Adaptive controllers allow robots to perform a wide variety of tasks, but the extensive computations required have generated an interest in developing decentralized adaptive controllers. Horner has designed an adaptive controller for a four-degree-of-freedom mobile robot and tested it through simulations. The study described in this thesis uses the techniques described by Horner to design and test a decentralized model reference adaptive controller (DMRAC) for a physical four-degree-of-freedom mobile robot. The study revealed several difficulties in implementing this design. Most notably, the robot available for the research did not allow for the measurement of joint velocity, so it was necessary to estimate the velocity as the derivative of the position measurement. The noise created by this estimation made completion of testing impossible. Future research should be performed on a robot that provides joint velocity measurement. Alternatively, a study could include state estimation as part of the controller, thus reducing and possibly eliminating the need for velocity measurement. / Master of Science

A crescente complexidade do controle dos processos industriais vem exigindo sistemas de controle cada vez mais precisos, confiáveis e versáteis. No sentido de atender a estas exigências, algoritmos diversos de controle e estimação, tais como: técnicas de controle adaptativas, preditivas, estimação paramétrica, filtros de Kalman, observação de estados, etc. têm sido desenvolvidas, simuladas e implementadas com relativa facilidade nos modernos sistemas digitais. Este trabalho propõe uma estratégia de controle em redes Fieldbus usando controle adaptativo por modelo de referência através de variáveis de estado, para resolver sistemas complexos. O algoritmo de controle proposto é composto por um observador de estados trabalhando em conjunto com um esquema de controle adaptativo por modelo de referência. As malhas de controle no Fieldbus consistem em um conjunto de blocos funcionais padrões existentes, conectados aos novos blocos funcionais propostos e desenvolvidos de acordo com as especificações da norma Fieldbus Foundation. Este algoritmo de controle realiza os cálculos de maneira distribuída entre os dispositivos da rede Fieldbus, implicando em várias vantagens, tais como: a) perda do supervisório não implica na perda do algoritmo de controle, b) menor tráfego de dados na rede, c) algoritmo de controle que não depende do tempo de processamento do computador. Os resultados dos testes obtidos são apresentados e demonstraram um alto grau de precisão, destacando-se a estabilidade e aplicabilidade do algoritmo proposto. / The rising complexity of the industrial control processes has been claiming more and more accurate, reliable and versatile control systems. Attempting to satisfy this demand, several control and estimation systems algorithms, such as adaptive and predictive techniques, parametric estimation, Kalman filtering, state observation, have been designed, simulated and implemented with relative easiness in the modern digital systems. This work proposes a new control strategy in Fieldbus networks, using adaptive control techniques through state variables, to solve complex systems. The proposed control algorithm is based on an states observer concurrently working with a reference model adaptive control scheme. The modified Fieldbus network consists of a set of standard function blocks, connected to the proposed new function blocks. These new function blocks comply with the specifications of the Fieldbus Foundation norm. This control algorithm accomplishes its calculation in a distributed way among the fieldbus devices. This operating feature results in some advantages, such as: any failure in the supervisory system does not imply in the loss of the control algorithm, b) a lower data transmission in the network, c) control algorithm that does not depend on the processing time of the computer. The obtained results corroborate with the expected advantages of the proposed algorithm, in terms of high degree of accuracy, stability and applicability.

