Fuzzy modelling in expert systems for process control

This chapter deals with the potential of expert system–based control software for developing some of the sophisticated control and automation techniques that are required for the process chromatography of proteins. An important distinction must be made between automation and control. Automation involves the simple replacement of manual actions with automatic ones and for chromatography usually entails switching valves, and hence the fluid stream, at preset points in the process. The majority of conventional process control programs are written in imperative languages, and this makes real-time interfacing of the program with the plant and equipment a relatively straightforward procedure. The development of an expert system for process control is a time-consuming and multidisciplinary activity. The central module houses the column, inlet filters, and much of the monitoring equipment. The feed and receiving vessels are located in the right-hand module together with the pumps and most of the valves.

Knowledge representation in expert systems for process control

Architectures and Techniques of Artificial Intelligence in Process Control

Abstract

The numerical modelling and process simulation for the fault diagnosis of rotary kiln incinerator were accomplished. In the numerical modelling, two models applied to the modelling within the kiln are the combustion chamber model including the mass and energy balance equations for two combustion chambers and 3D thermal model. The combustion chamber model predicts temperature within the kiln, flue gas composition, flux and heat of combustion. Using the combustion chamber model and 3D thermal model, the production-rules for the process simulation can be obtained through interrelation analysis between control and operation variables. The process simulation of the kiln is operated with the production-rules for automatic operation. The process simulation aims to provide fundamental solutions to the problems in incineration process by introducing an online expert control system to provide an integrity in process control and management. Knowledge-based expert control systems use symbolic logic and heuristic rules to find solutions for various types of problems. It was implemented to be a hybrid intelligent expert control system by mutually connecting with the process control systems which has the capability of process diagnosis, analysis and control.

This paper proposes new algorithms based on the fuzzy c-regressing model algorithm for Takagi-Sugeno (T-S) fuzzy modeling of the complex nonlinear systems. A fuzzy c-regression state model (FCRSM) algorithm is a T-S fuzzy model in which the functional antecedent and the state-space-model-type consequent are considered with the available input-output data. The antecedent and consequent forms of the proposed FCRSM consists mainly of two advantages: one is that the FCRSM has low computation load due to only one input variable is considered in the antecedent part; another is that the unknown system can be modeled to not only the polynomial form but also the state-space form. Moreover, the FCRSM can be extended to FCRSM-ND and FCRSM-Free algorithms. An algorithm FCRSM-ND is presented to find the T-S fuzzy state-space model of the nonlinear system when the input-output data cannot be precollected and an assumed effective controller is available. In the practical applications, the mathematical model of controller may be hard to be obtained. In this case, an online tuning algorithm, FCRSM-FREE, is designed such that the parameters of a T-S fuzzy controller and the T-S fuzzy state model of an unknown system can be online tuned simultaneously. Four numerical simulations are given to demonstrate the effectiveness of the proposed approach.

Interpretability improvements to find the balance interpretability-accuracy in fuzzy modeling: an overview.- Regaining comprehensibility of approximative fuzzy models via the use of linguistic hedges.- Identifying flexible structured premises for mining concise fuzzy knowledge.- A multiobjective genetic learning process for joint feature selection and granularity and contexts learning in fuzzy rule-based classification systems.- Extracting linguistic fuzzy models from numerical data-AFRELI algorithm.- Constrained optimization of fuzzy decision trees.- A new method for inducing a set of interpretable fuzzy partitions and fuzzy inference systems from data.- A Feature Ranking Algorithm for Fuzzy Modelling Problems.- Interpretability in multidimensional classification.- Interpretable semi-mechanistic fuzzy models by clustering, OLS and FIS model reduction.- Trade-off between approximation accuracy and complexity: TS controller design via HOSVD based complexity minimization.- Simplification and reduction of fuzzy rules.- Effect of rule representation in rule base reduction.- Singular value-based fuzzy reduction with relaxed normalization condition.- Interpretability, complexity, and modular structure of fuzzy systems.- Hierarchical genetic fuzzy systems: accuracy, interpretability and design autonomy.- About the trade-off between accuracy and interpretability of Takagi-Sugeno models in the context of nonlinear time series forecasting.- Accurate, transparent and compact fuzzy models by multi-objective evolutionary algorithms.- Transparent fuzzy systems in modeling and control.- Uniform fuzzy partitions with cardinal splines and wavelets: getting interpretable linguistic fuzzy models.- Relating the theory of partitions in MV-logic to the design of interpretable fuzzy systems.- A formal model of interpretability of linguistic variables.- Expressing relevance and interpretability of rule-based systems.- Conciseness of fuzzy models.- Exact trade-off between approximation accuracy and interpretability: solving the saturation problem for certain FRBSs.- Interpretability improvement of RBF-based neurofuzzy systems using regularized learning.- Extracting fuzzy classification rules from fuzzy clusters on the basis of separating hyperplanes.

