Underwater Vehicles part 15 pot

Dynamic Modelling and Motion Control for Underwater Vehicles with Fins 549 4 3 2 1 3 2 1 0 2 1 0 -2 1 0 -1 -2 0 -1 -2 -3 Table 1. Control rules table Generally, the function of sigmoid curve is given by ( ) 0.10.10.2 −+= −kx ey (31) Then, the function of sigmoid curved surface is ( ) ( ) 0.10.10.2 21 −+= −− ykxk ez (32) Thus, the designed control model of S surface controller is () ( ) 0.10.10.2 21 −+= −− ekek eu  (33) where e and e  stand for the input information (error and the rate of error change, which are normalized), u is the control output which is the output force (normalized) in each freedom, and 1 k and 2 k are the control parameters corresponding to error and rate of error change respectively. In equation (33), there are only two control parameters ( 1 k and 2 k ) which S surface controller need to adjust. It is important to note that S surface controller can not get the best matching, whether adopting manual adjustment or adaptive adjustment. This is because that the adjustment is global and local adjustment is not available. Therefore, parameter adjustment is just the approximation of the system. After all, due to the complexity and uncertainty of control object, any kind of approach has big approximation. Thus, the optimal parameters 1 k and 2 k are different due to different velocities. Manual adjustment of control parameters can make the motion control of underwater vehicle meet the demand in most cases. Response is more sensitive to small deviation but vibrations easily occur when 1 k and 2 k are larger. Therefore, the initial values of 1 k and 2 k we choose are generally about 3.0. If the overshoot is large, we can reduce 1 k and increase 2 k simultaneously. By contrast, if the speed of convergence is slow, we can increase 1 k and reduce 2 k simultaneously. The ocean current and unknown disturbances can be considered as fixed disturbance force in a samlping period. Thus, we can eliminate the fixed deviation by adjusting the excursion of S surface and the function of control model is ( ) ( ) ueu ekek Δ0.10.10.2 21 +−+= −−  (34) where uΔ is the value(normalized) of fixed disturbance force which is obtained through adaptive manner. The adaptive manner is as follows: Underwater Vehicles 550 a. Check whether the velocity of the vehicle is smaller than a preset threshold. If it is, go to step b), if not, go to step c); b. Give the deviation value of this degree to a set array, at the same time, add 1 to the set counter, when the very counter reaches the predefined value, go to step d); c. Shift each element in the array to the left by one, and at the meantime, decrease the counter by 1, then go to step a); d. Weighted average the values of the array and the gained average deviation values are obtained. Then these deviation values are used to compute the side-play amount of control output, self-adapt the control output to eliminate fixed deviation, meanwhile, set the counter to zero, turn to the next loop. Thus, a simple and practical controller is constructed, which can meet the work requirement in complicated ocean environment. However, the parameter adjustment of S surface controller is completely by hand. We hope to adjust the parameters for the controller by itself online, so we will present the self-learning algorithm the idea borrowed from BP algorithm in neural networks. 3.2 Self-learning algorithm Generally, we define a suitable error function using neural networks for reference, so we can adjust the control parameters by BP algorithm on-line. As is known, an AUV has its own motion will, which is very important for self-learning and will be discussed in detail in the next section, so there is also an expected motion state. Namely, there is an expected control output for S surface controller. Therefore, the error function is given by 2 )( 2 1 uuE dp −= (35) where d u is the expected control output, and u is the last time output which can be obtained by eqution (34) . We can use gradient descent optimization method, i.e. use the gradient of E p to adjust k 1 and k 2 . i p i k E ηk ∂ ∂ −=Δ (36) where η is the learning ratio ( 10 < < η ). i ekek ekek d i d i p e e e uu k u uu k E 2 )1( 0.2 )()( 21 21   −− −− + ⋅−−= ∂ ∂ ⋅−−= ∂ ∂ (37) where 2,1=i ; ee = 1 ; ee  = 2 Therefore, 1 k and 2 k can be optimized by the following eqution. i ekek ekek diiii e e e uuηtkktktk ⋅ + ⋅−+=+=+ −− −− 2 )1( 2 )()(Δ)()1( 21 21   (38) We can get the expected speed by expected state programming. The expected control output can be obtained by the following principles. Dynamic Modelling and Motion Control for Underwater Vehicles with Fins 551 If the speed v is less than or equal to d v , then u is less than d u , and u needs to be magnified. In the contrast, u needs to be reduced. The expected control output is given by )( vvcuu dd − ⋅ + = (39) where c is a proper positive constant. Therefore, S surface controller has the ability of self- learning. 