A crescente complexidade do controle dos processos industriais vem exigindo sistemas de controle cada vez mais precisos, confiáveis e versáteis. No sentido de atender a estas exigências, algoritmos diversos de controle e estimação, tais como: técnicas de controle adaptativas, preditivas, estimação paramétrica, filtros de Kalman, observação de estados, etc. têm sido desenvolvidas, simuladas e implementadas com relativa facilidade nos modernos sistemas digitais. Este trabalho propõe uma estratégia de controle em redes Fieldbus usando controle adaptativo por modelo de referência através de variáveis de estado, para resolver sistemas complexos. O algoritmo de controle proposto é composto por um observador de estados trabalhando em conjunto com um esquema de controle adaptativo por modelo de referência. As malhas de controle no Fieldbus consistem em um conjunto de blocos funcionais padrões existentes, conectados aos novos blocos funcionais propostos e desenvolvidos de acordo com as especificações da norma Fieldbus Foundation. Este algoritmo de controle realiza os cálculos de maneira distribuída entre os dispositivos da rede Fieldbus, implicando em várias vantagens, tais como: a) perda do supervisório não implica na perda do algoritmo de controle, b) menor tráfego de dados na rede, c) algoritmo de controle que não depende do tempo de processamento do computador. Os resultados dos testes obtidos são apresentados e demonstraram um alto grau de precisão, destacando-se a estabilidade e aplicabilidade do algoritmo proposto. / The rising complexity of the industrial control processes has been claiming more and more accurate, reliable and versatile control systems. Attempting to satisfy this demand, several control and estimation systems algorithms, such as adaptive and predictive techniques, parametric estimation, Kalman filtering, state observation, have been designed, simulated and implemented with relative easiness in the modern digital systems. This work proposes a new control strategy in Fieldbus networks, using adaptive control techniques through state variables, to solve complex systems. The proposed control algorithm is based on an states observer concurrently working with a reference model adaptive control scheme. The modified Fieldbus network consists of a set of standard function blocks, connected to the proposed new function blocks. These new function blocks comply with the specifications of the Fieldbus Foundation norm. This control algorithm accomplishes its calculation in a distributed way among the fieldbus devices. This operating feature results in some advantages, such as: any failure in the supervisory system does not imply in the loss of the control algorithm, b) a lower data transmission in the network, c) control algorithm that does not depend on the processing time of the computer. The obtained results corroborate with the expected advantages of the proposed algorithm, in terms of high degree of accuracy, stability and applicability.

The application of Model Reference Adaptive Control (MRAC) for train dynamic braking is investigated in order to control dynamic braking forces while remaining within the allowable adhesion and coupler forces.  This control method can accurately determine the train braking distance.  One of the critical factors in Positive Train Control (PTC) is accurately estimating train braking distance under different operating conditions.  Accurate estimation of the braking distance will allow trains to be spaced closer together, with reasonable confidence that they will stop without causing a collision.  This study develops a dynamic model of a train consist based on a multibody formulation of railcars, trucks (bogies), and suspensions.   The study includes the derivation of the mathematical model and the results of a numerical study in Matlab.  A three-railcar model is used for performing a parametric study to evaluate how various elements will affect the train stopping distance from an initial speed.  Parameters that can be varied in the model include initial train speed, railcar weight, wheel-rail interface condition, and dynamic braking force.  Other parameters included in the model are aerodynamic drag forces and air brake forces. An MRAC system is developed to control the amount of current through traction motors under various wheel/rail adhesion conditions while braking.  Minimizing the braking distance of a train requires the dynamic braking forces to be maximized within the available wheel/rail adhesion.  Excessively large dynamic braking can cause wheel lockup that can damage the wheels and rail.  Excessive braking forces can also cause large buff loads at the couplers.  For DC traction motors, an MRAC system is used to control the current supplied to the traction motors.  This motor current is directly proportional to the dynamic braking force.  In addition, the MRAC system is also used to control the train speed by controlling the synchronous speed of the AC traction motors.  The goal of both control systems for DC and AC traction motors is to apply maximum available dynamic braking while avoiding wheel lockup and high coupler forces.  The results of the study indicate that the MRAC system significantly improves braking distance while maintaining better wheel/rail adhesion and coupler dynamics during braking.  Furthermore, according to this study, the braking distance can be accurately estimated when MRAC is used.  The robustness of the MRAC system with respect to different parameters is investigated, and the results show an acceptable robust response behavior. / Ph. D.

