Stability of a fuzzy control system is closely related to the analytical structure of the fuzzy controller, which is determined by its components such as input and output fuzzy sets and fuzzy rules. We first characterize the mathematical input-output structure of fuzzy controllers and then utilize the structure characteristics to advance stability analysis. We study how the components of a general class of Mamdani fuzzy controllers dictate the controller's input-output relationship. The controllers can use input fuzzy sets of any types, arbitrary fuzzy rules, arbitrary inference methods, either Zadeh or the product fuzzy logic AND operator, singleton output fuzzy sets, and the centroid defuzzifier. We theoretically prove that regardless of the choices for the other components, if and only if Zadeh fuzzy AND operator and piecewise linear (e.g., trapezoidal or triangular) input fuzzy sets are used, the fuzzy controllers become a peculiar class of nonlinear controllers with the following interesting characteristics: (1) they are linear with respect to input variables; (2) their control gains dynamically change with the input variables; and (3) they become linear controllers with constant gains around the system equilibrium point. These properties make the fuzzy controllers suitable for analysis and design using conventional control theory. This necessary and sufficient condition becomes a sufficient condition if the product AND operator is employed instead. We name the fuzzy controllers of this peculiar class type-A fuzzy controllers. Taking advantage of this new structure knowledge, we have established a necessary and sufficient local stability condition for the type-A fuzzy control systems. It can be used not only for the stability determination, but also for practically designing a type-A fuzzy control system that is at least stable at the equilibrium point even when model of the controlled system is mathematically unknown. Three numerical examples are provided to demonstrate the utility of our new findings.

关键词：控制器；模糊控制系统；稳定性

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The integration of fuzzy systems, Wavelet theory, and neural networks has recently become a popular approach in the engineering fields for control of nonlinear systems. Therefore, the application of Fuzzy Wavelet Neural Network controllers is clearly obvious to investigators. A lot of research has been done in the control of nonlinear systems by using the models based on type-1 Fuzzy Logic Systems (FLS). However, they are regularly unable to handle uncertainties in the rules. This chapter develops a novel structure of Type-2 Fuzzy Wavelet Neural Networks (T2FWNN) to control a nonlinear system. This has been performed by invoking some of the specific advantages of wavelets, such as dynamic compatibility, compression, and step parameter adaptation along with a combination of type-2 fuzzy concepts regarding the neural networks abilities. The proposed network is constructed based on a set of TSK fuzzy rules that includes a wavelet function in the consequent part of each rule. This can provide appropriate tools on adaptation of plant output signal to follow a desired one. In this regard, the merits of utilizing wavelets and type-2 FLS simultaneously have been discussed and explored to efficiently handle the uncertainties. It is worth mentioning that the stability of the system is effectively dependent on the learning procedure and the initial values of the network parameters. Here, an adaptive gradient descent strategy is used to adjust the unknown parameters. Furthermore, the performance of the proposed T2FWNN is compared with the type-1 FLS networks. As investigated, this method has gained considerably high levels of accuracy with the reasonable number of parameters. Finally, the efficiency of the proposed approach is demonstrated via the simulation results of two nonlinear case studies.

