【正文】 v=? ?w,v T is the vector of velocities, v and w are the linear and angular velocities respectively。從這個(gè)表中我們同樣選擇了不同的速度和位置參數(shù) 表 2 不同模糊控制器實(shí)驗(yàn)仿真 追蹤控制器是將單輪移動(dòng)遙控裝置的模糊邏輯控制器與可測定點(diǎn)的穩(wěn)定性和速度軌跡的動(dòng)力學(xué)整合起來的。在圖 5到圖 8中，我們體現(xiàn)了對于情況 1的模擬結(jié)果。 ycos? xsin? =0 (2) 移動(dòng)遙控裝置式的追蹤控制器構(gòu)造如下：一條特定的預(yù)想軌跡 q和移動(dòng)遙控裝置的方向，我們必須設(shè)計(jì)出一個(gè)控制器使其適用于合適 的扭矩諸如測定的位置達(dá)到參考位置（由 3式表示）。第五部分做出了結(jié)論。 然而上述提到的發(fā)表中大多數(shù)都集中在移動(dòng)式遙控裝置的運(yùn)動(dòng)模塊，即這些模塊是受速度控制的。用計(jì)算機(jī)模擬來確定追蹤控制器的工作情況和它對不同航向的實(shí)際用途。 關(guān)鍵詞：智能控制、 2型模糊理論、移動(dòng)式遙控裝置 I. 介紹 由于受運(yùn)動(dòng)學(xué)強(qiáng)制約束，移動(dòng)遙控裝置是非完整的系統(tǒng)。而很少有發(fā)表關(guān)注到不完整的動(dòng)力系統(tǒng)，即受力和扭矩控制的模塊：布洛克。 II. 疑難問題陳述 A移動(dòng)控制裝置 這個(gè)被看作單輪移動(dòng)控制器的模型（見圖 1），它是由兩個(gè)同軸驅(qū)動(dòng)輪和一個(gè)自由前輪組成。 0)(lim ???? tqq dt （ 3） 為了達(dá)到控制目標(biāo)，我們基于 5的步驟，我們得到 τ(t) 利用模糊邏輯控制器（ FLC）控制著輪系 ()。位置和方向錯(cuò)誤分別見圖 5和圖 6，錯(cuò)誤可近似于零。計(jì)算機(jī)模擬結(jié)果確定了這臺控制器可以實(shí)現(xiàn)我們的目標(biāo)。 rR?? is the input vector,M(q)?Rnxn is a symmetric and positivedefinite inertia matrix, V(q,q)?Rnxn is the centripetal and Coriolis matrix,G(q)?Rn is the gravitational vector. Equation () represents the kinematics or steering system of a mobile robot. Notice that the noslip condition imposed a nonholonomic constraint described by (2), that it means that the mobile robot can only move in the direction normal to the axis of the driving wheels. ycos? xsin? =0 (2) B. Tracking Controller of Mobile Robot Our control objective is established as follows: Given a desired trajectory qd(t) and orientation of mobile robot we must design a controller that apply adequate torque τ such that the measured positions q(t) achieve the desired reference qd(t) represented as (3): 0)(lim ???? tqq dt （ 3） To reach the control objective, we are based in the procedure of [5], we deriving a τ(t) of a specific vc(t) that controls the steering system () using a Fuzzy Logic Controller (FLC). A general structure of tracking control system is presented in the Fig. 2. III. CONTROL OF THE KINEMATIC MODEL We are based on the procedure proposed by Kanayama et al. [10] and Nelson et al. [15] to solve the tracking problem for the kinematic model, this is denoted as vc(t). Suppose the desired trajectory qd satisfies (4): qd =0sincosdd??100ddwv (4) Using the robot local frame (the moving coordinate system xy in figure 1), the error coordinates can be defined as (5): e=Te (qd q),?eeeyx =1000cossin0sincos????? =?????dddyyxx (5) And the auxiliary velocity control input that achieves tracking for () is given by (6): vc =fc (e,vd ),ccwv =?? ekvekww ekevdyddxd s inc os321?? ?? (6) Where k1, k2 and k3 are positive constants. IV. FUZZY LOGIC CONTROLLER The purpose of the Fuzzy Logic Controller (FLC) is to find a control input τ such that the current velocity vector v to reach the velocity vector vc this is denoted as (7): 0vlim ???? vdt （ 7） As is shown in Fig. 2, basically the FLC have 2 inputs variables corresponding the velocity errors obtained of (7) (denoted as ev and ew: linear and angular velocity errors respectively), and 2 outputs variables, the driving and rotational input torques τ (denoted by F and N respectively). The membership functions (MF)[9] are defined by 1 triangular and 2 trapezoidal functions for each variable involved due to the fact are easy to implement putationally. Fig. 3 and Fig. 4 depicts the MFs in which N, C, P represent the fuzzy sets [9] (Negative, Zero and Positive respectively) associated to each input and output variable, where the universe of discourse is normalized into [1,1] range. Fig. 2. Tracking control structure Fig. 3. Membership function of the input variables ev and ew Fig. 4. Membership functions of the output variables F and N. The rule set of FLC contain 9 rules which governing the inputoutput relationship of the FLC and this adopts the Mamdanistyle inference engine [16], and we use the center of gravity method to realize defuzzification procedure. In Table I, we present the rule set whose format is established as follows: Rule i: If ev is G1 and ew is G2 then F is G3 and N is G4 Where G1..G4 are the fuzzy set associated to