3.3 AUV motion will As an intelligent system, the AUV has motion will to some degree. It knows the expected speed and when and how to run and stop. The effect from environment changing is secondary, and it can overcome the distubance by itself. Certainly, the obility to overcome the distubance is not given by researchers, because they may not have the detailed knowledge of the changing of environment. Howerver, the AUV motion will can be given easily, because the artificial machine must reflect the human ideas. For example, when an AUV runs from the current state to the objective state, how to get the expected acceleration(motion will) can be considered synthetically by the power of thrusters, the working requirement and the energy consumption. However, the active compensation to various acting force (the reflective intelligence for achieving the motion will) will be obtained from self-learning. This is the path which we should follow for the AUV motion control (Peng, 1995). The purpose of motion control is to drive the error S and the error variance ratio V between the current state and and the objective state to be zero. The pre-programming of control output is given by ),(},,,,{ VSVa faaaaa θψzyx = = = (40) where the concrete form of )(⋅f can be given by synthetically consideration according to the drive ability of the power system. max Paa = (41) where max a is the AUV maximal acceleration, which lies on the drive ability of power system and the vehicle mass. P is given by ⎥ ⎥ ⎥ ⎥ ⎥ ⎥ ⎦ ⎤ ⎢ ⎢ ⎢ ⎢ ⎢ ⎢ ⎣ ⎡ = 5 4 3 2 1 0 0 p p p p p P (42) where ⎪ ⎪ ⎪ ⎩ ⎪ ⎪ ⎪ ⎨ ⎧ = = = = = )2/tanh( )2/tanh( )2/tanh( )2/tanh()/( )2/tanh()/( 5 4 3 2 1 θ ψ z xyxyy xyxyx pp pp pp pppp pppp (43) Underwater Vehicles 552 ⎪ ⎪ ⎪ ⎪ ⎩ ⎪ ⎪ ⎪ ⎪ ⎨ ⎧ += −= −= −= −= −= 22 * * * * * yxxy θθθθ ψψψψ zzzz yyyy xxxx ppp VcSp VcSp VcSp VcSp VcSp (44) where * x S , * y S , * z S , * ψ S , * θ S are difined as the traction distances in x , y , z , ψ , θ direction given by ⎪ ⎪ ⎩ ⎪ ⎪ ⎨ ⎧ −≤− −<<− ≥ = )( )( )( * max * max * max * max * max * max * iii iiii iii i SSS SSSS SSS S (45) where =ix, y , z , ψ , θ . * max i S and i c are undetermined coefficients, and * max i S are the predefined maximal distances which are determined based on the AUV’s ability. We hope that the maximal transfer speed maxi V 0 max * max =− iii VcS (46) As can be seen, we can not determine * max i S and i c by equation (46), so we define the other constraint equation shown in equation (47). () () ⎪ ⎪ ⎪ ⎩ ⎪ ⎪ ⎪ ⎨ ⎧ = ′ = > ⎟ ⎟ ⎟ ⎟ ⎠ ⎞ ⎜ ⎜ ⎜ ⎜ ⎝ ⎛ ⎟ ⎠ ⎞ ⎜ ⎝ ⎛ − ′ + −= ″ 0 max * max 0max t=t , , exp1 2 1 ii ii iii ii VS SS tt SSc aS (47) Therefore, to all 0 tt > , 0> i S , and get smallest possible 0 tt n > . To all n tt > , we can obtain ii εS < (48) where i ε is the state precision. The constraint condition is to reduce errors as well as drive overshoot to zero. 4. Experiments In this part, simulation and lake experiments have been conducted on WEILONG mini-AUV for many times to verify the feasibility and superiority of the mathmetical modelling and control method. The position errors of longitudinal control simulation are shown in Fig. 8. Reference inputs are 5m, the velocity of current is 0 m/s, and the voltage of thrusters is restricted by 2.5V. As can be seen, S surface control is feasible for the AUV motion control. For the figure on the left, 0.8 1 =k and 0.5 2 =k . Since the initial parameters are too big, there is certain overshoot and concussion aroud the object state in S surface control. However, the Dynamic Modelling and Motion Control for Underwater Vehicles with Fins 553 parameters are adjusted by self-learning in improved S surface control. The overshoot is reduced and the balance (? Do you mean steady state) is achieved rapidly. For