关键词：模糊神经网络；非线性系统；控制

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In the last decade, the demand of micro products and miniaturization has seen a wide spread growth. Currently, micro products and micro features are produced through conventional macro scale ultra-precision machines and MEMS manufacturing techniques. These technologies have limitations as conventional machining centers consume large energy and space. For mass production of micro components using non-silicon materials and real 3D shapes or free-form surfaces, mechanical micro manufacturing technology based machine tools are developed as an alternative method. The principle of "Small equipment for small parts" is gaining trend towards the investigation on micro-machine tools. One example of miniaturization of manufacturing equipment and systems is the Japanese micro-factory concept. Few micro-machines and associated handling micro grippers and transfer arms are developed to create micro-factory. The manufacturing processes are performed in a desktop factory environment. To explore the micro-factory idea, large number of micro machines can be installed in a small work-floor. The control of this micro factory concept for operation, maintenance and monitoring becomes a Cyber-physical system capable of producing micro-precision products in a fully-automated manner at low cost. Manufacturing processing data and condition monitoring of micro machine tools in a micro factory are the variables of interest to run a smooth process flow. Every machine out of hundreds of micro machines will have sensing equipment and the sensors data is being compiled at one place, ideally using wireless communication systems. One or two operators can run and monitor the whole micro-factory and access the machine if the fault alarms receive from any station. A variety of sensors will be employed for machine control, process control, metrology and calibration, condition monitoring of machine tools, assembly and integration technology at the micro-scale resulting in smooth operation of micro-factory. Single machine can be designed with a computer numerical control, but, flexible reconfigurable controllers are envisioned to control variety of processes that will lead to the development of open architecture controllers to operate micro-factory. Therefore, the control effort and algorithms have to utilize process models to improve the overall process and, ultimately, the product. Thus, we aim to introduce machine to machine (M2M) communication in the micro factory test bed. M2M communication enables micro actuator/sensor & controller devices to communicate with each other directly i.e., without human intervention, automating management, monitoring, and data collection between devices, as well as communicating with neighboring machines. All micro sensors communicate with a local short distance wireless network e.g. via Bluetooth piconet as well as with a centralized controller via WLAN 802.11 to exchange control/command from it. In this chapter, inherent issues are first highlighted where bulk micro-part manufacturing is carried out using large size machines. State-of-the-art micro machine tool systems designed and developed so far are discussed. With the help of precision engineering fundamentals and miniaturization scaling issues, a design strategy is formulated for a high precision 3-axis CNC micro machine tool as a model for micro-factory working. Based on this, a mathematical model is built that includes machine's design variables and its inherent errors. The volumetric error between tool/work-piece is evaluated from the machine's mathematical model and further used as an objective function to be minimized. Robust design optimization at micro machine development stage reveals the sensitivity analysis of each design variable. The optimization analysis employs different design of Experiment (DOE) techniques to make initial population that is governed by multi-objective genetic algorithm. Hence, the robust design is achieved for 3-axis micro machine tool using the essential knowledge base. The technique is used to remove the machine's repeatable scale errors via calibration and is known as error mapping. These errors are entered into the machine controller, which has the capability of compensating for the error. The machine does not need any extra hardware. Error mapping is a cost-effective tool in achieving volumetric accuracy in a micro manufacturing system.

关键词：微工厂；微型计算机；机器人网络物理系统

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Approximately 89% of the paved-road network in Kansas is asphalt surfaced (bituminous and composite). According to the Kansas Department of Transportation (KDOT), typical design performance period of hot-mix asphalt (HMA) pavement for new construction or reconstruction is approximately 12 years. In most cases, these pavements are overlaid as they reach the end of their design life. Both bituminous and composite pavements are usually overlaid with Superpave HMA for pavement preservation. The new highway program of KDOT also emphasizes pavement preservation. KDOT is currently seeking to extend the lives of Superpave mixes for these overlays through educated better selection of asphalt and aggregates. Thus, KDOT is contemplating use of the Hamburg Wheel Tracking Device (HWTD) as a performance tester.

关键词：沥青；路面；试验设备；骨料；建筑

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The scope of this chapter is to trace the recent developments in the field of Intelligent Multiple Models based Adaptive Switching Control (IMMASC) and provide at the same time all the essential information about the conventional single model and multiple models adaptive control, which constitute the base for the development of the new intelligent methods. This work emphasizes on the importance and the advantages of IMMASC in the field of control systems technology presenting control structures that contain linear robust models, neural models and T-S (Takagi-Sugeno) fuzzy models. One of the main advantages of switching control systems against the single model control architectures is that they are able to provide stability and improved performance in multiple environments when the systems to be controlled have unknown parameters or highly uncertain parameters. Some hybrid multiple models control architectures are presented and a numerical example is given in order to illustrate the efficiency of the intelligent methods.