High blood pressure, also called hypertension is one of the most common worldwide diseases afflicting humans and is a major risk factor for stroke, myocardial infarction, vascular disease, and chronic kidney disease. If blood pressure is controlled and oscillations in the hemodynamic variables are reduced, patients experience fewer complications after surgery. In clinical practice, this is usually achieved using manual drug delivery. Given that different patients have different sensitivity and reaction time to drugs, determining manually the right drug infusion rates may be difficult. This is a problem where automatic drug delivery can provide a solution, especially if it is designed to adapt to variations in the patient’s conditions. This research work presents an investigation into the development of abnormal blood pressure (hypertension) controllers for postoperative patients. Control of the drugs infusion rates is used to simultaneously regulate the hemodynamic variables such as the Mean Arterial Pressure (MAP) and the Cardiac Output (CO) at the desired level. The implementation of optimal control system is very essential to improve the quality of patient care and also to reduce the workload of healthcare staff and costs. Many researchers have conducted studies earlier on modelling and/or control of abnormal blood pressure for postoperative patients. However, there are still many concerns about smooth transition of blood pressure without any side effect. The blood pressure is classified in two categories: high blood pressure (Hypertension) and low blood pressure (Hypotension). The hypertension often occurred after cardiac surgery, and the hypotension occurred during cardiac surgery. To achieve the optimal control solution for these abnormal blood pressures, many methods are proposed, one of the common methods is infusing the drug related to blood pressure to maintain it at the desired level. There are several kinds of vasodilating drugs such as Sodium Nitroprusside (SNP), Dopamine (DPM), Nitro-glycerine (NTG), and so on, which can be used to treat postoperative patients, also used for hypertensive emergencies to keep the blood pressure at safety level. A comparative performance of two types of algorithms has been presented in chapter four. These include the Internal Model Control (IMC), and Proportional-Integral-Derivative (PID) controller. The resulting controllers are implemented, tested and verified for three sensitivity patient response. SNP is used for all three patients’ situation in order to reduce the pressure smoothly and maintain it at the desire level. A Genetic Algorithms (GAs) optimization technique has been implemented to optimise the controllers’ parameters. A set of experiments are presented to demonstrate the merits and capabilities of the control algorithms. The simulation results in chapter four have demonstrated that the performance criteria are satisfied with the IMC, and PID controllers. On the other hand, the settling time for the PID control of all three patients’ response is shorter than the settling time with IMC controller. Using multiple interacting drugs to control both the MAP and CO of patients with different sensitivity to drugs is a challenging task. A Multivariable Model Reference Adaptive Control (MMRAC) algorithm is developed using a two-input, two-output patient model. Because of the difference in patient’s sensitivity to the drug, and in order to cover the wide ranges of patients, Model Reference Adaptive Control (MRAC) has been implemented to obtain the optimal infusion rates of DPM and SNP. This is developed in chapters five and six. Computer simulations were carried out to investigate the performance of this controller. The results show that the proposed adaptive scheme is robust with respect to disturbances and variations in model parameters, the simulation results have demonstrated that this algorithm cannot cover the wide range of patient’s sensitivity to drugs, due to that shortcoming, a PID controller using a Neural Network that tunes the controller parameters was designed and implemented. The parameters of the PID controller were optimised offline using Matlab genetic algorithm. The proposed Neuro-PID controller has been tested and validated to demonstrate its merits and capabilities compared to the existing approaches to cover wide range of patients.

