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1、第11页 英语原文：Adhesion Control for the Alicia3 Climbing RobotD. Longo and G. MuscatoDipartimento di Ingegneria Elettrica Elettronica e dei Sistemi, Universita degliStudi di Catania, viale A. Doria 6, 95125 Catania ItalyAbstract. Climbing robots are useful devices that can be adopted in a variety ofappli
2、cations like maintenance, building, inspection and safety in the process andconstruction industries. The main target of the Alicia3 robot is to inspect non porous vertical wall with any regard for the material of the wall. To meet this target, a pneumatic-like adhesion for the system has been select
3、ed. Also the system can move over the surface with a suitable velocity by means of two DC motors and overcomesome obstacle thanks to a special cup sealing. This adhesion technology requires a suitable controller to improve system reliability. This is because small obstacles passing under the cup and
4、 wall irregularitycan vary the value of the internal pressure of the cup putting the robot in some anomalous working conditions. The methodologies used for deriving an accuratesystem model and controller will be explained and some result will be presented inthis work.1 Introduction Climbing robots c
5、an be used to inspect vertical walls to search for potential damage or problems on external or internal surface of aboveground/underground etrochemical storage tanks, concrete walls and metallic structures14. By using this system as carrier, it will be possible to conduct anumber of NDI over the wal
6、l by carrying suitable instrumentation 5, 6.The main application of the proposed system is the automatic inspectionof the external surface of aboveground petrochemical storage tanks where it is very important to perform periodic inspections (rate of corrosion, risk of air or water pollution) at diff
7、erent rates, as standardized by the AmericanPetroleum Institute 7. The system can be also adopted to inspect concrete dams. While these kinds of inspections are important to prevent ecologicaldisasters and risks for the people working around the plant, these are veryexpensive because scaffolding is
8、often required and can be very dangerous Fig. 1. Typical operating environment and the Alicia3 robotfor technicians that have to perform these inspections. Moreover, for safetyreasons, plant operations must be stopped and the tank must be emptied,cleaned and ventilated when human operators are condu
9、cting inspections. InFig. 1(a) and 1(b) typical environments for climbing robots are shown. Figure1c shows the Alicia3 robot prototype while attached to a concrete wall duringa system test.2 System Description The Alicia II system (the basic module for the Alicia3 system) is mainlycomposed by a cup,
10、 an aspirator, two actuated wheels that use two DC motorswith encoders and gearboxes and four passive steel balls with clearance toguarantee plain contact of the cup to the wall. The cup can slide over a wallby means of a special sealing that allows maintaining a suitable vacuum insidethe cup and at
11、 the same time creating the right amount of friction with respectsystem weight and a range of a target wall kind. The structure of the Alicia II module, shown in Fig. 2, currently comprisesthree concentric PVC rings held together by an aluminums disc. The biggerring and the aluminums disc have a dia
12、meter of 30 cm. The sealing system isallocated in the first two external rings. Both the two rings and the sealing are Fig. 2. Structure of the Alicia II moduledesigned to be easily replaceable, as they wear off while the robot is running.Moreover the sealing allows the robot passing over small obst
