There has been remarkable progress in the field of robotics over the past few years, whether it is stationary robots that perform dynamically changing tasks in the manufacturing sector or automated guided vehicles for warehouse management or space exploration. The use of artificial intelligence (AI), especially reinforcement learning (RL), has contributed significantly to the success of various robotics tasks, proving that the shift toward intelligent control paradigms is successful and feasible. A fascinating aspect of RL is its ability to function both as low-level controller and as a high-level decision-making tool at the same time. An example of this is the manipulator robot whose task is to guide itself through an environment with irregular and recurrent obstacles. In this scenario, low-level controllers can receive the joint angles and execute smooth motion using the Joint Trajectory controllers. On a higher level, RL can also be used to define complex paths designed to avoid obstacles and self-collisions. An important aspect of successful operation of an AGV is the ability to make timely decisions. When Convolutional Neural Networks (CNN) based networks are incorporated with RL, agents can decide to direct AGVs to the destination effectively, which is mitigating the risk of catastrophic collisions. Even though many of these challenges can be addressed with classical solutions, devising such solutions takes a great deal of time and effort, making this process quite expensive. With an eye on different categories of RL applications to robotics, this study will provide an overview of the use of RL in robotic applications, examining the advantages and disadvantages of state-of-the-art applications. Additionally, we provide a targeted comparative analysis between classical robotics methods and RL-based robotics methods. Along with drawing conclusions from our analysis, an outline of the future possibilities and advancements that may accelerate the progress and autonomy of robotics in the future is provided.

The design and validation of a low-level backstepping controller for speed and heading that is adaptive in speed for a twin-hulled underactuated unmanned surface vessel is presented. Consideration is given to the autonomous launch and recovery of an underwater vehicle in the decision to pursue an adaptive control approach. Basic system identification is conducted and numerical simulation of the vessel is developed and validated. A speed and heading controller derived using the backstepping method and a model reference adaptive controller are developed and ultimately compared through experimental testing against a previously developed control law. Experimental tests show that the adaptive speed control law outperforms the non-adaptive alternatives by as much as 98% in some cases; however heading control is slightly sacrificed when using the adaptive speed approach. It is found that the adaptive control law is the best alternative when drag and mass properties of the vessel are time-varying and uncertain. / Includes bibliography. / Thesis (M.S.)--Florida Atlantic University, 2014. / FAU Electronic Theses and Dissertations Collection

published_or_final_version / Electrical and Electronic Engineering / Doctoral / Doctor of Philosophy

published_or_final_version / Industrial and Manufacturing Systems Engineering / Doctoral / Doctor of Philosophy