Considering the difficulty of getting accurate forward and inverse models of object in the majority of practical plants with IMC, A modeling technique based on C-R fuzzy step model for nonlinear systems is introduced. On that basis, nonlinear fuzzy internal model control (FIMC) design based on C-R fuzzy step model is studied. Because there is the model mismatch problem between the identified forward and inverse models of the plant based on the input-output data of plant, it will make the system controllable quality lessened in the control system based on C-R fuzzy step Model. To improve the performance of the control system, PID control is connected parallel to the fuzzy internal model control structure, then the double control strategy with nonlinear fuzzy internal model control and PID control based on C-R fuzzy step model (CR-FIMC-PID) is proposed. The simulation results show that the proposed control strategy is effective, feasible, and has good control performance.

Data mining with a multitude of methodologies is a good basis for the integration of intelligent systems. Small, specialised systems have a large number of feasible solutions, but developing truly adaptive, and still understandable, systems for highly complex systems require domain expertise and more compact approaches at the basic level. This paper focuses on the integration of methodologies in the smart adaptive applications. Statistical methods and arti cial neural networks form a good basis for the data-driven analysis of interactions and fuzzy logic introduces solutions for knowledge-based understanding the system behaviour and the meaning of variable levels. Efficient normalisation, scaling and decomposition approaches are the key methodologies in developing large-scale applications. Linguistic equation (LE) approach originating from fuzzy logic is an efficient technique for these problems. The nonlinear scaling methodology based on advanced statistical analysis is the corner stone in representing the variable meanings in a compact way to introduce intelligent indices for control and diagnostics. The new constraint handling together with generalised norms and moments facilitates recursive parameter estimation approaches for the adaptive scaling. Well-known linear methodologies are used for the steady state, dynamic and case-based modelling in connection with the cascade and interactive structures in building complex large scale applications. To achieve insight and robustness the parameters are de ned separately for the scaling and the interactions. Introduction EK Juuso Intelligent Methods in Modelling and Simulation of Complex Systems 2 SNE 24(1) – 4/2014 ON 1 Steady-State Modelling multiple input, multiple output MIMO response surface methodology RSM multiple input, single output MISO Fuzzy set theory Extension principle Type-2 fuzzy Figure 1: Methodologies for modelling of complex system. EK Juuso Intelligent Method of Modelling and Simulation in Complex Systems SNE 24(1) – 4/2014 3 O N Linguistic fuzzy models Takagi-Sugeno (TS) fuzzy models Singleton models Fuzzy relational models Figure 2: Combined fuzzy modelling. Artificial neural networks ANN Linear networks multilayer perceptron MLP backpropagation learning Neurofuzzy systems Figure 3: A fuzzy neuron. function expansion Approximate reasoning 2 Decomposition Methodologies EK Juuso Intelligent Methods in Modelling and Simulation of Complex Systems 4 SNE 24(1) – 4/2014 ON Decomposition Figure 4: Decomposition for modelling. Hierarchical clustering Partitioning-based clustering algorithms fuzzy clustering Fuzzy c-means (FCM) Subtractive clustering Neural clustering Robust clustering number of clusters Composite local model Linear parameter varying (LPV) models Piecewise affine (PWA) systems Fuzzy models Multiple neural network systems mixed approach EK Juuso Intelligent Method of Modelling and Simulation in Complex Systems SNE 24(1) – 4/2014 5 O N 3 Adaptive Nonlinear Scaling Membership 3.1 Working point and feasible ranges Figure 5: Nonlinear scaling [28] EK Juuso Intelligent Methods in Modelling and Simulation of Complex Systems 6 SNE 24(1) – 4/2014 ON 3.2 Membership definitions linguistic range linguistic values • the corner points (Figure 5) are good for visualisation; • the parameters suit for tuning; • the coefficients are used in the calculations. 3.3 Adaptation of nonlinear scaling EK Juuso Intelligent Method of Modelling and Simulation in Complex Systems SNE 24(1) – 4/2014 7 O N 4 Intelligent Systems 4.1 LE models 4.2 Hybrid LE systems EK Juuso Intelligent Methods in Modelling and Simulation of Complex Systems 8 SNE 24(1) – 4/2014 ON extension principle linguistification

