Rule Modules Research Articles

403 乗用車モデルに対する動吸振器を併用した単一入力ルール群結合型ファジィ推論によるアクティブサスペンションの一研究

403 乗用車モデルに対する動吸振器を併用した単一入力ルール群結合型ファジィ推論によるアクティブサスペンションの一研究

402 単一入力ルール群結合型ファジィ推論による乗用車モデルに対する操舵・サスペンション制御の一研究

402 単一入力ルール群結合型ファジィ推論による乗用車モデルに対する操舵・サスペンション制御の一研究

A proposal of SIRMs dynamically connected fuzzy inference model for plural input fuzzy control

Single input rule modules (SIRMs) dynamically connected fuzzy inference model is proposed for plural input fuzzy control. For each input item, a SIRM is constructed and a dynamic importance degree is defined. The dynamic importance degree consists of a base value insuring the role of the input item through a control process, and a dynamic value changing with control situations to adjust the dynamic importance degree. Each dynamic value can be easily tuned based on the local information of current state. The model output is obtained by summarizing the products of the dynamic importance degree and the fuzzy inference result of each SIRM. The controller constructing method for constant value control systems is given, and constant value controls of typical first- and second-order lag plants are tested. The simulation results show that by using the proposed mode, the reaching time can be reduced by more than 15% without any steady-state error, overshoot, or vibration compared with the SIRMs fixed importance degree connected fuzzy inference model. The proposed model is further successfully applied to stabilization control of an inverted pendulum system including the position control of the cart.

Stabilization control of series-type double inverted pendulum systems using the SIRMs dynamically connected fuzzy inference model

A new fuzzy controller for stabilizing series-type double inverted pendulum systems is proposed based on the SIRMs (Single Input Rule Modules) dynamically connected fuzzy inference model. The controller deals with six input items. Each input item is provided with a SIRM and a dynamic importance degree (DID). The SIRM and the DID are set up such that the angular control of the upper pendulum takes the highest priority order over the angular control of the lower pendulum and the position control of the cart when the relative angle of the upper pendulum is big. By using the SIRMs and the DIDs, the control priority orders are automatically adjusted according to control situations. Simulation results show that the controller stabilizes series-type double inverted pendulum systems of different parameter values in about 10.0 s for a wide range of the initial angles.

Upswing and stabilization control of inverted pendulum system based on the SIRMs dynamically connected fuzzy inference model

A new fuzzy controller is presented based on the single input rule modules (SIRMs) dynamically connected fuzzy inference model for upswing and stabilization control of inverted pendulum system. The fuzzy controller takes the angle and angular velocity of the pendulum and the position and velocity of the cart as its input items, and the driving force as its output item. Each input item is assigned with a SIRM and a dynamic importance degree. When the pendulum locates at the pending domain, the fuzzy controller becomes an upswing controller by using the saturation feature of the membership functions of the pendulum angle. When the pendulum locates at the upright domain, the fuzzy controller then becomes a stabilization controller and realizes smoothly the pendulum angular control and the cart position control in parallel by using the SIRMs and the dynamic importance degrees. The fuzzy controller has a simple structure and is easily understandable compared with the other approaches. Simulation results show that the fuzzy controller can swing up the pendulum from the pending position and then stabilize the whole system in about 3.0 s.

A New Fuzzy Controller for Stabilizing Inverted Pendulums Based on Single Input Rule Modules Dynamically Connected Fuzzy Inference Model

A fuzzy controller is presented based on the Single Input Rule Modules (SIRMs) dynamically connected fuzzy inference model for stabilization control of inverted pendulums. The angle and angular velocity of the pendulum and the position and velocity of the cart are selected as input items and the driving force as the output item. By using SIRMs and dynamic importance degrees, the fuzzy controller realizes angular control of the pendulum and position control of the cart in parallel with totally only 24 fuzzy rules. Switching between angular control of the pendulum and position control of the cart is smoothly performed by automatically adjusting dynamic importance degrees according to control situations. For any inverted pendulums, of which the pendulum length is among [0.5m, 2.2m], simulation results show that the proposed fuzzy controller has a high generalization ability to stabilize the pendulum systems completely in about 6.0 seconds when the initial angle of the pendulum is among [-30.0°, +30.0°], or the initial position of the cart is among [-2.1m, +2.1m].

An Active Suspension System of One-Wheel Car Models Using Fuzzy Reasoning.

This paper is concerned with the design of an active suspension system for one-wheel car models using fuzzy reasoning. The proposed active control for the suspension is derived as a linear combination of the defuzzificated values of the outputs in single input rule modules. The model is approximately described as a two-degrees nonlinear system subject to a random input. The experimental result shows that the proposed active suspension system more improves the vibration response of the vehicle body than a skyhook damper suspension system.

