Dynamic Importance Degree Research Articles

Mobile Robot Path Following Controller Based On the Sirms Dynamically Connected Fuzzy Inference Model

In this paper, the single input rule modules (SIRMs) dynamically connected fuzzy inference model is employed to design a path following controller for a unicycle wheeled mobile robot(WMR).The SIRMs model is mainly used to reduce the number of fuzzy inference rules and consequently reduce the processing time of the conventional Fuzzy Logic Control (FLC) schemes. The adopted path following strategy uses two control unites working in parallel to drive the WMR to follow a path composed of a succession of discrete waypoints. The first unit is a heading controller charged of generatingthe angular velocity command and thesecond one is a linear velocity controller charged of the generation the speed control signal.For the headingcontroller, all input variables are assigned with two fuzzy inference modules: a SIRM and a dynamic importancedegree (DID). The structure of the SIRMs based linear velocitycontrolunitwas modified to simplify the designand to fulfill the requirements of the path following strategy. It contains two SIRM modules andonecommonDID. To ensure the stability of the WMR'smotion, the SIRMs and the DIDs in the two control units were designedin such a manner that the control of the robot's orientationgets higher priority over the control of the WMRspeed. The use of the SIRM fuzzy inference model allows the online automatic adjustment of priority orders of thedifferent control actions. No optimizationof the controller parameters is required since thatall base values areset to 0 and all the breadthsare set to 1 for the DID modules. This means that all the input items play equal rolesin the control of the WMR.The structure of the proposedpath following controller is simple and the inferencerules are intuitively understandable because they are inspired from the knowledge of a human expert. Numericalsimulations conducted in the MATLABenvironment show that the proposed SIRMs based controller outperforms apath followingcontroller that uses the conventional FLC scheme.

Social network group decision making: Managing self-confidence-based consensus model with the dynamic importance degree of experts and trust-based feedback mechanism

Due to the development of intelligent decision-making, social network group decision-making (SNGDM) has become increasingly valued. Generally, real SNGDM cases involve not only the mathematical formulation of the social network analysis but also the experts’ psychological behaviors. Self-confidence, an expert's psychological implication of self-statement, is a significant topic in SNGDM problems, while it is overlooked in most existing research. To address this issue, this study takes experts’ self-confidence into account in SNGDM. All experts use self-confident fuzzy preference relations (SC-FPRs) to express their opinions. Subsequently, we have developed a novel self-confidence-based consensus approach for SNGDM with SC-FPRs. A dynamic importance degree of experts which combines the external trust and internal self-confidence is proposed to determine their weights. A consensus index considering self-confidence is defined to assess the consensus level among experts. Meanwhile, a trust-based feedback mechanism is presented to improve the consensus efficiency. The rule of the feedback mechanism is that experts dynamically adjust their self-confidence levels while revising the preferences. Using a self-confidence score function, an alternative that has the highest self-confidence score can be selected as the best solution. An illustrative example and some comparisons are given to verify the feasibility and effectiveness of the proposed method.

Energy-aware scheduling for information fusion in wireless sensor network surveillance

Effective energy control while maintaining reliable monitoring performance becomes a key issue in wireless sensor networks (WSNs) based surveillance applications. While importance difference of surveillance zone, limited energy and dynamic network topology pose great challenges to surveillance performance. It is necessary to adjust sensor nodes' awakening frequency dynamically for information fusion. Thus an energy-aware scheduling with quality guarantee method named ESQG is proposed in this paper which considers sensor nodes' residual energy, different importance degrees of the surveillance zone and network topology comprehensively. It first uses a Voronoi diagram to determine the effective scope of each sensor node and then calculates node importance according to its residual energy and the importance degree of the effective scope. Then ESQG utilizes the importance of individual sensing scope and current forwarding costs to further compute node importance and awakening frequency for information fusion. In this way, ESQG can dynamically adapts each nodes awakening frequency to its dynamic network topology and importance degree of each individual sensing scope. The nodes are then turned on stochasticlly via the node awakening probability and node importance based information fusion is conducted for target detection. Besides, an adaptive process of perception factor C is proposed to match actual situation, and automatically change according to the detected data. Experiments results demonstrate that the proposed method ESQG can reduce the number of awakening nodes to a large extent while maintaining high reliability via information fusion.

