AI approaches to identification and control of total plant production systems

Recent progress in intelligent control techniques has enabled complex systems such as cultivation and fruit-storage processes to be dealt with. This paper presents the application of a hierarchical intelligent control system, which consists of an expert system and an optimizer based on neural networks and genetic algorithms, for optimizing a total plant production process. Environmental factors in the cultivation and storage processes are optimally controlled, based on the physiological status of the plant (or fruit). The expert system determines suitable environmental setpoints throughout growth, and the optimizer determines optimal environmental setpoints during important growth stages and during storage, based on plant responses. In the optimizer, neural networks were used for the identification of plant responses to environmental factors, and genetic algorithms were used to search for the optimal environmental setpoints through the simulation of the identified models. Optimal setpoints of the nutrient concentration in hydroponic tomato cultivation and optimal setpoints of the temperature during tomato storage were determined using this control technique.     

Modeling and control of non-linear systems using soft computing techniques

This work is an attempt to illustrate the utility and effectiveness of soft computing approaches in handling the modeling and control of complex systems. Soft computing research is concerned with the integration of artificial intelligent tools (neural networks, fuzzy technology, evolutionary algorithms, …) in a complementary hybrid framework for solving real world problems. There are several approaches to integrate neural networks and fuzzy logic to form a neuro-fuzzy system. The present work will concentrate on the pioneering neuro-fuzzy system, Adaptive Neuro-Fuzzy Inference System (ANFIS). ANFIS is first used to model non-linear knee-joint dynamics from recorded clinical data. The established model is then used to predict the behavior of the underlying system and for the design and evaluation of various intelligent control strategies.     

New approach to intelligent control systems with self-exploring process

This paper proposes an intelligent control system called self-exploring-based intelligent control system (SEICS). The SEICS is comprised of three basic mechanisms, namely, controller, performance evaluator (PE), and adaptor. The controller is constructed by a fuzzy neural network (FNN) to carry out the control tasks. The PE is used to determine whether or not the controller's performance is satisfactory. The adaptor, comprised of two elements, action explorer (AE) and rule generator (RG), plays the main role in the system for generating new control behaviors in order to enhance the control performance. AE operates through a three-stage self-exploration process to explore new actions, which is realized by the multiobjective genetic algorithm (GA). The RG transforms control actions to fuzzy rules based on a numerical method. The application of the adaptor can make a control system more adaptive in various environments. A simulation of robotic path-planning is used to demonstrate the proposed model. The results show that the robot reaches the target point from the start point successfully in the lack-of-information and changeable environments.     

非线性互联系统的分散混合自适应智能控制

针对一类高阶互联MIMO非线性系统，利用TS模糊系统和神经网络的通用逼近能力，在神经网络控制器中引入模糊基函数，提出一种分散混合自适应智能控制器设计的新方案．基于等价控制思想，设计分散自适应控制器，无需计算TS模型．通过对不确定项进行自适应估计，取消了其存在已知上界的假设．通过理论分析，证明了闭环智能控制系统所有信号有界，跟踪误差收敛到零．     

一种工业回转窑炉的混合智能控制

本文提出一种专家控制与模糊神经网络控制相结合的新型混合智能控制（ＨＩＣ）。这种ＨＩＣ控制系统由知识库、信息预处理器、智能协调控制器组成。计算机仿真和实际的工业回转窑炉温控实验结果表明，ＨＩＣ具有良好的控制性能。     

An intelligent robust tracking control for electrically-driven robot systems

This article addresses the problem of designing intelligent robust tracking controls of robot systems actuated by brushed direct current motors. The structures of both mechanical and electrical dynamics are allowed to be completely unknown and adaptive fuzzy (or neural network) systems are employed to approximate these two uncertainties. Consequently, an adaptive fuzzy-based (or neural network-based) state feedback tracking controller is developed such that the resulting closed-loop system guarantees that all the states and signals are bounded and the tracking error can be made as small as possible. Finally, simulation examples are made to demonstrate the effectiveness and tracking performance.     

Meta knowledge of intelligent manufacturing: An overview of state-of-the-art

This paper tries to stimulate empirical research into the overall impacts of intelligent systems in manufacturing aspects. To reach this goal, a schema of intelligent applications is provided for each aspect as frame base structure, meaning the knowledge of intelligent applications in that specific aspect. Then, a semantic network is developed for intelligent manufacturing based on hierarchical structure of manufacturing systems to provide Meta knowledge of intelligent manufacturing applications. The analysis of semantic network indicates an increasing growth in application of soft computing in manufacturing aspects. Specially, fuzzy logic and its derivates have found more applications in recent years.     

