressed as additional terms in the state and input matrix of the state equation, which is called as a structured uncertainty. For this system, a robust tracking controller and a robust state observer are applied. Both components are designed based on a robust control, which is called guaranteed cost control. This is a kind of cost minimization method for designing problem of a linear feedback system. The controller is obtained as the solution of the matrix algebraic Riccati like equation, which is referred to as stochastic algebraic Riccati equation. The nominal system performance is degraded due to the influence of the uncertainty, but it is able to design a system with robustness for disturbance. Next, we consider the design problem of a state observer to estimate all state variables form the output signal easy to measure, like rotation angle of the beam. For the design problem of the state observer, we consider dual problem of the original system. Consequently, our proposed system estimates the whole state vector, and using estimated state values, it controls the ball to the desired position. Both components have robustness for the uncertainty. Numerical result shows the validity of our proposed method.     

控制方向未知的不确定系统预设性能自适应神经网络反演控制

对一类控制方向未知的不确定严格反馈非线性系统的预设性能自适应神经网络反演控制问题进行了研究.系统中含有时变非匹配不确定项且控制方向未知.首先,提出了一种新的误差转化方法,放宽了对初始误差已知的限制;随后,利用径向基函数(radial basis function,RBF)神经网络及跟踪微分器分别实现了对未知函数和虚拟控制量导数的逼近,并综合运用Nussbaum函数和反演控制技术设计了控制器.所设计的控制器能保证系统内所有信号有界且输出误差满足预设的瞬态和稳态性能要求.最后的仿真研究验证了控制器设计方法的有效性.     

A neural network model for fault detection in conjunction with a programmable logic controller

This paper discusses the feasibility of using neural networks as a tool in the fault detection process. A neural network is integrated with a state language programmable logic controller, an important device in an automatic control system. Time series data related to time spent in a state is gathered and used as input into a neural network, for the purpose of identifying when a fault has occurred. A feedforward neural network is used to identify which (if any) of three types of faults may have occurred. Experimental results related to sensitivity and accuracy measures are presented. A brief review of related applications and research is also presented.     

无人机测控链路中双混沌直序扩频技术的应用

为了提高无人机测控链路的抗干扰能力、抗截获能力和多址能力,提出一种新颖的双混沌直序扩频通信方式.用混沌序列替代传统的伪随机序列,发送端采用2条不同混沌序列对信号进行直接扩频调制,接收端用混沌序列进行相关解调,然后经过差分运算后得到原始信号.通过Matlab建立仿真模型,结果表明:在相同的信道条件下,系统的性能得到提升.     

A greenhouse control with feed-forward and recurrent neural networks

Greenhouses are classified as complex systems, so it is difficult to implement classical control methods for this kind of process. In our case we have chosen neural network techniques to drive the internal climate of a greenhouse. An Elman neural network has been used to emulate the direct dynamics of the greenhouse. Based on this model, a multilayer feed-forward neural network has been trained to learn the inverse dynamics of the process to be controlled. The inverse neural network has been placed in cascade with the neural model in order to drive the system outputs to desired values. Simulation results will be given to prove the performance of neural networks in control of the greenhouse.     

Design and performance study on a new biaxial micro-accelerometer with variable cross-section beam

Microsystem Technologies - The thesis takes comb capacitance type micro-accelerometer as the research target and designs a new type of biaxial micro-accelerometer with variable cross-section beam....