Systematic design method of stabilization fuzzy controllers for pendulum systems

A systematic method to construct stabilization fuzzy controllers for a single pendulum system and a series-type double pendulum system is presented based on the single input rule modules (SIRMs) dynamically connected fuzzy inference model. The angle and angular velocity of each pendulum and the position and velocity of the cart are selected as the input items. Each input item is given with a SIRM and a dynamic importance degree. All the SIRMs have the same rule setting. The dynamic importance degrees use the absolute value(s) of the angle(s) of the pendulum(s) as the antecedent variable(s). The dynamic importance degrees are designed such that the upper pendulum angular control takes the highest priority and the cart position control takes the lowest priority when the upper pendulum is not balanced upright. The control priority orders are automatically adjusted according to control situations. The simulation results show that the proposed fuzzy controllers have high generalization ability to completely stabilize a wide range of single pendulum systems and series-type double pendulum systems in short time. By extending the architecture, a stabilization fuzzy controller for a series-type triple pendulum system is even possible. © 2001 John Wiley & Sons, Inc.

Trajectory Tracking Control of Unconstrained Object Using the SIRMs Dynamically Connected Fuzzy Inference Model

A trajectory tracking experiment system taking an unconstrained table-tennis ball as the control object is constructed, and a fuzzy controller based on the SIRMs dynamically connected fuzzy inference model is proposed. For each of the three input items of the fuzzy controller, a SIRM (Single Input Rule Module) is established and an importance degree is defined. Especially for the input item corresponding to ball velocity, its importance degree is tuned dynamically according to moving conditions. The summation of the products of the importance degree and the fuzzy inference result of the SIRMs is calculated to control the angles of a table, making the ball on the table move along a desired trajectory. A virtual spiral asymptotic trajectory is also introduced to give the object an adequate desired position at each sampling time. Tracking experiment results for three kinds of circles and one kind of ellipses show that in more than 80% of the experiments performed under the SIRMs dynamically connected fuzzy inference model, the maximum tracking error is smaller than 0.05m and the unevenness of the sampling steps necessary for each round is very small. Compared with conventional fuzzy controller, the SIRMs dynamically connected fuzzy inference model is proved to be effective in tracking control of unconstrained objects.

Stabilization fuzzy control of inverted pendulum systems

A new fuzzy controller for stabilization control of inverted pendulum systems is presented based on the Single Input Rule Modules (SIRMs) dynamically connected fuzzy inference model. The fuzzy controller has four input items, each with a SIRM and a dynamic importance degree. The SIRMs and the dynamic importance degrees are designed such that pendulum angular control has priority over cart position control. It is made clear that the fuzzy controller performs the pendulum angular control and the cart position control in parallel, and switching between the two controls is realized by automatically tuning the dynamic importance degrees according to control situations. The simulation results show that the proposed fuzzy controller has a high generalization ability to stabilize completely a wide range of the inverted pendulum systems within 9.0 s for an initial angle up to 30.0°.

615 1車輪モデルに対する単一入力ルール群結合型ファジィ推論を用いた空気圧アクティブサスペンションの設計(O.S.4-4 振動制御)(O.S.4 機械の制御)

615 1車輪モデルに対する単一入力ルール群結合型ファジィ推論を用いた空気圧アクティブサスペンションの設計(O.S.4-4 振動制御)(O.S.4 機械の制御)

Construction of an active suspension system of a quarter car model using fuzzy reasoning based on single input rule modules

This paper presents the construction of an active suspension system for a quarter car model using fuzzy reasoning. The active control is obtained as a weighted sum of the defuzzificated values of the outputs in single input rule modules, and is generated by using a pneumatic actuator. The experimental result indicates that the proposed active control is more effective in the vibration isolation of the vehicle body than the skyhook damper and passive controls.

907 単一入力ルール群結合型ファジィ推論による1車輪モデルに対するアクティブサスペンションの設計に関する研究(機械力学・計測制御VIII)

907 単一入力ルール群結合型ファジィ推論による1車輪モデルに対するアクティブサスペンションの設計に関する研究(機械力学・計測制御VIII)

SIRMs Connected Fuzzy Inference Model Applied to Process Control -- Automatic Tuning Using a Genetic Algorithm Carla Cavalcante

A tuning process proposed for the single input rule modules (SIRMs) connected inference model is applied to process control. Using a genetic algorithm (GA), the importance degree is adjusted to improve process response. Simulation using first-order processes with dead time verified the validity of this approach.