Orientador: José Alfredo Colovan Ulson / Banca: Ricardo Augusto Souza Fernandes / Banca: André Luíz Andreoli / Resumo: O Brasil possui um grande potencial no setor do agronegócio e a associação desse setor com o desenvolvimento tecnológico deu origem à Agricultura de Precisão. Nesse contexto, o uso de sensores de Nitrogênio foliar de tempo real, especificamente os que utilizam como princípio de funcionamento e espectrometria, em conjunto com sistemas inteligentes computacionais, tem contribuído de forma decisiva para o incremento da produtividade no campo, evitando a aplicação excessiva de insumos e, assim, preservando o meio ambiente. Um insumo comumente aplicado na cultura de cana-de-açúcar é o Nitrogênio que, apesar de ter grande contribuição econômica, impõe grande impacto ao meio ambiente, principalmente na poluição de aquíferos e mananciais. Dessa maneira, a quantidade aplicada desse nutriente é de grande importância, pois sua falta limita o crescimento da cultura e seu excesso polui o meio ambiente. A determinação da quantidade de Nitrogênio pode ser feita por meio do uso de sensores espectrométricos na faixa do infravermelho próximo visando a cobertura verde da cultura. Entretanto, no estágio inicial de crescimento, a cobertura verde não é plena, de forma que o sensor detecta, além da cobertura verde, o solo e cobertura morta, acrescentando ruído à medida da refletância usada para a estimação do teor de Nitrogênio na planta. Nesse cenário, este trabalho tem o objetivo de mapear a relação entre os valores fornecidos pelo sensor N-SENSOR ALS do fabricante norueguês YARA e os teores reais de Nitrogênio na planta medidos em laboratório. Mais especificamente, sistema de inferência neuro-fuzzy adaptativo (ANFIS), redes neurais artificiais do tipo Perceptron de Múltiplas Camadas (PMC) e General Regression Neural Network (GRNN) serão empregados visando a identificação e o aprendizado da relação entre os valores medidos pelo sensor N-SENSOR ALS e os valores reais obtidos em laboratório, eliminando os ruídos... / Abstract: Brazil has a great potential in the agribusiness sector and the association of this sector with technological development gave rise to precision agriculture. In this context, the use of soil sensors for real-time, specifically those using operating principle as spectrometry, together with computational intelligent systems are contributing decisively to increasing productivity in the field, avoiding excessive use of inputs and thus preserving the environment. An ingredient commonly used in the cultivation of sugar cane is the nitrogen that, depite great economic contribution, imposes great impact on the environment, especially in aquifers and fountains pollution. Thus, the applied amount of this nutrient is of great importance, since the lack of limits crop growth and excess pollute the environment. The determination of the nitrogen content can be made through the use of spectrometric sensors in the near-infrared aiming the green cover crop. However, in the initial stage of growth the green coverage is not complete, so the sensor "sees" beyond the green cover, soil and mulch, adding noise to the measurement of reflectance used to estimate the percentage of nitrogen in plant. In this scenario, this paper aims to map the relationship between the values provided by the sensor N-SENSOR ALS Norwegian YARA manufacturer and the actual levels of nitrogen in the plant measured in the laboratory. More specifically, systems of inference adaptive neuro-fuzzy (ANFIS), artificial neural network Multilayer Perceptron type (MLP) and General Regression Neural Network (GRNN) are employed in order to identify learning and the relationship between the values measured by the sensor N-SENSOR ALS and actual obtaind in the laboratory, eliminating the noise imposed by non-green roof and external disturbances such as the variation of ambient light. The results indicate that the neuro-fuzzy approach has superior performance and neural networks can be used to correct... / Mestre

Fung Wai-keung. / "February 5, 2001." / Thesis (Ph.D.)--Chinese University of Hong Kong, 2001. / Includes bibliographical references (p. 109-116). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Mode of access: World Wide Web. / Abstracts in English and Chinese.