13、acles (about 1 cmheight) like screws or welding traces. The third ring (the smallest one) is usedas a base for a cylinder in which a centrifugal air aspirator and its electricalmotor are mounted. The aspirator is used to depressurize the cup formed bythe rings and the sealing, so the whole robot can
14、 adhere to the wall like astandard suction cup. The motor/aspirator set is very robust and is capable of working in harshenvironments. The total weight of the module is 4 Kg. The Alicia3 robot is made with the three modules linked together by meansof two rods and a special rotational joint. By using
15、 two pneumatic pistons it ispossible to rise and to lower each module to overcome obstacles. Each modulecan be raised 15 cm with respect to the wall, so obstacles that are 1012 cmheight, can be easily overcame. The system is designed to be able to stayattached using only two cups while the third, an
16、y of the three, is raised up.The total weight of the system is about 20 Kg.3 Electro-Pneumatic System Model By using this kind of movement and sealing method, it is possible, due tounexpected small obstacles on the surface, to have some air leakage in thecup. This leakage can cause the internal nega
17、tive pressure to rise up and inthis situation the robot could fall down. On the other side if the internalpressure is too low (high p), a very big normal force is applied to thesystem. As a consequence, the friction can increase in such a way to notallow robot movements. This problem can be solved b
18、y introducing a controlloop to regulate the pressure inside the chamber to a suitable value to sustainthe system. The considered open loop system and the most easily accessiblesystem variables has been schematized in Fig. 3; in this scheme the first blockincludes the electrical and the mechanical su
19、bsystem and the second blockincludes the pneumatic subsystem. The used variables are the Motor voltagereference (the input signal that fixes the motor power) and the Vacuum level(the negative pressure inside the chamber). Fig. 3. The open loop system considered Fig. 4. I/O variable acquisition schem
20、e Since it is very difficult to have a reliable analytical model of that system,because of the big number of parameters involved, it has been decided toidentify a black box dynamic model of the system by using input/outputmeasurements. This model was designed with two purposes: to compute asuitable
21、control strategy and to implement a simulator for tuning the controlparameters. An experimental setup was realized, as represented in Fig. 4, by usingthe DS1102 DSP board from Dspace in order to generate and acquire theinput/output variables. Since the aspirator is actuated by an AC motor, apower in
22、terface has been realized in order to translate in power the referencesignal for the motor coming from a DAC channel of the DS1102 board. Theoutput system variable has been measured with a piezoresistive pressuresensor with a suitable electronic conditioning block and acquired with oneanalog input o
23、f the DS1102. The software running on the DSpace DSP board,in this first phase simply generates an exciting motor voltage reference signal(pseudo random, ramp or step signals) and acquires the two analog inputswith a sampling time of 0.1 s, storing the data in its internal SRAM. Typical Input/Output
24、 measurements are represented in Fig. 5 and Fig. 6.In order to obtain better results in system modeling, the relationship betweenInput and Output needs to be considered as non-linear. A NARX model hasbeen used is in the form of (1), where f is a non linear function 8, 9. y(k) = f(u(k), u(k 1), . . .