the figure on the right, 0.3 1 =k and 0.5 2 =k . The initial parameters are too small, so the rate of convergence is too slow in S surface control. In improved S surface control, the rate of convergence is picked up and the performance is improved greatly. Field experiments are conducted in the lake. The experiments use the impoved S surface control and the results are shown in Fig. 9 and Fig. 10. As there exits various disturbance (such as wave and current), the result curves are not smooth enough. In yaw control experiment, the action of the disturbances is greater than the acting force, so we can see some concussions in Fig. 9. It needs to be explained in the depth control that there is no response at the beginning of the experiment. The reason is the velocity of WEILONG mini- AUV is very low and the fin effect is too small. In the computer simulation, we don’t use the fins until the velocity reaches certain value. a. k1=8.0, k2=5.0 -1 0 1 2 3 4 5 6 0 20406080100 t (0.25s) position error (m) S surface control improved S surface control b. k1=3.0, k2=5.0 -1 0 1 2 3 4 5 6 0 50 100 150 200 250 t (0.25s) position error (m) S surface control improved S surface control Fig. 8. Simulation results of longitudinal control 140 150 160 170 180 190 200 210 220 0 100 200 300 400 500 600 700 800 t (0.25s) yaw (degree) actual value desired value Fig. 9. Results of yaw control in lake experiments Underwater Vehicles 554 -0.2 0 0.2 0.4 0.6 0.8 1 1.2 0 50 100 150 200 250 t (0.25s) depth (m) actual value desired value Fig. 10. Results of depth control in lake experiments As can be seen, the control performance meets the requirement for the AUV motion control by using improved S surface control. It has high response speed and good robustness to various disturbances in field experiments. 5. Conclusion This chapter concentrates on the problem of modeling and motion control for the AUVs with fins. Firstly, we develop the motion equation in six-degree freedom and analyze the force and hydrodynamic coefficients, especilly the fin effect. The feasibility and accuracy are verified by comparing the results between at-sea experiments and simulation. The model is applicable to most AUVs. Secondly, we present a simple and practical control method—S surface control to achieve motion control for the AUVs with fins, and deduce the self- learning algorithm using BP algorithm of neural networks for reference. Finally, the experiment results verify the feasibility and the superiority of the mathmetical modelling and control method. 6. Acknowledgements The authors wish to thank all the researchers at the AUV Lab in Harbin Engineering University without whom it would have been impossible to write this chapter. Specifically, the authors would like to thank Professor Yuru Xu who is the subject leader of Naval Architecture and Ocean Engineering in Harbin Engineering University and has been elected as the member of Chinese Academy of Engineering since 2003. Moreover, the authors would like to thank Pang Shuo who is an assistant professor of Embry-Riddle Aeronautical University in USA. Dynamic Modelling and Motion Control for Underwater Vehicles with Fins 555 7. References Blidberg D.R. (1991). Autonomous underwater vehicles: a tool for the ocean, Unmanned Systems, Vol. 9, No. 2, 10-15, 1991. Xu Y.R.; Pang Y.J.; Gan Y. & Sun Y.S. (2006). AUV-state-of-the-art and prospect. CAAI Transactions on Intelligent Systems, Vol.1, No.1, 9-16, September 2006. Xu Y.R. & Xiao K. (2007). Technology development of autonomous ocean vehicle. Journal of Automation, Vol. 33, No. 5, 518-521, 2007. Conte G. & Serrani A. (1996). Modelling and simulation of underwater vehicles. Proceedings of the 1996 IEEE International Symposium on Computer-Aided Control System Design, pp. 62-67, Dearborn, Michigan, September 1996 Timothy P. (2001). Development of a Six-Degree of Freedom Simulation Model for the REMUS Autonomous Underwater Vehicle: Oceans. MTS/IEEE Conference and Exhibition, pp. 450-455, May 2001 Prestero T. J. (2001). Development of a six-degree of freedom simulation model for the remus autonomous underwater vehicle. Proceedings of the OCEANS 2001 MTS/IEEE Conference and Exhibition, pp. 450-455, Honolulu, Hawaii, November 2001 Ridley P.; Fontan J. & Corke P. (2003). Submarine dynamic modeling. Proceedings of the Australian Conference on Robotics and Automation, Brisbane, Australia, December 2003 Chang W.J.; Liu J.C. & Yu H.N. (2002). Mathematic model of the AUV motion control and simulator. Ship Engineering, y, Vol.12, No.3, 58-60, September 2002. Li Y.; Liu J.C. & Shen M.X.