by Chiu, Kit Chau. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2005. / Includes bibliographical references (leaves 90-92). / Abstracts in English and Chinese. / ABSTRACT --- p.02 / 摘要 --- p.04 / ACKNOWLEDGEMENTS --- p.06 / TABLE OF CONTENTS --- p.07 / LIST OF FIGURES --- p.10 / LIST OF TABLES --- p.12 / Chapter 1 --- INTRODUCTION --- p.13 / Chapter 1.1 --- Application with formation flying --- p.14 / Chapter 1.2 --- Previous work --- p.16 / Chapter 1.3 --- The present work --- p.18 / Chapter 1.4 --- Thesis outline --- p.19 / Chapter 2 --- OPTIMIZATION IN DESIRED TRAJECTORY --- p.21 / Chapter 2.1 --- Problem formulation --- p.21 / Chapter 2.1.1 --- System model --- p.21 / Chapter 2.1.2 --- System constraints --- p.22 / Chapter 2.1.3 --- Cost function of the system --- p.23 / Chapter 2.2 --- Reformation as optimal control problem --- p.23 / Chapter 2.2.1 --- Polynomial form for input --- p.24 / Chapter 2.2.2 --- Problem simplification --- p.26 / Chapter 2.3 --- Numerical case studies --- p.27 / Chapter 2.3.1 --- Case study 2-1: Equal weightings in all units and directions --- p.27 / Chapter 2.3.2 --- Case study 2-2: Equal weightings in all directions but different weightings in control units --- p.30 / Chapter 2.3.3 --- Case 2-3: Different weightings in x-y-z directions but equal weightings in all control units --- p.33 / Chapter 2.4 --- Chapter summary --- p.35 / Chapter 3 --- OBSTACLE AVOIDANCE --- p.36 / Chapter 3.1 --- Additions of obstacle constraints --- p.36 / Chapter 3.2 --- Simulation case studies --- p.37 / Chapter 3.2.1 --- Case study 3-1: No obstacle --- p.38 / Chapter 3.2.2 --- Case study 3-2: Single obstacles --- p.40 / Chapter 3.2.3 --- Case study 3-3: Two obstacles --- p.42 / Chapter 3.2.4 --- Case study 3-4: Two obstacles and optimal velocity --- p.48 / Chapter 3.3 --- Chapter summary --- p.51 / Chapter 4 --- FUZZY INTERACTIVE FORCE BETWEEN ELEMENTS --- p.52 / Chapter 4.1 --- Region of repulsive force --- p.52 / Chapter 4.2 --- Region of attractive force --- p.53 / Chapter 4.3 --- Beyond the attractive region --- p.53 / Chapter 4.4 --- Interactive force as function of separation --- p.54 / Chapter 4.5 --- Fuzzy mapping --- p.55 / Chapter 4.6 --- Chapter summary --- p.58 / Chapter 5 --- VIRTUAL LEADER --- p.59 / Chapter 5.1 --- Virtual leader --- p.59 / Chapter 5.2 --- Two maneuverable elements and two virtual leaders --- p.60 / Chapter 5.3 --- Rotational Trajectories for the two virtual leaders --- p.61 / Chapter 5.4 --- Chapter summary --- p.65 / Chapter 6 --- OPIMIZATION BY INTERACTIVE FORCE --- p.66 / Chapter 6.1 --- Narrow channel passage --- p.66 / Chapter 6.2 --- Interactive forces --- p.68 / Chapter 6.3 --- Definition of interactive force --- p.69 / Chapter 6.4 --- Formulation as optimization problem --- p.71 / Chapter 6.4.1 --- Parameterization of f1 and f2 --- p.71 / Chapter 6.4.2 --- Reformulated optimization problem --- p.73 / Chapter 6.5 --- Simulation results --- p.74 / Chapter 6.6 --- Chapter summary --- p.77 / Chapter 7 --- MODIFICATION IN OBSTACLE --- p.78 / Chapter 7.1 --- Modification for interactive force --- p.78 / Chapter 7.2 --- Modification in obstacle description --- p.79 / Chapter 7.3 --- """Shortest distance"" between control unit and obstacle" --- p.80 / Chapter 7.4 --- Simulation case studies --- p.81 / Chapter 7.4.1 --- Case study 7-1: Single triangular obstacle --- p.81 / Chapter 7.4.2 --- Case study 7-2: Two triangular obstacles --- p.83 / Chapter 7.5 --- Chapter summary --- p.85 / Chapter 8 --- Conclusions and future works --- p.86 / Chapter 8.1 --- Conclusions --- p.86 / Chapter 8.2 --- Future works --- p.88 / Chapter 8.2.1 --- Fuzzy mapping --- p.88 / Chapter 8.2.2 --- Intrinsic parameters and properties --- p.89 / BIBLIOGRAPHY --- p.90

