Mathematical modeling and identification of surface vessel model parameters

The paper considers the problems of modeling and identification of parameters for models of surface ships. The proposed identification method is applied to a modified second order Nomoto model for ship steering. The identification algorithm is based on the Dynamic Regressor Extension and Mixing Method (DREM) that is performed in two steps. At the first stage parameterization is used for a regression model, in which the regressor and regression depend on the measured signals, namely, longitudinal, lateral and angular velocities and steering angle. At the second stage a new regression model is built using linear stable filters and delays. Finally, the parameters are estimated by the standard gradient descent method. The paper proposes a new algorithm which identifies the parameters for models of surface ships. The authors analyzed the prospects of the proposed estimating method by computer experiments. Experiments have shown the advantage of the method: when using the gradient descent method, the transient time spent to estimate the signal parameters is much longer than using the DREM method. At the same time, in the case of using the DREM method, there is no overshoot. The results of the work can serve as a basis for methods, algorithms and software for designing ship automated navigation systems and control systems for other modes of transport. This is confirmed by the simulation results

A brief review of neural networks based learning and control and their applications for robots

As an imitation of the biological nervous systems, neural networks (NN), which are characterized with powerful learning ability, have been employed in a wide range of applications, such as control of complex nonlinear systems, optimization, system identification and patterns recognition etc. This article aims to bring a brief review of the state-of-art NN for the complex nonlinear systems. Recent progresses of NNs in both theoretical developments and practical applications are investigated and surveyed. Specifically, NN based robot learning and control applications were further reviewed, including NN based robot manipulator control, NN based human robot interaction and NN based behavior recognition and generation

Experimental Validation Of An Integrated Guidance And Control System For Marine Surface Vessels

Autonomous operation of marine surface vessels is vital for minimizing human errors and providing efficient operations of ships under varying sea states and environmental conditions which is complicated by the highly nonlinear dynamics of marine surface vessels. To deal with modelling imprecision and unpredictable disturbances, the sliding mode methodology has been employed to devise a heading and a surge displacement controller. The implementation of such a controller necessitates the availability of all state variables of the vessel. However, the measured signals in the current study are limited to the global X and Y positioning coordinates of the boat that are generated by a GPS system. Thus, a nonlinear observer, based on the sliding mode methodology, has been implemented to yield accurate estimates of the state variables in the presence of both structured and unstructured uncertainties. Successful autonomous operation of a marine surface vessel requires a holistic approach encompassing a navigation system, robust nonlinear controllers and observers. Since the overwhelming majority of the experimental work on autonomous marine surface vessels was not conducted in truly uncontrolled real-world environments. The first goal of this work was to experimentally validate a fully-integrated LOS guidance system with a sliding mode controller and observer using a 16’ Tracker Pro Guide V-16 aluminium boat with a 60 hp. Mercury outboard motor operating in the uncontrolled open-water environment of Lake St. Clair, Michigan. The fully integrated guidance and controller-observer system was tested in a model-less configuration, whereby all information provided from the vessel’s nominal model have been ignored. The experimental data serves to demonstrate the robustness and good tracking characteristics of the fully-integrated guidance and controller/observer system by overcoming the large errors induced at the beginning of each segment and converging the boat to the desired trajectory in spite of the presence of environmental disturbances. The second focus of this work was to combine a collision avoidance method with the guidance system that accounted for “International Regulations for Prevention of Collisions at Sea” abbreviated as COLREGS. This new system then needed to be added into the existing architecture. The velocity obstacles method was selected as the base to build upon and additional restrictions were incorporated to account for these additional rules. This completed system was then validated with a software in the loop simulation

Adaptive Control

Adaptive control has been a remarkable field for industrial and academic research since 1950s. Since more and more adaptive algorithms are applied in various control applications, it is becoming very important for practical implementation. As it can be confirmed from the increasing number of conferences and journals on adaptive control topics, it is certain that the adaptive control is a significant guidance for technology development.The authors the chapters in this book are professionals in their areas and their recent research results are presented in this book which will also provide new ideas for improved performance of various control application problems

From plain visualisation to vibration sensing: using a camera to control the flexibilities in the ITER remote handling equipment

