Este trabalho propõe a utilização de uma técnica de controle PID adaptativo gaussiano (GAPID) com o objetivo de aumentar o desempenho do controle PID tradicional aplicado a um conversor Buck. A função gaussiana utilizada para definir os ganhos adaptativos tem características como: é uma função suave e com derivadas suaves, possui limites inferior e superior bem definidos e possui concavidade ajustável. Pelo fato de ser uma função suave, ajuda a evitar problemas relacionados às transições abruptas dos ganhos, comumente encontradas em outros métodos adaptativos. Entretanto, não há uma metodologia algébrica para obter os ganhos adaptativos, visto que originalmente o conjunto de parâmetros do GAPID é composto por oito elementos. Para tanto, utilizaram-se técnicas como otimização através de metaheurísticas bio-inspiradas, métricas avaliativas de desempenho, alteração no método de obtenção do tempo de acomodação, com o objetivo de aumentar o desempenho deste controlador (GAPID) e obter os ganhos adaptativos. O uso do conjunto de oito elementos na otimização gerou soluções ótimas, porém, muito especializadas, levando o controlador a não se comportar bem quando as condições de operação mudam. Desta forma, é proposto um vínculo entre os parâmetros não lineares das curvas gaussianas com os parâmetros lineares do controlador PID, que demonstrou gerar soluções quase tão boas quanto com parâmetros livres e menos especializadas, com comportamento mais homogêneo face a mudanças no ponto de operação do controlador e trazendo como principal vantagem a utilização dos mesmos requisitos de projeto do PID tradicional, o que facilitaria a migração do controlador PID para o GAPID dentro da maioria das indústrias. Os resultados obtidos, tanto na simulação quanto no protótipo foram semelhantes. Isso se deve ao cuidado com a modelagem e o rigor nos procedimentos de projeto, implementados da mesma forma no modelo e no protótipo. / This work proposes the use of a Gaussian adaptive PID control technique (GAPID) in order to increase the performance of the traditional PID control applied to a Buck converter. The Gaussian function used to define adaptive gains has characteristics such as; it is a smooth function with smooth derivatives, it has well defined lower and upper bounded and it has the adjustable concavity. Because it is a smooth function, it helps avoid problems related to abrupt gains transition, commonly found in other adaptive methods. However, there is no algebraic methodology to obtain the adaptive gains, since originally the GAPID parameter set consists of eight elements. Therefore, was used techniques such as optimization through bio-inspired metaheuristics, performance evaluation metrics, and change in the method to obtaining the settling-time, in order to increase the performance of this controller (GAPID) and obtain the adaptive gains. The use of the eight elements in the optimization generated optimized but very specialized solutions, causing the controller not to behave well when the operating conditions change. In this way, a link between the nonlinear parameters of the gaussian curves and the linear parameters of the PID controller was proposed, which demonstrated to generate solutions almost as good as with free and less specialized parameters, with a more homogeneous behavior in relation to changes in the operating point of the controller and bringing as a main advantage the use of the same traditional PID design requirements, which would facilitate the migration of PID controller to GAPID within most industries. The results obtained in both the simulation and the prototype were similar. This is due to careful modeling and rigor in design procedures, implemented in the same way in the model and the prototype.

Este trabalho propõe a utilização de uma técnica de controle PID adaptativo gaussiano (GAPID) com o objetivo de aumentar o desempenho do controle PID tradicional aplicado a um conversor Buck. A função gaussiana utilizada para definir os ganhos adaptativos tem características como: é uma função suave e com derivadas suaves, possui limites inferior e superior bem definidos e possui concavidade ajustável. Pelo fato de ser uma função suave, ajuda a evitar problemas relacionados às transições abruptas dos ganhos, comumente encontradas em outros métodos adaptativos. Entretanto, não há uma metodologia algébrica para obter os ganhos adaptativos, visto que originalmente o conjunto de parâmetros do GAPID é composto por oito elementos. Para tanto, utilizaram-se técnicas como otimização através de metaheurísticas bio-inspiradas, métricas avaliativas de desempenho, alteração no método de obtenção do tempo de acomodação, com o objetivo de aumentar o desempenho deste controlador (GAPID) e obter os ganhos adaptativos. O uso do conjunto de oito elementos na otimização gerou soluções ótimas, porém, muito especializadas, levando o controlador a não se comportar bem quando as condições de operação mudam. Desta forma, é proposto um vínculo entre os parâmetros não lineares das curvas gaussianas com os parâmetros lineares do controlador PID, que demonstrou gerar soluções quase tão boas quanto com parâmetros livres e menos especializadas, com comportamento mais homogêneo face a mudanças no ponto de operação do controlador e trazendo como principal vantagem a utilização dos mesmos requisitos de projeto do PID tradicional, o que facilitaria a migração do controlador PID para o GAPID dentro da maioria das indústrias. Os resultados obtidos, tanto na simulação quanto no protótipo foram semelhantes. Isso se deve ao cuidado com a modelagem e o rigor nos procedimentos de projeto, implementados da mesma forma no modelo e no protótipo. / This work proposes the use of a Gaussian adaptive PID control technique (GAPID) in order to increase the performance of the traditional PID control applied to a Buck converter. The Gaussian function used to define adaptive gains has characteristics such as; it is a smooth function with smooth derivatives, it has well defined lower and upper bounded and it has the adjustable concavity. Because it is a smooth function, it helps avoid problems related to abrupt gains transition, commonly found in other adaptive methods. However, there is no algebraic methodology to obtain the adaptive gains, since originally the GAPID parameter set consists of eight elements. Therefore, was used techniques such as optimization through bio-inspired metaheuristics, performance evaluation metrics, and change in the method to obtaining the settling-time, in order to increase the performance of this controller (GAPID) and obtain the adaptive gains. The use of the eight elements in the optimization generated optimized but very specialized solutions, causing the controller not to behave well when the operating conditions change. In this way, a link between the nonlinear parameters of the gaussian curves and the linear parameters of the PID controller was proposed, which demonstrated to generate solutions almost as good as with free and less specialized parameters, with a more homogeneous behavior in relation to changes in the operating point of the controller and bringing as a main advantage the use of the same traditional PID design requirements, which would facilitate the migration of PID controller to GAPID within most industries. The results obtained in both the simulation and the prototype were similar. This is due to careful modeling and rigor in design procedures, implemented in the same way in the model and the prototype.