Nowadays, industrial processes are having to be rapidly developed to meet high standards regarding increases in the production rate and/or improving product quality. Fulfilling these requirements is having to work in tandem with the pressure to reduce energy consumption due to global environmental regulations. Consequently, most industrial processes critically rely on automatic control, which can provide efficient solutions to meet such challenges and prerequisites. For this thesis, an intelligent system design has been investigated for controlling the distillation process, which is characterised by highly nonlinear and dynamic behaviour. These features raise very challenging tasks for control systems designers. Fuzzy logic and artificial neural networks (ANNs) are the main methods used in this study to design different controllers, namely: PI- PD- and PID-like fuzzy controllers, ANN-based NARMAL2 in addition to a conventional PID controller for comparison purposes. Genetic algorithm (GA) and particle swarm optimisation (PSO) have also been utilised to tune fuzzy controllers by finding the best set of scaling factors. Finally, an intelligent controller is proposed, called ANFIS-based NARMA-L2, which uses ANFIS as an approximation approach for identifying the underlying systems in a NARMA-L2 configuration. The controllers are applied to control two compositions of a binary distillation column, which has been modelled and simulated in MATLAB® and on the Simulink® platform. Comparative analysis has been undertaken to investigate the controllers' performance, which shows that PID-like FLC outperforms the other tested fuzzy control configurations, i.e. PI- and PD-like. Moreover, PSO has been found to outperform GA in finding the best set of scaling factors and over a shorter time period. Subsequently, the performance of PID-like FLC has been compared with ANN-based NARMA-L2 and the proposed ANFIS-based NARMA-L2, by subjecting the controlled column to different test scenarios. Furthermore, the stability and robustness of the controllers have been assessed by subjecting the controlled column to inputs variance and disturbances situations. The proposed ANFIS-based NARMAL2 controller outperforms and demonstrates more tolerance of disturbances than the other controllers. Finally, the study has involved investigating the control of a multicomponent distillation column due to its significant enhancement in operational efficiency regarding energy saving and recent widespread implementation. That is, Kaibel's distillation column with 4×4 configuration has been simulated also in MATLAB® and on the Simulink® platform with the proposed controller being implemented to control the temperatures of the column and the outcomes subsequently compared with conventional PID controllers. Again, the novel controller has proven its superiority regarding the disturbances tolerance as well as dealing with the high dynamics and nonlinear behaviour.

This thesis provides a unified and comprehensive treatment of the fuzzy neural networks as the intelligent controllers. This work has been motivated by a need to develop the solid control methodologies capable of coping with the complexity, the nonlinearity, the interactions, and the time variance of the processes under control. In addition, the dynamic behavior of such processes is strongly influenced by the disturbances and the noise, and such processes are characterized by a large degree of uncertainty. Therefore, it is important to integrate an intelligent component to increase the control system ability to extract the functional relationships from the process and to change such relationships to improve the control precision, that is, to display the learning and the reasoning abilities. The objective of this thesis was to develop a self-organizing learning controller for above processes by using a combination of the fuzzy logic and the neural networks. An on-line, direct fuzzy neural controller using the process input-output measurement data and the reference model with both structural and parameter tuning has been developed to fulfill the above objective. A number of practical issues were considered. This includes the dynamic construction of the controller in order to alleviate the bias/variance dilemma, the universal approximation property, and the requirements of the locality and the linearity in the parameters. Several important issues in the intelligent control were also considered such as the overall control scheme, the requirement of the persistency of excitation and the bounded learning rates of the controller for the overall closed loop stability. Other important issues considered in this thesis include the dependence of the generalization ability and the optimization methods on the data distribution, and the requirements for the on-line learning and the feedback structure of the controller. Fuzzy inference specific issues such as the influence of the choice of the defuzzification method, T-norm operator and the membership function on the overall performance of the controller were also discussed. In addition, the e-completeness requirement and the use of the fuzzy similarity measure were also investigated. Main emphasis of the thesis has been on the applications to the real-world problems such as the industrial process control. The applicability of the proposed method has been demonstrated through the empirical studies on several real-world control problems of industrial complexity. This includes the temperature and the number-average molecular weight control in the continuous stirred tank polymerization reactor, and the torsional vibration, the eccentricity, the hardness and the thickness control in the cold rolling mills. Compared to the traditional linear controllers and the dynamically constructed neural network, the proposed fuzzy neural controller shows the highest promise as an effective approach to such nonlinear multi-variable control problems with the strong influence of the disturbances and the noise on the dynamic process behavior. In addition, the applicability of the proposed method beyond the strictly control area has also been investigated, in particular to the data mining and the knowledge elicitation. When compared to the decision tree method and the pruned neural network method for the data mining, the proposed fuzzy neural network is able to achieve a comparable accuracy with a more compact set of rules. In addition, the performance of the proposed fuzzy neural network is much better for the classes with the low occurrences in the data set compared to the decision tree method. Thus, the proposed fuzzy neural network may be very useful in situations where the important information is contained in a small fraction of the available data.

Thesis (Ph. D.)--University of Hong Kong, 2006. / Title proper from title frame. Also available in printed format.