Thermonuclear fusion is expected to play a key role in the energy market during the second half of this century, reaching 20% of the electricity generation by 2100. For many years, fusion scientists and engineers have been developing the various technologies required to build nuclear power stations allowing a sustained fusion reaction. To the maximum possible extent, maintenance operations in fusion reactors are performed manually by qualified workers in full accordance with the "as low as reasonably achievable" (ALARA) principle. However, the option of hands-on maintenance becomes impractical, difficult or simply impossible in many circumstances, such as high biological dose rates. In this case, maintenance tasks will be performed with remote handling (RH) techniques. The International Thermonuclear Experimental Reactor ITER, to be commissioned in southern France around 2025, will be the first fusion experiment producing more power from fusion than energy necessary to heat the plasma. Its main objective is “to demonstrate the scientific and technological feasibility of fusion power for peaceful purposes”. However ITER represents an unequalled challenge in terms of RH system design, since it will be much more demanding and complex than any other remote maintenance system previously designed. The introduction of man-in-the-loop capabilities in the robotic systems designed for ITER maintenance would provide useful assistance during inspection, i.e. by providing the operator the ability and flexibility to locate and examine unplanned targets, or during handling operations, i.e. by making peg-in-hole tasks easier. Unfortunately, most transmission technologies able to withstand the very specific and extreme environmental conditions existing inside a fusion reactor are based on gears, screws, cables and chains, which make the whole system very flexible and subject to vibrations. This effect is further increased as structural parts of the maintenance equipment are generally lightweight and slender structures due to the size and the arduous accessibility to the reactor. Several methodologies aiming at avoiding or limiting the effects of vibrations on RH system performance have been investigated over the past decade. These methods often rely on the use of vibration sensors such as accelerometers. However, reviewing market shows that there is no commercial off-the-shelf (COTS) accelerometer that meets the very specific requirements for vibration sensing in the ITER in-vessel RH equipment (resilience to high total integrated dose, high sensitivity). The customisation and qualification of existing products or investigation of new concepts might be considered. However, these options would inevitably involve high development costs. While an extensive amount of work has been published on the modelling and control of flexible manipulators in the 1980s and 1990s, the possibility to use vision devices to stabilise an oscillating robotic arm has only been considered very recently and this promising solution has not been discussed at length. In parallel, recent developments on machine vision systems in nuclear environment have been very encouraging. Although they do not deal directly with vibration sensing, they open up new prospects in the use of radiation tolerant cameras. This thesis aims to demonstrate that vibration control of remote maintenance equipment operating in harsh environments such as ITER can be achieved without considering any extra sensor besides the embarked rad-hardened cameras that will inevitably be used to provide real-time visual feedback to the operators. In other words it is proposed to consider the radiation-tolerant vision devices as full sensors providing quantitative data that can be processed by the control scheme and not only as plain video feedback providing qualitative information. The work conducted within the present thesis has confirmed that methods based on the tracking of visual features from an unknown environment are effective candidates for the real-time control of vibrations. Oscillations induced at the end effector are estimated by exploiting a simple physical model of the manipulator. Using a camera mounted in an eye-in-hand configuration, this model is adjusted using direct measurement of the tip oscillations with respect to the static environment. The primary contribution of this thesis consists of implementing a markerless tracker to determine the velocity of a tip-mounted camera in an untrimmed environment in order to stabilise an oscillating long-reach robotic arm. In particular, this method implies modifying an existing online interaction matrix estimator to make it self-adjustable and deriving a multimode dynamic model of a flexible rotating beam. An innovative vision-based method using sinusoidal regression to sense low-frequency oscillations is also proposed and tested. Finally, the problem of online estimation of the image capture delay for visual servoing applications with high dynamics is addressed and an original approach based on the concept of cross-correlation is presented and experimentally validated

Intelligent methods for complex systems control engineering

This thesis proposes an intelligent multiple-controller framework for complex systems that incorporates a fuzzy logic based switching and tuning supervisor along with a neural network based generalized learning model (GLM). The framework is designed for adaptive control of both Single-Input Single-Output (SISO) and Multi-Input Multi-Output (MIMO) complex systems. The proposed methodology provides the designer with an automated choice of using either: a conventional Proportional-Integral-Derivative (PID) controller, or a PID structure based (simultaneous) Pole and Zero Placement controller. The switching decisions between the two nonlinear fixed structure controllers is made on the basis of the required performance measure using the fuzzy logic based supervisor operating at the highest level of the system. The fuzzy supervisor is also employed to tune the parameters of the multiple-controller online in order to achieve the desired system performance. The GLM for modelling complex systems assumes that the plant is represented by an equivalent model consisting of a linear time-varying sub-model plus a learning nonlinear sub-model based on Radial Basis Function (RBF) neural network. The proposed control design brings together the dominant advantages of PID controllers (such as simplicity in structure and implementation) and the desirable attributes of Pole and Zero Placement controllers (such as stable set-point tracking and ease of parameters’ tuning). Simulation experiments using real-world nonlinear SISO and MIMO plant models, including realistic nonlinear vehicle models, demonstrate the effectiveness of the intelligent multiple-controller with respect to tracking set-point changes, achieve desired speed of response, prevent system output overshooting and maintain minimum variance input and output signals, whilst penalising excessive control actions

