【導讀】鐵路牽引接觸網(wǎng)使用單項工頻交流電，為了提高電網(wǎng)利用率，促進電力系統(tǒng)三相負荷平衡，本畢業(yè)設計希望通過對機車防帶電過分相系統(tǒng)研究培養(yǎng)學生綜合運用所學基礎理論和專。綜述機車過分相方法，設計并論證系統(tǒng)設計方案；對機車安全信息綜合監(jiān)測裝置數(shù)據(jù)幀的解析；完成分相點位置計算；要求認真獨立完成所規(guī)定內容，所設計的內容要求正確、合理；按照學校畢業(yè)論文要求撰寫論文，按規(guī)定格式打印論文。[摘要]鐵路運輸是國家重要交通運輸方式、也是目前最大眾化交通方式。加快鐵路發(fā)展，對促進社會經(jīng)。經(jīng)過多年的努力，我國鐵路事業(yè)已取得了非常大的進步，但是目前鐵路運輸能力與運。輸需求的矛盾依然十分突出，成為社會經(jīng)濟進一步發(fā)展的制約。重載和提速是目前我國鐵路發(fā)展的重要方向，對。機車時速的大規(guī)模提升，對機車的自動化運行提出了更高的要求。因此，研究一種成本相對較低、可靠性高、維。護方便的機車自動過分相裝置對于目前鐵路運輸技術的發(fā)展具有重要意義。

　　

【正文】 nonholonomic systems have given rise to a lot of work for the past 8 years. Brockett’s condition [2] made stabilization about a given configuration a challenging task for such systems, proving that it could not be performed by a simple continuous state feedback. Alternative solutions as timevarying feedback [l0, 4, 11, 13, 14, 15, 18] or discontinuous feedback [3] have been then proposed. See [5] for a survey in mobile robot motion control. On the other hand, tracking a trajectory for a nonholonomic system does not meet Brockett’s condition and thus it is an easier task. A lot of work have also addressed this problem [6, 7, 8, 12, 16] for the particular case of mobile robots.All these control laws work under the same assumption: the evolution of the system is exactly known and no perturbation makes the system deviate from its papers dealing with mobile robots control take into account perturbations in the kinematics equations. [l] however proposed a method to stabilize a car about a configuration, robust to control vector fields perturbations, and based on iterative trajectory tracking.The presence of obstacle makes the task of reaching a configuration even more difficult and require 陜西理工學院畢業(yè)設計 第 28 頁 共 63 頁 a path planning task before executing any motion. In this paper, we propose a robust scheme based on iterative trajectory tracking, to lead a robot towing a trailer to a configuration. The trajectories are puted by a motion planner described in [17] and thus avoid obstacles that are given in input. In the won’t give any development about this planner,we refer to this reference for details. Moreover,we assume that the execution of a given trajectory is submitted to perturbations. The model we chose for these perturbations is very simple and very presents some mon points with [l]. The paper is anized as follows. Section 2 describes our experimental system Hilare and its trailer:two hooking systems will be considered (Figure 1).Section 3 deals with the control scheme and the analysis of stability and robustness. In Section 4, we present experimental results. 2 Description of the system Hilare is a two driving wheel mobile robot. A trailer is hitched on this robot, defining two different systems depending on the hooking device: on system A, the trailer is hitched above the wheel axis of the robot (Figure 1, top), whereas on system B, it is hitched behind this axis (Figure l , bottom). A is the particular case of B, for which rl = 0. This system is however singular from a control point of view and requires more plex putations. For this reason, we deal separately with both hooking systems. Two motors enable to control the linear and angular velocities （ vr ， r? ) of the robot. These velocities are moreover measured by odometric sensors, whereas the angle ? between the robot and the trailer is given by an optical encoder. The position and orientation（ xr , yr , r? ） of the robot are puted by integrating the former velocities. With these notations, the control system of B is: c ossi nsi n( ) c os( )r r rr r rrrr r rrttxvyvvlll?????? ? ? ????? ? ? ? （ 1） 陜西理工學院畢業(yè)設計 第 29 頁 共 63 頁 Figure 1: Hilare with its trailer 3 Global control scheme Motivation When considering real systems, one has to take into account perturbations during motion may have many origins as imperfection of the motors, slippage of the wheels, inertia effects ... These perturbations can be modeled by adding a term in the control system (l),leading to a new system of the form ( , )x f x u ??? where? may be either deterministic or a random the first case, the perturbation is only due to a bad knowledge of the system evolution, whereas in the second case, it es from a random behavior of the system. We will see later that this second model is a better fit for our experimental system. To steer a robot from a start configuration to a goal, many works consider that the perturbation is only the initial distance between the robot and the goal, but that the evolution of the system is perfectly known. To solve the problem, they design an input as a function of the state and time that makes the goal an asymptotically stable equilibrium of the closed loop system. Now, if we introduce the previously 陜西理工學院畢業(yè)設計 第 30 頁 共 63 頁 defined term ? in this closed loop system, we don39。t know what will happen. We can however conjecture that if the perturbation is small and deterministic, the equilibrium point (if there is still one) will be close to the goal, and if the perturbation is a random variable, the equilibrium point will bee an equilibrium we don39。t know anything about the position of these new equilibrium point or subset. Moreover, time varying methods are not convenient when dealing with obstacles. They can only be used in the neighborhood of the goal and this neighborhood has to be properly defined to ensure collisionfree trajectories of the closed loop system. Let us notice that discontinuous state feedback cannot be applied in the case of real robots, because discontinuity in the velocity leads to infinite accelerations. The method we propose to reach a given configuration tn the presence of obstacles is the following. We first build a collision free path between the current configuration and the goal using a collisionfreemotion planner described in [17], then we execute the trajectory with a simple tracking control law. At the end of the motion, the robot does never reach exactly the goal because of the various perturbations, but a neighborhood of this goal. If the reached configuration is too far from the goal, we pute another trajectory that we execute as we have done for the former one. We will now describe our trajectory tracking control law and then give robustness