(2005). Dynamics model of underwater robot motion control in 6 degrees of freedom. Journal of Harbin Institute of Technology, Vol.12, No.4, 456-459, December 2005. Nahon M. (2006). A Simplified Dynamics Model for Autonomous Underwater Vehicles. Journal of Ocean Technology, Vol. 1, No. 1, pp. 57-68, 2006 Silva J.; Terra B.; Martins R. & Sousa J. (2007). Modeling and Simulation of the LAUV Autonomous Underwater Vehicle. Proceedings of the 13th IEEE IFAC International Conference on Methods and Models in Automation and Robotics, pp. 713-718, Szczecin, Poland, August 2007 Su Y.M.; Wan L. & Li Y. (2007). Development of a small autonomous underwater vehicle controlled by thrusters and fins. Robot, Vol. 29, No. 2, 151-154, 2007. Shi S.D. (1995). Submarine Maneuverability. National Defence Industry Press, Beijing. Louis A.G. (2004). Design, modelling and control of an autonomous underwater vehicle. Bachelor of engineering honours thesis, University of Western Australia, 2004. Giuseppe C. (1999). Robust Nonlinear Motion Control for AUVs. IEEE Robotics & Automation Magazine. pp. 33-38, May 1999 Peng L.; Lu Y.C. & Wan L. (1995). Neural network control of autonomous underwater vehicles. Ocean Engineering, Vol.12, No.2, 38-46, December 1995. Liu X.M. & Xu Y.R. (2001). S control of automatic underwater vehicles. Ocean Engineering, Vol.19, No.3, 81-84, September 2001. Underwater Vehicles 556 Liu J.C.; Yu H.N. & Xu Y.R. (2002). Improved S surface control algorithm for underwater vehicles. Journal of Harbin Engineering University, Vol.23, No.1, 33- 36, March 2002. 29 Fundamentals of Underwater Vehicle Hardware and Their Applications Hiroshi Yoshida Japan Agency for Marine-Earth Science and Technology Japan 1. Introduction The evolution of electrical and electronic engineering technology including nanotechnology over the last several years has led to improvements in the development of mobile underwater platforms or autonomous underwater vehicles (AUVs) enabling them to go where tethered vehicles or manned vehicles have trouble reaching, such as under the ice, other dangerous zones, and into the deepest depths. In order to survey the whole ocean efficiently, the development of intelligent underwater vehicles will be one necessary solution. For the development of practical intelligent underwater vehicles, designers need cutting-edge fundamental devices incorporated into advanced underwater vehicles. Over the past ten years, the underwater research and development team to which the author belongs has developed five custom-made underwater vehicles: Urashima (Aoki 2001 & 2008), UROV7k (Murashma 2004), MR-X1 (Yoshida 2004), PICASSO, and ABISMO. Urashima is the prototype vehicle of a long cruising range AUV (LCAUV) powered by the hybrid power source of a lithium-ion battery and a fuel cell. Urashima autonomously travelled over 300 km for about 60 hours in 2005. The LCAUV aims to make surveys under the arctic ice possible for distances of over 3000 km. The UROV7k is a tether cable-less ROV, having its power source in its body like an AUV. The UROV7k was designed to dive up to 7000 m without large on-board equipment such as a cable winch, a traction winch or a power generator. The MR-X1 is a middle-size prototype AUV for the test of modern control methods and new hardware and for the development of new mission algorithms. The plankton survey system development project named Plankton Investigatory Collaborating Survey System Operon (PICASSO) project at the Japan Agency for Marine-earth Science and TEChnology (JAMSTEC) aims to establish a multiple vehicle observation system for efficient and innovative research on plankton. By using the ROV Kaiko, which was the deepest diving ROV in the world, a number of novel bacteria were found from mud samples taken in the Challenger Deep in the Mariana Trench (Takai, 1999). However, the lower vehicle of the KAIKO system was lost when the secondary tether was sheared (Watanabe 2004). The most important goal of the ABISMO system is to obtain mud samples from the Challenger Deep in the Mariana Trench, because scientists still want