Fuzzy systems modeling in practice

US reconfirms anti-dumping duties on Indian & Chinese carbazole violet

Based on the knowledge rule models and the analysis mathematical models, the two-level real-time expert control system (ECS) was established. With the self-learning ability in expert optimal mode, the expert system (ES) was developed to find the calcining zone's optimal temperature value. To optimize the process, the real-time control system (RTCS) was designed to realize the control of the feeding capacity and negative pressure by self-adjusting algorithm based on CARMA module and least square method. At present, the system was started to work on the stream, and the long-term application shows that it has high reliability and good practicability. Besides, the process and the product quality are developed, and the power is economized.

An expert system for manufacturing cell control

In the simplest cases, P-, PI- and PID-controllers with rigid (unchanged) parameter settings are used to control technological processes. If the mathematical model of the control object contains unknown disturbing influences and parameters that change during the control process, then a digital control system with a learning model should be used. The tasks of control, analysis and modeling of various processes and systems are solved using their mathematical models. The choice of a model is dictated by the conditions of implementation and the requirement of adequacy. The problem of developing control algorithms under uncertainty occupies one of the central places in modern control theory. To solve the arising problems of structural and parametric identification, as a rule, methods, and algorithms of the theory of adaptive control systems are used. The application of the principles of adaptation allows to ensure high accuracy of modeling with a significant change in the dynamic properties of the system under study, to unify individual subsystems and their blocks; reduce the development and debugging time of the system. This article discusses the problem of studying a digital adaptive system for turning the blades of guide apparatus (GA) of an axial compressor of gas turbine engines (GTE). The aim of the study is to increase the efficiency of the control system for turning the blades of the GA using a digital adaptive system. The gas-dynamic stability of the GTE axial compressor under changes in external conditions and engine throttling is provided by air bypass and turning the blades of the first and last compressor stages. The statement of the problem of synthesis of the system of adaptive control of the rotation of the blades of the GA compressor of the gas turbine engine is carried out. A mathematical model of the ACS in the canonical form “model of the system in the state space” and an algorithm for turning the blades of the GA using an adaptive PI-controller have been developed. Simulation modeling of ACS was carried out using the Matlab / Simulink software package. When implementing an adaptive PI-controller, the pressure deviation downstream of the compressor stage from the required value is reduced to 0.4 %.

This research is aimed at solving the problem of safeguarding robot operation by modeling the subjective reality of an intellectual anthropomorphic robot. Analysis has been carried out on trends in designing advanced robot control systems and methods of improving the safety of robot usage. The following conclusions are drawn: the risk of causing damage to human life, health or property increases when people interact with robots; it is necessary to combine neural network control systems with expert control systems in order to enhance the intellect of a robot and raise the level of trust held by humans towards robots; developing norms and regulations, designing collaborative robots, drawing visualization diagrams for safety zones cannot really provide the depth of robot socialization sufficient for the human society; an informative model of the subjective reality could be integrated into an intellectual anthropomorphic robot to provide a safer ‘human-robot’ interaction. Such an approach should result in achieving the greatest trust of humans and their safety due to anthropomorphic conversion of both the external design of a robot and its internal control structure. By its nature, the research is interdisciplinary – it is run in the fields of artificial intelligence and philosophy of subjective reality.