iv ABSTRACT ADAPTIVE CONTROL OF GUIDED MISSILES Tiryaki Kutluay, Kadriye Ph.D., Department of Aerospace Engineering Supervisor: Asst. Prof. Dr. Ilkay Yavrucuk February 2011, 147 Pages This thesis presents applications and an analysis of various adaptive control augmentation schemes to various baseline flight control systems of an air to ground guided missile. The missile model used in this research has aerodynamic control surfaces on its tail section. The missile is desired to make skid to turn maneuvers by following acceleration commands in the pitch and yaw axis, and by keeping zero roll attitude. First, a linear quadratic regulator baseline autopilot is designed for the control of the missile acceleration in pitch axis at a single point in the flight envelope. This baseline autopilot is then augmented with a Direct Model Reference Adaptive Control (DMRAC) scheme using Neural Networks for parameter estimation, and an L1 Adaptive Control scheme. Using the linearized longitudinal model of the missile at the design point, simulations are performed to analyze and demonstrate the performance of the two adaptive augmentation schemes. By injecting uncertainties to the plant model, the effects of adaptive augmentations on the linear baseline autopilot are examined. v Secondly, a high fidelity simulation software of the missile is used in order to analyze the performance of the adaptive augmentations in 6 DoF nonlinear flight simulations. For the control of the missile in three axis, baseline autopilots are designed using dynamic inversion at a single point in the flight envelope. A linearizing transformation is employed during the inversion process. These coarsely designed baseline autopilots are augmented with L1 adaptive control elements. The performance of the adaptive control augmentation system is tested in the presence of perturbations in the aerodynamic model and increase in input gain, and the simulation results are presented.

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High blood pressure, also called hypertension is one of the most common worldwide diseases afflicting humans and is a major risk factor for stroke, myocardial infarction, vascular disease, and chronic kidney disease. If blood pressure is controlled and oscillations in the hemodynamic variables are reduced, patients experience fewer complications after surgery. In clinical practice, this is usually achieved using manual drug delivery. Given that different patients have different sensitivity and reaction time to drugs, determining manually the right drug infusion rates may be difficult. This is a problem where automatic drug delivery can provide a solution, especially if it is designed to adapt to variations in the patient’s conditions. This research work presents an investigation into the development of abnormal blood pressure (hypertension) controllers for postoperative patients. Control of the drugs infusion rates is used to simultaneously regulate the hemodynamic variables such as the Mean Arterial Pressure (MAP) and the Cardiac Output (CO) at the desired level. The implementation of optimal control system is very essential to improve the quality of patient care and also to reduce the workload of healthcare staff and costs. Many researchers have conducted studies earlier on modelling and/or control of abnormal blood pressure for postoperative patients. However, there are still many concerns about smooth transition of blood pressure without any side effect. The blood pressure is classified in two categories: high blood pressure (Hypertension) and low blood pressure (Hypotension). The hypertension often occurred after cardiac surgery, and the hypotension occurred during cardiac surgery. To achieve the optimal control solution for these abnormal blood pressures, many methods are proposed, one of the common methods is infusing the drug related to blood pressure to maintain it at the desired level. There are several kinds of vasodilating drugs such as Sodium Nitroprusside (SNP), Dopamine (DPM), Nitro-glycerine (NTG), and so on, which can be used to treat postoperative patients, also used for hypertensive emergencies to keep the blood pressure at safety level. A comparative performance of two types of algorithms has been presented in chapter four. These include the Internal Model Control (IMC), and Proportional-Integral-Derivative (PID) controller. The resulting controllers are implemented, tested and verified for three sensitivity patient response. SNP is used for all three patients’ situation in order to reduce the pressure smoothly and maintain it at the desire level. A Genetic Algorithms (GAs) optimization technique has been implemented to optimise the controllers’ parameters. A set of experiments are presented to demonstrate the merits and capabilities of the control algorithms. The simulation results in chapter four have demonstrated that the performance criteria are satisfied with the IMC, and PID controllers. On the other hand, the settling time for the PID control of all three patients’ response is shorter than the settling time with IMC controller. Using multiple interacting drugs to control both the MAP and CO of patients with different sensitivity to drugs is a challenging task. A Multivariable Model Reference Adaptive Control (MMRAC) algorithm is developed using a two-input, two-output patient model. Because of the