Underwater Vehicles

For the latest twenty to thirty years, a significant number of AUVs has been created for the solving of wide spectrum of scientific and applied tasks of ocean development and research. For the short time period the AUVs have shown the efficiency at performance of complex search and inspection works and opened a number of new important applications. Initially the information about AUVs had mainly review-advertising character but now more attention is paid to practical achievements, problems and systems technologies. AUVs are losing their prototype status and have become a fully operational, reliable and effective tool and modern multi-purpose AUVs represent the new class of underwater robotic objects with inherent tasks and practical applications, particular features of technology, systems structure and functional properties

A Biologically Inspired Framework for the Intelligent Control of Mechatronic Systems and Its Application to a Micro Diving Agent

Mechatronic systems are becoming an intrinsic part of our daily life, and the adopted control approach in turn plays an essential role in the emulation of the intelligent behavior. In this paper, a framework for the development of intelligent controllers is proposed. We highlight that robustness, prediction, adaptation, and learning, which may be considered the most fundamental traits of all intelligent biological systems, should be taken into account within the project of the control scheme. Hence, the proposed framework is based on the fusion of a nonlinear control scheme with computational intelligence and also allows mechatronic systems to be able to make reasonable predictions about its dynamic behavior, adapt itself to changes in the plant, learn by interacting with the environment, and be robust to both structured and unstructured uncertainties. In order to illustrate the implementation of the control law within the proposed framework, a new intelligent depth controller is designed for a microdiving agent. On this basis, sliding mode control is combined with an adaptive neural network to provide the basic intelligent features. Online learning by minimizing a composite error signal, instead of supervised off-line training, is adopted to update the weight vector of the neural network. The boundedness and convergence properties of all closed-loop signals are proved using a Lyapunov-like stability analysis. Numerical simulations and experimental results obtained with the microdiving agent demonstrate the efficacy of the proposed approach and its suitableness for both stabilization and trajectory tracking problems.</p

Flexible adaptation of iterative learning control with applications to synthetic bone graft manufacturing

Additive manufacturing processes are powerful tools; they are capable of fabricating structures without expensive structure specific tooling -- therefore structure designs can efficiently change from run-to-run -- and they can integrate multiple distinct materials into a single structure. This work investigates one such additive manufacturing process, micro-Robotic Deposition ($\mu$RD), and its utility in fabricating advanced architecture synthetic bone grafts. These bone grafts, also known as synthetic bone scaffolds, are highly porous three-dimensional structures that provide a matrix to support the natural process of bone remodeling. Ideally, the synthetic scaffold will stimulate complete bone healing in a skeletal defect site and also resorb with time so that only natural tissue remains. The objective of this research is to develop methods to integrate different regions with different porous microstructures into a single scaffold; there is evidence that scaffolds with designed regions of specific microstructures can be used to elicit a strong and directed bone ingrowth response that improves bone ingrowth rate and quality. The key contribution of this work is the development of a control algorithm that precisely places different build materials in specified locations, thereby the fabrication of advanced architecture scaffolds is feasible. Under previous control methods, designs were relegated to be composed of a single material. The control algorithm developed in this work is an adaptation of Iterative Learning Control (ILC), a control method that is typically best suited for mass manufacturing applications. This adaptation reorients the ILC framework such that it is more amenable to additive manufacturing systems, such as $\mu$RD. Control efficacy is demonstrated by the fabrication of advanced architecture scaffolds. Scaffolds with contoured forms, multiple domains with distinct porous microstructures, and hollow cavities are feasible when the developed controller is used in conjunction with a novel manufacturing workflow in which scaffolds are filled within patterned molds that support overhanging features. An additional application demonstrates controller performance on the robot positioning problem; this work has implications for additive manufacturing in general