1、自动化专业外文翻译alicia3爬壁机器人的粘着控制本科论文英语原文：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 ado
2、pted in a variety ofapplications 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 t
3、he system has been selected. 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
4、passing under the cup and 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 Intro
5、duction Climbing robots can 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 an
6、umber of NDI over the wall 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
7、 water pollution) at different 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 veryexpensiv
8、e because scaffolding is 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
9、human operators are conducting 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
10、 mainlycomposed by a cup, 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 va
11、cuum insidethe cup and at 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
12、aluminums disc have a diameter 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 robo
13、t passing over small obstacles (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 sealin
14、g, so the whole robot can 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 r
15、otational joint. By using 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
16、 cups while the third, any 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 ca
17、n cause the internal negative 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. Thi
18、s problem can be solved by 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 electri
19、cal and the mechanical subsystem 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 v
20、ariable acquisition scheme 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 purpos
21、es: to compute asuitable 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
22、by an AC motor, apower interface 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 acquir
23、ed with oneanalog input of 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 S
24、RAM. Typical Input/Output 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
25、) = f(u(k), u(k 1), . . . ; 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 r
26、eal input measurement only (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
27、 these aremean error, quadratic 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
28、(3). y(t) = f(u(t), y(t 1) (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.
29、The structure of theNeuro-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
30、 the standard LevenbergMarquardt. 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 reporte
31、d. From a comparison between 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
32、Algorithm Once a system model 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 state error allowedwas fixed