SIRMs (Single Input Rule Modules) Connected Fuzzy Inference Model

A new fuzzy inference model, SIRMs (Single Input Rule Modules) Connected Fuzzy Inference Model, is proposed for plural input fuzzy control. For each input item, an importance degree is defined and single input fuzzy rule module is constructed. The importance degrees control the roles of the input items in systems. The model output is obtained by the summation of the products of the importance degree and the fuzzy inference result of each SIRM. The proposed model needs both very few rules and parameters, and the rules can be designed much easier. The new model is first applied to typical secondorder lag systems. The simulation results show that the proposed model can largely improve the control performance compared with that of the conventional fuzzy inference model. The tuning algorithm is then given based on the gradient descent method and used to adjust the parameters of the proposed model for identifying 4-input 1-output nonlinear functions. The identification results indicate that the proposed model also has the ability to identify nonlinear systems.

Hybrid Connectionist Fuzzy Production System: Towards Building Comprehensive AI

ABSTRACTThe article presents major principles of building complex hybrid systems for knowledge engineering. The systems make use of neural networks (for low level processing), fuzzy systems (for intermediate level processing), and symbolic AI systems (for higher level processing). Knowledge acquisition and rules extraction is considered an important part of the whole system. An experimental environment, Fuzzy COPE, which facilitates building comprehensive AI systems, is described. It consists of data a analysis module, a neural network module, a fuzzy inference module, a production rules module, and a fuzzy rules extraction module. Such an environment makes possible using all of the three paradigms, i.e. fuzzy rules, neural networks and symbolic production rules, as well as other paradigms of soft computing, in one system. A methodology for building comprehensive AI systems is described. The use of Fuzzy COPE for building hybrid systems is illustrated on a test problem for decision making in stock trading...

RGEN: A method for computer-aided design of rule-based systems for plant process diagnosis

A method is intended for designing rule-based systems for plant processes diagnosis. The method features universality and orientation to objective (non-heuristic) sources of diagnostic knowledge such as process simulation of dynamic systems, thermal hydraulic calculations, and engineering specifications. The method dwells upon original algorithms using learning on examples from artificial intelligence for automatic generation of modules of rules employing temporal logic and flexible certainty factor mechanisms. The diagnostic rules can analyze mutual behavior of process parameter trends in time, thus enabling one to distinguish among numerous failures (both single and multiple), to carry out diagnosis in all operational modes, to diagnose failures in the course of their propagation, and to maintain operation in noisy environments. Apart from the fundamentals of the method, this paper gives a brief description of the computer-aided technology of diagnostic system design. Experimental data are presented as well.

Objet-Sexi: Objects, Rules and Fuzzy Functions for Identification

The main aspects of an Object-Oriented knowledge-based system for process identification are presented. Its knowledge base relies on a mixed structure, implemented within an object-oriented representation as well as modules of production rules. Objects allow an easy implementation, updating and extension of the structured theoretical knowledge about models, transfer functions, polynomials, etc. Relatively small rule bases are attached to the slots of the objects and include the empirical knowledge about process identification. Fuzzy compatibility functions are used for the numerical-symbolic conversion of values supplied by the computations of the parameter estimation module. The search strategy for an “optimal” model structure has been implemented within a dedicated meta knowledge base.

避航エキスパートシステムと航路航行シミュレーションに関する研究

In the present report an expert system for ship collision avoidance is developed. The expert system has a knowledge base of hierarchical structure. The knowledge base consists of many rule modules and the rule modules are classified into several levels. Combining with the knowledge base with the subsystems for the data acquisition on traffic situations and for the guidance controls of ship position, the developed system can lead a ship safely to her destination even under heavy traffic conditions. If ships on traffic lanes are conducted by the collision avoidance expert system, marine traffic simulations can be carried out as an application of the expert system. Examples of such simulations show successfully that the system has an excellent ability of collision avoidance and also that the traffic simulation thus obtained is the powerful tool for investigations on marine traffic problems.

柔軟な推論機能と知識編集支援機能を備えた原子力機器点検保守支援システムの開発

For the reliable and efficient diagnosis and inspection work of the nuclear power plant equipments, “Diagnosis and Maintenance Support System” has been developed. This system has functions to assist operators or engineers to observe and evaluate equipment conditions based on the experts' knowledge. These functions are carried out through dialogue between the system and users.This system has two subsystems: diagnosis subsystem and knowledge base edition support subsystem.To achieve the functions of diagnosis subsystem, a new method of knowledge processing for equipment diagnosis is adopted. This method is based on the concept of “Cause Generation and Checking”. Knowledge for diagnosis is represented with modularized production rules. And each rule module consists of four different type rules with hierarchical structure. With this approach, the system is equipped with sufficient performance not only in diagnosis function but also in flexible man-machine interface.Knowledge base edition support subsystem (Graphical Rule Editor) is provided for this system. This editor has functions to display and edit the contents of knowledge base with tree structures through the graphic display. With these functions, the efficiency of constructing expert system is highly improved.By applying this system to the maintenance support of neutron monitoring system, it is proved that this system has satisfactory performance as a diagnosis and maintenance support system.