Neuro-fuzzy position control of demining tele-operation system based on RNN modeling

The paper considers the neuro-fuzzy position control of multi-finger robot hand in tele-operation system—an active master–slave hand system (MSHS) for demining. Recently, fuzzy control systems utilizing artificial intelligent techniques are also being actively investigated in robotic area. Neural network with their powerful learning capability are being sought as the basis for many adaptive control systems where on-line adaptation can be implemented. Fuzzy logic on the other hand has been proved to be rather popular in many control system applications providing a rule-base like structure. In this paper, the design and optimization process of fuzzy position controller is supported by learning techniques derived from neural network where a radial basis function (RBF) neural network is implemented to learn fuzzy rules and membership functions with predictor of recurrent neural network (RNN) model. The results of experiment show that based on the predictive capability of RNN model neuro-fuzzy controller with good adaptation and robustness capability can be designed.     

Unified stabilizing controller synthesis approach for discrete-time intelligent systems with time delays by dynamic output feedback

A novel model, termed the standard neural network model (SNNM), is advanced to describe some delayed (or non-delayed) discrete-time intelligent systems com- posed of neural networks and Takagi and Sugeno (T-S) fuzzy models. The SNNM is composed of a discrete-time linear dynamic system and a bounded static nonlinear operator. Based on the global asymptotic stability analysis of the SNNMs, linear and nonlinear dynamic output feedback controllers are designed for the SNNMs to stabilize the closed-loop systems, respectively. The control design equations are shown to be a set of linear matrix inequalities (LMIs) which can be easily solved by various convex optimization algorithms to determine the control signals. Most neural-network-based (or fuzzy) discrete-time intelligent systems with time delays or without time delays can be transformed into the SNNMs for controller synthesis in a unified way. Three application examples show that the SNNMs not only make controller synthesis of neural-network-based (or fuzzy) discrete-time intelligent systems much easier, but also provide a new approach to the synthesis of the controllers for the other type of nonlinear systems.     

Fuzzy neural networks for intelligent design retrieval using associative manufacturing features

In the conceptual design stage, designers usually initiate a design concept through an association activity. The activity helps designers collect and retrieve reference information regarding a current design subject instead of starting from scratch. By modifying previous designs, designers can create a new design in a much shorter time. To computerize this process, this paper proposes an intelligent design retrieval system involving soft computing techniques for both feature and object association functions. A feature association method that utilizes fuzzy relation and fuzzy composition is developed to increase the searching spectrum. In the mean time, object association functions composed by a fuzzy neural network allow designers to control the similarity of retrieved designs. Our implementation result shows that the intelligent design retrieval system with two soft computing based association functions can retrieve target reference designs as expected.     

A combination of fuzzy logic and neural network controller for multiple-input multiple-output systems

Generally, the difficulty of multiple-input multiple-output (MIMO) systems control is how to overcome the coupling effects between the degrees of freedom. Owing to the computational burden and dynamic uncertainty of MIMO systems, the model-based decoupling approach is not practical for real-time control. A hybrid fuzzy logic and neural network controller (HFNC) is proposed here to overcome this problem and to improve the control performance. Firstly, a traditional fuzzy controller (TFC) is designed from a single-input single-output (SISO) systems viewpoint for controlling the degrees of freedom of a MIMO system. Secondly, an appropriate coupling neural network controller is introduced into the TFC for compensating the system coupling effects. This control strategy not only can simplify the implementation problem of fuzzy control but also can improve the control performance. The state-space approach for fuzzy control systems stability analysis is employed to evaluate the stability and robustness of this intelligent hybrid controller. In addition, a dynamic absorber with a twolevel mass-spring-damper structure was designed and constructed to verify the stability and robustness of a HFNC by numerical simulation and to investigate the control performance by comparing the experimental results of the HFNC with that of a TFC for this MIMO system.     