difference in patient’s sensitivity to the drug, and in order to cover the wide ranges of patients, Model Reference Adaptive Control (MRAC) has been implemented to obtain the optimal infusion rates of DPM and SNP. This is developed in chapters five and six. Computer simulations were carried out to investigate the performance of this controller. The results show that the proposed adaptive scheme is robust with respect to disturbances and variations in model parameters, the simulation results have demonstrated that this algorithm cannot cover the wide range of patient’s sensitivity to drugs, due to that shortcoming, a PID controller using a Neural Network that tunes the controller parameters was designed and implemented. The parameters of the PID controller were optimised offline using Matlab genetic algorithm. The proposed Neuro-PID controller has been tested and validated to demonstrate its merits and capabilities compared to the existing approaches to cover wide range of patients. / Libyan Ministry of Higher Education scholarship

This thesis presents the initial components of an integrated guidance, navigation, and control system for vertical take-off and landing (VTOL) autonomous unmanned aerial vehicles (UAVs) such that they may map complex environments that may be hostile. The first part of this thesis presents an autonomous guidance system. For goal selection, the map is partitioned around the presence of obstacles and whether that area has been explored. To perform this partitioning, the Octree algorithm is implemented. In this thesis, we test this algorithm to find a parameter set that optimizes this algorithm. Having selected goal points, we perform a comparison of the LPA* and A* path planning algorithms with a custom heuristic that enables reckless or tactical maneuvers as the UAV maps the environment. For trajectory planning, the fMPC algorithm is applied to the feedback-linearized equations of motion of a quadcopter. For collision avoidance, standalone versions of 4 different constraint generation algorithms are evaluated to compare their resulting computation times, accuracy, and computed volume on a voxel map that simulates a 2-story house along with fixed paths that vary in length at fixed intervals as basis of tests. The second part of this thesis presents the theory of Model Reference Adaptive Control(MRAC) along with augmentation for output signal tracking and switched-dynamic systems. We then detail the development of longitudinal and lateral controllers a Quad-Rotor Tailsitter(QRBP) style UAV. In order to successfully implement the proposed controller on the QRBP, significant effort was placed upon physical design and testing apparatus. / Master of Science / For an autonomously operated, Unmanned Aerial Vehicle (UAV), to operate, it requires a guidance system to determine where and how to go, and a control system to effectively actuate the guidance system's commands. In this thesis, we detail the characterization and optimization of the algorithms comprising the guidance system. We then delve into the theory of MRAC and apply it toward a control system for a QRBP. We then detail additional tools developed to support the testing of the QRBP.

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior / This work proposes two vector control schemes for permanent magnet synchronous motors. They are destined to speed and position control, respectively, and are based on a control law called VS-RMRAC. Not being yet applied to the electric machines control, the VS-RMRAC control law presents robustness features that are potentially advantageous from the point of view of the closed loop PMSM dynamics. It also presents well established design and robust stability conditions, which makes its digital implementation easier. Both control structures are described and its respective design methods are presented. From simulation results, the behavior and performance of both structures are analyzed in face of load disturbances and parameter uncertainties. The speed control scheme and its simulation results are validated experimentally. This scheme is digitally implemented with fixed-point arithmetic using a TMS320F2812 DSP. Both schemes with its potentialities and limitations are then discussed. / Este trabalho propõe duas estratégias de controle vetorial para motores síncronos a ímãs permanentes (MSIP s). Destinam-se ao controle de velocidade e de posição, respectivamente, e são baseados em uma lei de controle chamada VS-RMRAC. Não tendo sido aplicado ainda ao controle de máquinas elétricas, a lei de controle VS-RMRAC apresenta características de robustez que são potencialmente vantajosas do ponto de vista da dinâmica em malha fechada de MSIP s. Também apresenta condições de projeto e estabilidade robusta bem estabelecidas para o tempo discreto, o que facilita sua implementação digital. Ambas as estruturas de controle são descritas e suas respectivas metodologias de projeto são apresentadas. A partir de resultados de simulação, o comportamento e desempenho de ambas são analisados frente a perturbações de carga e incertezas paramétricas. O esquema de controle de velocidade e seus resultados de simulação são validados experimentalmente. Este esquema é implementado digitalmente com aritmética de ponto fixo utilizando DSP TMS320F2812. As potencialidades e limitações de ambos os esquemas são, por fim, discutidos.