关键词：自适应转换控制；单模式；神经模型

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This chapter presents the application of a particle swarm optimization (PSO) to a controller tuning in selected power electronic and drive systems. The chapter starts with a relatively simple tuning of a cascaded PI speed and position control system for a BLDC servo drive. This example serves as the background for a discussion on selecting the objective function for the PSO. Then the PSO is used in two challenging controller tuning tasks. This includes optimizing selected learning parameters in the adaptive artificial neural network (ANN) based online trained speed controller for an urban vehicle (3D problem) and selecting penalty factors in the LQR with augmented state (i.e. with oscillatory terms) for a three-phase four-leg sine wave inverter (15D problem). It is demonstrated with the help of these case studies why and where the PSO, or any other similar population based stochastic search algorithm, can be beneficial. Engineers encounter many non-straightforward controller tuning problems in power electronic systems and this chapter illustrates that in some cases it is relatively easy to reduce these tasks into the objective function selection problem. The relevant controller parameters are then determined automatically by the PSO.

关键词：粒子群优化；人工神经网络；搜索算法

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This paper proposes two types of force control system based on measurements of an acceleration sensor attached on the tip-position of a 3-link planar redundant manipulator. To estimate the acceleration with high accuracy, Kalman filter(KF) based on dynamic model of the manipulator and extended Kalman filter(EKF) based on kinematic model of that are introduced to each system. Furthermore, Disturbance Force Observer(DFOB) and Reaction Force Observer(RFOB) are also implemented in the force control system, and estimated acceleration information by KF/EKF is directly utilized in those observers. Experiments are conducted to examine the characteristics of estimation performance and compare the force control performance based on each of KF and EKF.

关键词：力控制；冗余机械臂；加速度传感器

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Based on Lyapunov stabilization theory, this paper proposes a proportional plus integral time-delayed controller to stabilize unstable equilibrium points (UPOs) embedded in chaotic attractors. The criterion is successfully applied to the classic Chua's circuit. Theoretical analysis and numerical simulation show the effectiveness of this controller.

关键词：混沌系统；比例加积分时间延迟控制器；泰勒逼近

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对某型车用直列4缸柴油机开展了机油消耗、窜气和曲轴箱压力试验研究,试验结果表明该发动机在低负荷低转速工况下燃油消耗与机油消耗的比值(机燃比)不满足设计要求,随着转速和负荷的提高,总机油消耗量呈增加趋势,曲轴箱通风(PCV)系统所占总机油消耗比例较高,各工况点平均值约为10%。借助CFD仿真手段对原油气分离器及改进方案进行了对比分析,得知原结构油气分离效率较低,改进方案分离效率大幅提高。验证试验表明,3种对比工况下改进方案窜气特性和曲轴箱压力均满足设计要求,PCV系统机油消耗均大幅度下降,说明改进方案合理可行。

关键词：柴油机；曲轴箱通风系统；气缸盖罩；油气分离

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对一台车用高压共轨直喷式柴油机,分别燃用纯柴油和B20燃料,在未加装后处理装置的原机和加装柴油机氧化催化器与颗粒氧化催化转化器(DOC+POC)后处理装置的两种状态下,利用EEPS颗粒粒径谱仪,测试其排气颗粒数量排放及其粒径分布。结果表明:未加装后处理装置时,燃用B20燃料的核态颗粒数量排放略高于柴油;而聚集态颗粒的数量排放则低于柴油;加装DOC+POC后处理装置后,排气颗粒数量排放明显下降,颗粒净化效率存在两个较高的峰值,一个在粒径10nm附近的核态颗粒区域,另一个在粒径300nm附近的聚集态颗粒区域。燃用B20燃料时,总的来说排气颗粒数量排放低于柴油,一DOC+POC对多数工况下颗粒的净化效率明显高于柴油。

关键词：柴油机；生物柴油；颗粒排放；柴油机氧化催化器；颗粒氧化催化转化器

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