25、 ; y(k 1), y(k 2), . . .) (1)To implement this kind of non-linearity, some trials have been done usingNeuro-Fuzzy and Artificial Neural Network (ANN) methodologies. Once thatmodel has been trained to a suitable mean square error, it has been simulatedgiving it as input the real input measurement onl
26、y (infinite step predictor) 8.So (1) can be modified in order to obtain (2). y(k) = f(u(k), u(k 1), . . . ; y(k 1), y(k 2), . . .) (2)In (2), 4y is the estimated system output. In order to compare the simulationresults, a number of descriptor has been defined and used. Among these aremean error, qua
27、dratic mean error and some correlation indexes. A first setof simulation for both methodologies has been done to find out the best I/Oregression terms choice.3.1 Neuro-Fuzzy IdentificationUsing this kind of methodology, the best model structure was found to be inthe form of (3). y(t) = f(u(t), y(t 1
28、) (3)Once the best model structure has been found, some trials have beenperformed modifying the number of membership functions. The best results,comparing the indexes described above, have been obtained with 3 functionsand in Fig. 7 the simulation results has been reported. The structure of theNeuro
29、-Fuzzy model is the ANFIS-Sugeno 10.3.2 ANN Identification Using this kind of methodology, the best model structure was found to be inthe form of (4). y(t) = f(u(t), u(t 1), u(t 2), y(t 1), y(t 2) (4) A single layer perceptron network has been used. The training algorithm is the standard LevenbergMa
30、rquardt. Once the best model structure has been found, some trials have been performedmodifying the number of hidden neurons. The best results, comparingthe indexes described above, have been obtained with 7 hidden neurons andin Fig. 8 the simulation results has been reported. From a comparison betw
31、een the two models and their related indexes, itcan be seen that the Neuro-Fuzzy model has best approximation performanceand use less input information. In the next section, this model will be used assystem emulator to tune and test the required regulator.4 Pressure Control Algorithm Once a system m
32、odel has been obtained, a closed loop configuration like thatin Fig. 9, has been considered. The target of the control algorithm is to regulate the internal vacuum levelto a suitable value (from some trials, it was fixed to about 10 kPa) to sustainthe whole system and its payload; the maximum steady
33、 state error allowedwas fixed to less than 200Pa. Moreover the time constant of the real system(about 10 s) has to be considered. A first simulation trial has been done witha fuzzy controller while during a second trial a PID controller has been tunedover the system emulator to meet the controller t
34、arget. All these simulationshave been performed by using Simulink from Mathworks.4.1 Fuzzy Controller During this simulation, a fuzzy controller that uses as input only the systemerror has been used. This controller has three membership function (triangularand trapezoidal) and three output crisp mem
35、bership functions. The reference was set to 10 kPa and the noise signal on the pressure levelis a series of steps. In Fig. 10 a plot of the noise, reference and closed looppressure signal is represented11.4.2 PID ControllerA second simulation has been done tuning a PID controller over the Neuro-Fuzz
