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

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

A COLLISION AVOIDANCE SYSTEM FOR AUTONOMOUS UNDERWATER VEHICLES

The work in this thesis is concerned with the development of a novel and practical collision avoidance system for autonomous underwater vehicles (AUVs). Synergistically, advanced stochastic motion planning methods, dynamics quantisation approaches, multivariable tracking controller designs, sonar data processing and workspace representation, are combined to enhance significantly the survivability of modern AUVs. The recent proliferation of autonomous AUV deployments for various missions such as seafloor surveying, scientific data gathering and mine hunting has demanded a substantial increase in vehicle autonomy. One matching requirement of such missions is to allow all the AUV to navigate safely in a dynamic and unstructured environment. Therefore, it is vital that a robust and effective collision avoidance system should be forthcoming in order to preserve the structural integrity of the vehicle whilst simultaneously increasing its autonomy. This thesis not only provides a holistic framework but also an arsenal of computational techniques in the design of a collision avoidance system for AUVs. The design of an obstacle avoidance system is first addressed. The core paradigm is the application of the Rapidly-exploring Random Tree (RRT) algorithm and the newly developed version for use as a motion planning tool. Later, this technique is merged with the Manoeuvre Automaton (MA) representation to address the inherent disadvantages of the RRT. A novel multi-node version which can also address time varying final state is suggested. Clearly, the reference trajectory generated by the aforementioned embedded planner must be tracked. Hence, the feasibility of employing the linear quadratic regulator (LQG) and the nonlinear kinematic based state-dependent Ricatti equation (SDRE) controller as trajectory trackers are explored. The obstacle detection module, which comprises of sonar processing and workspace representation submodules, is developed and tested on actual sonar data acquired in a sea-trial via a prototype forward looking sonar (AT500). The sonar processing techniques applied are fundamentally derived from the image processing perspective. Likewise, a novel occupancy grid using nonlinear function is proposed for the workspace representation of the AUV. Results are presented that demonstrate the ability of an AUV to navigate a complex environment. To the author's knowledge, it is the first time the above newly developed methodologies have been applied to an A UV collision avoidance system, and, therefore, it is considered that the work constitutes a contribution of knowledge in this area of work.J&S MARINE LT