uninterrupted access to the deepest parts of the oceans using a vehicle equipped with sediment samplers. ABISMO consists of a sampling station and a sediment probe. The station contains two types of bottom samplers. One launches the probe to make a preliminary survey, launching the sampler to obtain a sample. Underwater Vehicles 558 Through the development of these vehicles, many improvements in fundamental devices for underwater vehicles were made. In this chapter, firstly, hardware information on the key devices needed to make cutting edge intelligent underwater vehicles are described. These include new original devices: a small electrical-optical hybrid communication system, an HDTV optical communication system, an inertial navigation system, buoyancy material for the deepest depths, a thin cable with high-tensile strength, a USBL system, a broadcast class HDTV camera system, an HDTV stereoscopic system, a high capacity lithium ion battery, a high efficiency closed-cycle PEM fuel cell, and a prototype of an underwater electromagnetic communication system. In the third section, we present attempts made for data processing methods for autonomous control of underwater vehicles. Finally, the details of the AUVs using the above-mentioned devices are given, including some of the sea trial results. 2. Underwater vehicle hardware 2.1 Categories of unmanned underwater vehicles and their basic device components Remotely operated vehicles (ROVs) and autonomous underwater vehicles (AUVs) are well- known kinds of underwater vehicles. Recently, there are also newer categories of underwater vehicles, untethered ROVs (UROVs) and hybrid ROVs (HROVs). UROVs (Aoki et al., 1992) have the feature that the vehicle is only connected to its support ship via a long thin optical fiber cable. The vehicle of an UROV system has its own power supply, in the form of batteries - much like an AUV. An operator controls the vehicle in real-time and has access to high quality real-time video images using high data rate optical communication tools. UROVs have both the advantages of ROVs and AUVs. An HROV (Bowen et al., 2004), one of which is under development at the Woods Hole Oceanographic Institution, is a single vehicle that can perform two different, but related, missions. It refers to the vehicle's ability to do scientific research while tethered to the ship, and also while swimming freely. Traditionally, a separate vehicle is used to conduct long range surveys, while another vehicle performs the close-up work and sampling. The HROV will simply transform between its two modes of operation to accomplish both of these tasks. In this section, cutting edge basic devices, except for those devices used for controlling vehicles and power sources, are described. a. Buoyancy Materials and Cables These are fundamental devices for underwater vehicles. In extreme environments, such as in the deepest depths, a developer should use special devices to match the mission. Full depth buoyancy materials have been commercialized but they have never actually been used in real situations at full ocean depth. The HROV project group at WHOI has chosen SeaSpheres, produced by Deepsea Power & Light, as an alternative to syntactic foams made from micro glass balloons. JAMSTEC has developed a new buoyancy material usable at full ocean depth. The prototype was used in the ABISMO system and it successfully withstood a 10,300 m depth deployment in 2008. The specifications of the prototype are a crush pressure of 56 MPa and a specific gravity of 0.63. Tether cables for underwater vehicles are also a key device for successful development. Many companies have produced underwater cables, except for cables rated for full depth. Kyo (Kyo 1999) used a Kevlar fiber cable for the full depth vehicle Kaiko, but it was broken during retrieval of the Kaiko vehicle in the face of an approaching typhoon (Watanabe 2004). JAMSTEC thus started the development of a new cable using para-aramid fiber with a [...]