36、y system emulator. As the system model is non-linear, trial and errortechnique has been used. The controller has been tested in the same conditionof the fuzzy controller. From the Fig. 12 it is possible to see that now the closedloop system has little more overshooting (see Fig. 13 for detail) but t
37、he samesteady state error. It has to be noted that overshooting is higher that themaximum error allowed but is faster with respect the system time constant.5 ConclusionIn this work the Alicia3 climbing robot was presented. Due to its specialadhesion mechanism, a controller for the vacuum level insid
38、e the cup isrequired. First of all, a system emulator has been designed by using black boxidentification methodologies. Among all the performed trial, Artificial NeuralNetworks and Neuro-Fuzzy are the two best models found and the Neuro-Fuzzy one has been selected as system emulator. A set of indexe
39、s has beenintroduced in order to make a comparison and to select the best system model.Once a system emulator has been become available, some Simulink simulationshave been performed in order to tune a controller. In that case a Fuzzy anda PID controller have been compared. Between the two, the Fuzzy
40、 controllerworks better than the PID but this is much simpler in its implementation andits performances are not so worst; in any case, it is compatibles with systemdynamics.中文原文 Alicia3爬壁机器人的粘着控制摘要. 爬壁机器人用途广泛，可以在许多不同的环境中应用，如维修、建设、检查安全的过程和建筑业。Alicia3机器人主要目标是检查非多孔垂直墙，且不管墙的材料如何。为了达到这个目标，我们选定气动粘连系统。该系统可
41、以通过两个直流电动使机器人以一个合适的速度在墙面移动并且借助于特殊的密封环越过障碍。 这种粘接技术需要一个合适的控制器，以改善系统可靠性。这是因为机器人穿过小的障碍时障碍物将使机器人密封环的内部压力发生较大的变化从而使得机器人无法正常工作。本文将致力于研究得到系统准确控制模型的方法并由此得到一些结论。1简介 爬壁机器人可以用来检查垂直墙壁，寻找潜在损坏或问题。如石油储藏罐，混凝土墙和金属结构的内外表面。通过使用此系统为载体外加适当的仪器，将可以在墙上进行一些NDI检测。主要应用系统的建议是自动检测外部表面地上石化储油罐的地方是非常重要的执行定期检查（率腐蚀，风险空气或水的污染）以不同的速度，其
42、标准美国石油学院提出。该系统还可以用于检查混凝土水坝。尽管这些种是重要的检查，但是进行这些检查是很危险的因为对技术员来说脚手架往往是非常危险的。此外，为了保证安全原因，工厂经营必须停止和油罐必须清空，清洁和通风都由操作员进行检查。其附着在混凝土墙中系统测试。2系统描述Alicia系统（基本模块Alicia3系统） ，主要是组成了密封杯的吸引，两个驱动车轮，使用两个直流电动机与编码器和变速箱和4个被动钢球与清除保证平原联系的世界杯在墙上。杯子可以在墙上投影片通过一个特殊的密封，可以保持一个合适的真空内杯子和在同一时间创造合适的数额方面的摩擦系统重量一系列目标墙实物。Alicia系统，目前有三个同
43、心环聚氯乙烯一并举行的铝业光盘。更大的环和铝业光盘有一个直径为30厘米。密封系统分配前两个外部环。两种环和密封旨在方便地更换，可以在机器人运行时更换。 此外，密封使小机器人越过障碍（约1厘米高度）像螺丝或焊接的痕迹。第三环（最小）是用来作为基地的缸中，离心空气吸引和其电气电机安装。在吸引用来减压的世界杯所形成的环和密封，使整个机器人能够坚持在墙上像标准吸盘。电动机/吸引一套非常强大的，并且能够在恶劣的工作环境。总重量模块4公斤。该Alicia3机器人是由三个模块连在一起的方法两棒和一种特殊旋转联合。通过使用两个气动活塞是可能上升，并降低每个模块克服障碍。每个模块可提高15厘米就在墙上可以很容易
44、克服。该系统的目的是能够留重视只用两杯第三上述三种是提高了。总重量的系统是20公斤左右。3电气动系统模型 通过使用这种运动和密封方法，它是可能的，因为表面上意想不到的小障碍，有一些在漏风密封杯。这可能会导致泄漏的内部负压变大和在这种情况下，机器人可以倒下。另一方面，如果内部压力过低（高p），一个非常大的正常力应用于系统。因此，摩擦可以增加这样一种方式，不让机器人的运动。这个问题可以得到解决的引入控制回路调节压力商会给一个合适的值，以维持该系统。考虑的开环系统和最容易进入系统变量已在图图式。在这个计划的第一个块包括电气和机械子系统和第二块包括气动子系统。用过的变数是汽车电压参考（输入信号，修正了
45、电机功率）和真空度（负压）。因为它是非常困难的有一个可靠的分析模型，该系统， 由于大量的参数参与，它已决定确定一个黑匣子动力学模型，利用该系统输入/输出测量。这一模式的目的是有两个目的：计算合适的控制策略和实施模拟器的调整控制参数。使用在DS1102的DSP来自Dspace ，以便产生和接受输入/输出变量。由于吸引的是驱动交流电动机，一个电源接口已经实现，以便将参考信号电机来自数模转换器通道DS1102局。那个输出系统变量测量了一个压阻式压力传感器与一个合适的电子块和后天条件之一模拟输入的DS1102 。该软件运于DSpace的DSP板， 在这第一阶段产生的只不过就是一个令人兴奋的电机电压参考
46、信号（伪随机，坡道或步骤信号）并获得了两个模拟输入与采样时间为0.1秒，将数据储存在其内部的SRAM 。 典型的输入/输出测量中的代表图。为了取得更好的成果，在系统建模之间的关系输入和输出的需要视为非线性。一个NARX模型使用的形式是（ 1 ） ，其中f是一个非线性函数： y(k) = f(u(k), u(k 1), . . . ; y(k 1), y(k 2), . . .) (1)实施这种非线性，一些试验已经完成使用模糊神经网络和人工神经网络（ ANN ）方法。一旦模型已培训，以一个合适的均方误差，它一直模拟给它的输入测量的实际投入只有（无限一步预测） 。 因此， (1)可以进行修改，以便
47、获得（2）。y(k) = f(u(k), u(k 1), . . . ; y(k 1), y(k 2), . . .) (2)在（ 2 ）式中 ， y为估计系统输出。为了比较仿真结果，一些广义的定义和使用。其中有平均误差，二次平均误差和一些相关指标。第一套模拟方法都做了，寻找最佳的I / O 回归条件的选择。3.1神经模糊识别 使用这种方法，得到最好的模型结构式（ 3 ）y(t) = f(u(t), y(t 1) (3)一旦最佳模型结构已经发现，一些试验已经完成修改一些隶属函数。最好的结果比较上述各项指标仿真结果的报告。的结构模糊神经网络模型是ANFIS的- Sugeno型。3.2人工神经网络识别使用这种方法，得到的最好的模型结构形式（ 4 ）。y(t) = f(u(t), u(t 1), u(t 2), y(t 1), y(t 2) (4)