Design of Unmanned Underwater Vehicle (UUV) For Precision Targeting Using Simple PID-Controler

A model of an Unmanned Underwater Vehicle (UUV) for precision targeting using simple PID controller has been designed. The system has been assumed to have two-dimensional character, such that the mechanical control mechanism would be performed solely by rudder. A GPS/IMU system was employed in the model to provide the exact location and current trajectory direction and will be used to compared between the instantaneous correct direction and instantaneous current direction. This difference would drive PID control system to give correct angle deflection of the rudder. Some parameters of the PID controller has to be well-tuned employing several schemes including the Routh-Hurwitz stability criterion. Keywords: UUV, PID Controller, Precision Targeting, GPS, IM

Intelligent Control Strategies for an Autonomous Underwater Vehicle

The dynamic characteristics of autonomous underwater vehicles (AUVs) present a control problem that classical methods cannot often accommodate easily. Fundamentally, AUV dynamics are highly non-linear, and the relative similarity between the linear and angular velocities about each degree of freedom means that control schemes employed within other flight vehicles are not always applicable. In such instances, intelligent control strategies offer a more sophisticated approach to the design of the control algorithm. Neurofuzzy control is one such technique, which fuses the beneficial properties of neural networks and fuzzy logic in a hybrid control architecture. Such an approach is highly suited to development of an autopilot for an AUV. Specifically, the adaptive network-based fuzzy inference system (ANFIS) is discussed in Chapter 4 as an effective new approach for neurally tuning course-changing fuzzy autopilots. However, the limitation of this technique is that it cannot be used for developing multivariable fuzzy structures. Consequently, the co-active ANFIS (CANFIS) architecture is developed and employed as a novel multi variable AUV autopilot within Chapter 5, whereby simultaneous control of the AUV yaw and roll channels is achieved. Moreover, this structure is flexible in that it is extended in Chapter 6 to perform on-line control of the AUV leading to a novel autopilot design that can accommodate changing vehicle pay loads and environmental disturbances. Whilst the typical ANFIS and CANFIS structures prove effective for AUV control system design, the well known properties of radial basis function networks (RBFN) offer a more flexible controller architecture. Chapter 7 presents a new approach to fuzzy modelling and employs both ANFIS and CANFIS structures with non-linear consequent functions of composite Gaussian form. This merger of CANFIS and a RBFN lends itself naturally to tuning with an extended form of the hybrid learning rule, and provides a very effective approach to intelligent controller development.The Sea Systems and Platform Integration Sector, Defence Evaluation and Research Agency, Winfrit

Optimization of Potential Field Method Parameters through networks for Swarm Cooperative Manipulation Tasks

An interesting current research field related to autonomous robots is mobile manipulation performed by cooperating robots (in terrestrial, aerial and underwater environments). Focusing on the underwater scenario, cooperative manipulation of Intervention-Autonomous Underwater Vehicles (I-AUVs) is a complex and difficult application compared with the terrestrial or aerial ones because of many technical issues, such as underwater localization and limited communication. A decentralized approach for cooperative mobile manipulation of I-AUVs based on Artificial Neural Networks (ANNs) is proposed in this article. This strategy exploits the potential field method; a multi-layer control structure is developed to manage the coordination of the swarm, the guidance and navigation of I-AUVs and the manipulation task. In the article, this new strategy has been implemented in the simulation environment, simulating the transportation of an object. This object is moved along a desired trajectory in an unknown environment and it is transported by four underwater mobile robots, each one provided with a seven-degrees-of-freedom robotic arm. The simulation results are optimized thanks to the ANNs used for the potentials tuning

Neural Network Predictive Control (NNPC) of a Deep Submergence Rescue Vehicle (DSRV)

In this paper, the modeling and design of the depth control systems using Neural Network Predictive Control (NNPC)for a small unmanned underwater vehicle (UUV) will be described. Underwater vehicles consist of robotic vehicles that have been developed to reduce the risks of human life and to carry out tasks that would be impractical with a manned mission. The design of a depth control of an UUV is described in this paper. The main purpose of the underwater vehicle is that the vehicle must be stable over the entire range of operation. These techniques have the purpose of ensuring zero steady state error and minimum error in response to step commands in the desired depth.The depth performance for NNPC is discussed in terms of error and execution time. This NNPC will be compared with conventional controller such as PD controller and also by using the Fuzzy Logic Controller (FLC). For the comparison of computational time between this controllers, it can be observed that Fuzzy Logic is faster and neural network predictive controller is the slowest between them. It has been shown that the neural network predictive controller improved the transient response and error measure which shows the effectiveness of the designed controller