... weight of these devices is needed for underwater applications of fuel cell technology JAMSTEC has developed underwater vehicles for surveys in the vast underwater environment The vehicles are utilized for sea floor observations, ocean environmental research, energy source exploration, and research on marine organisms and microorganisms One of the important underwater vehicles is an AUV with a large capacity... References Abu Sharkh, S M.; Griffiths, G & Webb, A D (2003), Power Sources for Unmanned Underwater Vehicles, In: Technology and Applications of Autonomous Underwater Vehicles, Griffiths, G Ed., pp 19-35, Taylor & Francis, ISBN 0- 415- 3 0154 -8, London Adams, M & Halliop, W (2002) Aluminium Energy Semi-Fuel Cell Systems For Underwater Applications: The State Of The Art And The Way Ahead, Proceedings of OCEANS... the lithium-ion design include lower cost of manufacturing and being more robust to physical damage 568 Underwater Vehicles Battery Type Specific energy, Wh/kg Energy density, Wh/L Cycle life Lead-acid 20-30 60-80 700 Silver-zinc 100-120 180-200 100 Nickel-MH 50-70 100 -150 150 0 Lithium-ion 90 -150 150 -200 600-1000 Lithium-polymer 130-190 170-240 300-3000 Table 3 Performance of batteries (ref Abu Sharkh... of underwater vehicles Two main evaluation factors for power sources are the specific energy, energy per unit mass: Wh/kg, and the energy density, energy per unit volume: Wh/L In vehicle design, not only the energy of the power source is considered, but also the maximum output power In this section, modern power sources for intelligent underwater vehicles, secondary batteries and fuel cells in particular... described Power sources are extremely important in underwater vehicle design The recent trends of Lithium-ion batteries, which are better for small to midsize vehicles design, and fuel cells for large vehicles are introduced Three vehicles developed in JAMSTEC incorporated the mentioned devices and their sea trial results are shown The development purpose of these vehicles is different but the techniques and... membrane fuel cells (PEMFC) are the most suitable for underwater applications such as for autonomous underwater vehicles Its operation temperature are around 70 degrees Celsius and its reactive product is only pure water Underwater, a typical PEMFC system for land applications, such as found in automobiles, cannot be used because intake air does not exist underwater and the water reaction product is not... sea trial in November 2008 Fundamentals of Underwater Vehicle Hardware and Their Applications 567 Fig 9 (left) An image of satellite communications with an underwater vehicle (right) Photograph of the attitude controller with the 4-element planar antenna 2.3 Modern power sources Power sources are extremely important in underwater vehicle development, in particular for AUVs, UROVs and HROVs Power source... cable (Young 2006) is practically used for underwater vehicles b Lights and Cameras For the observation of marine organisms, seafloor geology and underwater object recognition, the selection and arrangement of lights and cameras are important The popularity of high definition television (HDTV) cameras and LED lights are causing an increase in availability of underwater video In addition to high quality... tracking test using the MROV in the Enoshima Aquarium The cross shows the tracking point 4 Present intelligent underwater vehicles In this section, vehicles, equipped with state-of-the-art devices, that were developed at the institute for which the author works are the mainfocus 4.1 Plankton survey vehicles Research on planktonic organisms is important because they are the link between greenhouse gases... HDTV images obtained by PICASSO are shown in Figure 2 560 Underwater Vehicles Fig 2 An examples of an HDTV images taken by PICASSO-1 In this picture, the sponge and crabs are illuminated by a single HID lamp (left) High power white LEDs, originally developed by Nichia corporation, have become widely used Many underwater device makers produce underwater LED lights but they may be expensive A low cost . results. 2. Underwater vehicle hardware 2.1 Categories of unmanned underwater vehicles and their basic device components Remotely operated vehicles (ROVs) and autonomous underwater vehicles (AUVs). improvements in the development of mobile underwater platforms or autonomous underwater vehicles (AUVs) enabling them to go where tethered vehicles or manned vehicles have trouble reaching, such. into advanced underwater vehicles. Over the past ten years, the underwater research and development team to which the author belongs has developed five custom-made underwater vehicles: Urashima