规则推理与神经计算智能控制系统改进及比较

针对生产生活实践中的智能系统在实施控制过程中关键参数的实时在线智能整定与优化问题与需求,实现将不同类型人工智能方法与经典的控制方法对接从而构成多种复合控制（AI-CC）方法,提出改进算法并进行理论分析与仿真对比研究。首先实现了基于规则与模糊推理机制的AI-CC方法,提出了增量式改进算法,进而提出基于小波神经网络的AI-CC方法,进一步对两类智能系统的稳定性进行理论分析,提出稳定性保证算法,最后对比研究不同类型的智能系统在智能程度与性能特征方面的差异。研究成果为该领域研究者提供了多种改进的智能控制算法及其对比参照和理论分析,为该方法在工程实践中低成本地升级并稳定可靠地应用提供可操作方案。     

Hybrid intelligent control scheme of a polymerization kettle for ACR production

Polymerization kettle is the key controlled plant in ACR (Acrylate Copolymer Resin) production, which is a nonlinear time-delay system with parametric variance. However, modeling difficulties make the plant dynamic model poorly defined. A hybrid intelligent control scheme including an intelligent predictor is designed for this complex plant based on time-delay compensation theory. It consists of a Smith neural-network predictor and a self-adjusting-scaling-factor fuzzy logic controller. The simulation experiments verified the performance of our proposed system in two scenarios: one with invariant parameters and the other with time-varying parameters. Moreover, the comparison to other three typical control methods including Smith PID, Smith neural-network PID and Smith fuzzy logic control is also presented, which demonstrates that the proposed control scheme has satisfactory effect. Even when the system parameters vary with time, the proposed system still gives superior performance and improved robustness.     

Adaptive intelligent control of aircraft systems with a hybrid approach combining neural networks, fuzzy logic and fractal theory

We describe in this paper a hybrid method for adaptive model-based control of nonlinear dynamic systems using neural networks, fuzzy logic and fractal theory. The new neuro-fuzzy-fractal method combines soft computing techniques with the concept of the fractal dimension for the domain of nonlinear dynamic system control. The new method for adaptive model-based control has been implemented as a computer program to show that the neuro-fuzzy-fractal approach is a good alternative for controlling nonlinear dynamic systems. It is well known that chaotic and unstable behavior may occur for nonlinear systems. Normally, we will need to control this type of behavior to avoid structural problems with the system. We illustrate in this paper our new methodology with the case of controlling aircraft dynamic systems. For this case, we use mathematical models for the simulation of aircraft dynamics during flight. The goal of constructing these models is to capture the dynamics of the aircraft, so as to have a way of controlling this dynamics to avoid dangerous behavior of the aircraft dynamic system.     

Fusion of soft and hard computing: multi-dimensional categorization of computationally intelligent hybrid systems

The concept of fusion of soft computing and hard computing has rapidly gained importance over the last few years. Soft computing is known as a complementary set of techniques such as neural networks, fuzzy systems, or evolutionary computation which are able to deal with uncertainty, partial truth, and imprecision. Hard computing, i.e., the huge set of traditional techniques, is usually seen as the antipode of soft computing. Fusion of soft and hard computing techniques aims at exploiting the particular advantages of both realms. This article introduces a multi-dimensional categorization scheme for fusion techniques and applies it by analyzing several fusion techniques where the soft computing part is realized by a neural network. The categorization scheme facilitates the discussion of advantages or drawbacks of certain fusion approaches, thus supporting the development of novel fusion techniques and applications.     

Soft computing-based active vibration control of a flexible structure

Control of vibration of flexible structures has been of remarkable research attention in the last decade. Conventional control methods have not been widely successful due to the dynamic complexity of flexible structures. The literature has recently seen an emergence of demand of soft computing techniques in modelling and control of such dynamic systems. However, the form of soft computing required depends on the nature of the application. This paper accordingly presents investigations into modelling and control techniques based on soft computing methods for vibration suppression of two-dimensional flexible plate structures. The design and analysis of an active vibration control (AVC) system utilising soft computing techniques including neural networks and fuzzy logic is presented. The investigation involves soft computing approach with single-input single-output (SISO) and single-input multi-output (SIMO) AVC structures. A comprehensive comparative assessment of the approaches in terms of performance and design efficiency is also provided. Investigations reveal that the developed soft computing-based AVC system performs very well in the suppression of vibration of a flexible plate structure. It is also shown that the developed SIMO AVC system performs much better in the suppression of vibration of a flexible plate structure in comparison to the SISO AVC system.