Towards Odor-Sensitive Mobile Robots

J. Monroy, J. Gonzalez-Jimenez, "Towards Odor-Sensitive Mobile Robots", Electronic Nose Technologies and Advances in Machine Olfaction, IGI Global, pp. 244--263, 2018, doi:10.4018/978-1-5225-3862-2.ch012 Versión preprint, con permiso del editorOut of all the components of a mobile robot, its sensorial system is undoubtedly among the most critical ones when operating in real environments. Until now, these sensorial systems mostly relied on range sensors (laser scanner, sonar, active triangulation) and cameras. While electronic noses have barely been employed, they can provide a complementary sensory information, vital for some applications, as with humans. This chapter analyzes the motivation of providing a robot with gas-sensing capabilities and also reviews some of the hurdles that are preventing smell from achieving the importance of other sensing modalities in robotics. The achievements made so far are reviewed to illustrate the current status on the three main fields within robotics olfaction: the classification of volatile substances, the spatial estimation of the gas dispersion from sparse measurements, and the localization of the gas source within a known environment

Single Input Fuzzy Logic Controller For Yaw Control Of Underwater Remotely Operated Crawler

Underwater Remotely Operated Crawler (ROC) is a class of the Unmanned Underwater Vehicle (UUV) that is tethered, unoccupied, manoeuvres on the seabed and remotely operated by a pilot from a platform. Underwater characteristic parameters such as added mass, buoyancy, hydrodynamic forces, underwater currents, including pressure could considerably affect and reduce the mobility of the ROC. The challenges faced by the ROCs are that the needs to reduce the overshoot in the system response, including, the time response and settling time. For yaw control (a motion around the z-axis), an occurrence of an overshoot in the system response is highly intolerable. Reducing the overshoot in the ROC trajectory is crucial since there are many challenging underwater natures and underwater vehicle control problems while studies on finding the solutions are still ongoing to find an improvement. Conventional Proportional-Integral-Derivative (PID) controller is not robust to be applied in the ROC due to the non-linear dynamic model of the ROC and underwater conditions. Besides that, by reducing the overshoot, the ROC mobility will be much more efficient and provided a reliable platform for underwater data mining. This study is focused to give an optimum performance of yaw control without overshoot in the system response and faster time response. This research begins by designing an underwater ROC as the research’s platform. Then, the designed ROC is simulated by using SolidWorks software obtain the analysis of structural integrity and hydrodynamic properties. System identification technique is conducted to obtain the empirical modelling design of the fabricated ROC which equipped with Inertial Measurement Unit (IMU) sensor. The fuzzy logic controller (FLC) is designed based on 5 by 5 rule matrix which has to deal with fuzzification, rule base, inference mechanism and defuzzification operations. A simplification of the FLC is proposed and the method is called Single Input Fuzzy Logic Controller (SIFLC). The simplification is achieved by applying the “signed distance method” where the SIFLC reduces the two-input FLC to a single input FLC. In other words, SIFLC is based on the signed distance method which eventually reduces the controller as single input-single output (SISO) controller. A PID controller is designed for the purpose of benchmarking with the FLC and SIFLC. SIFLC has the capability to adapt the non-linear underwater parameters (currents, waves and etc.). This research has discussed and compared the performance of PID, FLC and SIFLC. The algorithm is verified in MATLAB/Simulink software. Based on the results, SIFLC provides more robust and reliable control system. Based on the computation results, SIFLC reduces the percentage of overshoot (%OS) of the system and achieve 0.121%, while other controllers (PID and FLC) 4.4% and 1.7% respectively. Even that so, this does not mean that PID and FLC are not reliable but due to the presence of %OS

Classifying motion states of AUV based on graph representation for multivariate time series

Acknowledgement This work is supported by Natural Science Foundation of Shandong Province (ZR2020MF079) and China Scholarship Council (CSC).Peer reviewedPostprin