Anti-swing and positioning control of overhead traveling crane

A new fuzzy controller for anti-swing and position control of an overhead traveling crane is proposed based on the Single Input Rule Modules (SIRMs) dynamically connected fuzzy inference model. The trolley position and velocity, the rope swing angle and angular velocity are selected as the input items, and the trolley acceleration as the output item. Each input item is given with a SIRM and a dynamic importance degree. The control system is proved to be asymptotically stable to the destination. The controller is robust to different rope lengths and has generalization ability for different initial positions. Control simulation results show that by using the fuzzy controller, the crane is smoothly driven to the destination in short time with small swing angle and almost no overshoot.

A new fuzzy controller for stabilization of parallel-type double inverted pendulum system

A new fuzzy controller with 6 input items and 1 output item for stabilizing a parallel-type double inverted pendulum system is presented based on the single input rule modules (SIRMs) dynamically connected fuzzy inference model. Each input item is assigned with a SIRM and a dynamic importance degree. The SIRMs and the dynamic importance degrees are designed such that the angular control of the longer pendulum takes the highest priority over the angular control of the shorter pendulum and the position control of the cart when the angle of the longer pendulum is big. By using the SIRMs and the dynamic importance degrees, the priority orders of the three controls are automatically adjusted according to control situations. The proposed fuzzy controller has a simple and intuitively understandable structure, and executes the three controls entirely in parallel. Simulation results show that the proposed fuzzy controller can stabilize completely a parallel-type double inverted pendulum system within 10.0 s for a wide range of the initial angles of the two pendulums. This is the first result for a fuzzy controller to achieve successfully complete stabilization control of a parallel-type double inverted pendulum system.

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].

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.

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°.

動的重視度を用いたSIRMsファジィ推論モデル

複数入力カフアジィ制御のための「SIRMs動的重視度結合型ファジィ推論モデル」を提案する。本モデルでは各入力項目に対してそれぞれ、対応する入力項目のみを前件部変数とする単一入力ルール群(SIRM、Single Input Rule Module)を設定し、動的重視度を導入する。動的重視度は、入力項目の定常状態時の機能を保証する基本値と、制御状況に応じてダイナミックに変化する変動値の和として定義する。そして各ルール群の推論結果の動的重視度付き総和をモデルの出力とする。定値制御系に対して、偏差と偏差の1階差分などを入力項目に、操作量の変化分を出力項目にして、本提案モデルの具体的な構築手法を示し、動的重視度が対応する入力項目のローカル情報に基づいてファジィ推論で簡単に求められることを明らかにする。また、提案手法を1次遅れ+むだ時間系および2次遅れ+むだ時間系に適用し、「単一人力ルール群結合型ファジィ推論モデル」の場合に比べてオーバーシュートなしで目標到達時間がさらに10%以上短縮できることを示す。

1次遅れ系に適用したSIRMs動的重視度 : 結合型ファジィ推論モデルにおける動的重視度のパラメータ設定手法

SIRMs動的重視度結合型ファジィ推論モデルは、通常のSIRMsファジィ推論モデルに比べて1次遅れ系などの制御性能を大幅に改善できるが、動的重視度を設定する系統的な方法が確立されていない。本論文では、制御対象を1次遅れ系に限定して、偏差と偏差の1階差分を入力項目として操作量の変化分を出力項目とするファジィ制御器を構築する。このファジィ制御器が項目ごとには従来の線形PI制御則に完全に対応し、全体では非線形PI制御則であることを証明する。時定数が0.20から30.00までのプラントに対して、ランダム最適探索法で収集したデータに基づき、各入力項目の動的重視度のパラメータである基本値と変動幅の設定式を導出する。本設定式を用いることによって、振動と定常誤差が生じずに、オーバーシュートとアンダーシュートがともに1.0%程度に抑えられ、しかも制御量が目標値に速やかに到達できることを示す。