O objetivo deste trabalho é desenvolver um sistema de controle capaz de realizar o apontamento automático de uma antena parabólica de forma mais precisa e com menor tempo de apontamento quando comparado ao apontamento manual. A antena parabólica em estudo consta de uma parábola metálica de 1.60 m de diâmetro, base de sustentação em ferro, dois conjuntos de engrenagens e dois motores elétricos para realização dos movimentos. Os parâmetros físicos do sistema mecânico, tais como massa, volume e inércia, puderam ser facilmente obtidos a partir de uma modelagem tridimensional em um software de plataforma CAD. Para a modelagem dinâmica do sistema utilizou-se a similaridade do sistema físico em estudo com um manipulador de cadeia aberta de dois graus de liberdade o que permitiu que se aplicassem conceitos referentes a cinemática e modelagem de manipuladores robóticos. Através da notação de Denavit-Hartenberg a cinemática direta da antena com dois graus de liberdade foi obtida com sucesso. As equações dinâmicas que descrevem o movimento do sistema foram levantadas através de um modelador automático implementado em um software de manipulação simbólica. Para tanto foi desenvolvido um algoritmo que descreve os passos necessários para obtenção das equações de movimento de um manipulador robótico em cadeia aberta, a partir da formulação Lagrangeana. Um sistema de controle adaptativo por modelo de referência foi projetado e implementado considerando as incertezas do modelo oriundas de imperfeições contidas na modelagem tridimensional realizada. Os resultados obtidos por simulação do sistema de controle adaptativo se mostraram satisfatórios e os índices de desempenho esperados para um perfeito apontamento foram alcançados. / The objective of this work is to develop a control system capable of performing the automatic maneuver of a satellite dish more accurately with less time maneuvering when compared to manual maneuver. The dish consists of a study on metal parabola 1.60 m in diameter, base of support in iron, two sets of gears and two electric motors to perform the movements. The physical parameters of the mechanical system, such as mass, volume and inertia could be easily obtained from a three-dimensional modeling in a CAD software platform. For modeling the system dynamics we used the similarity of the physical system under study with an open chain manipulator of two degrees of freedom that allowed it to apply concepts related to kinematics and modeling of robotic manipulators. Through the Denavit-Hartenberg notation of the direct kinematics of the antenna with two degrees of freedom was successfully obtained. The dynamic equations describing the motion of the system were raised through an automatic model implemented in symbolic manipulation software. To that end, an algorithm that describes the steps necessary to obtain the equations of motion of a robotic manipulator in open chain, from the Lagrangian method, was developed. A model reference adaptive control system was designed and implemented considering the uncertainties of the model arising from imperfections within the three-dimensional modeling. The results obtained by simulation of the system of closed loop control were satisfactory as well as the high rates of the perfect maneuver have been achieved.

We extend the study of learning and generalization in feed forward Boolean networks to random Boolean networks (RBNs). We explore the relationship between the learning capability and the network topology, the system size, the training sample size, and the complexity of the computational tasks. We show experimentally that there exists a critical connectivity Kc that improves the generalization and adaptation in networks. In addition, we show that in finite size networks, the critical K is a power-law function of the system size N and the fraction of inputs used during the training. We explain why adaptation improves at this critical connectivity by showing that the network ensemble manifests maximal topological diversity near Kc. Our work is partly motivated by self-assembled molecular and nanoscale electronics. Our findings allow to determine an automata network topology class for efficient and robust information processing.

Adaptive control designs using neural networks (NNs) based on dynamic inversion are investigated for aerospace vehicles which are operated at highly nonlinear dynamic regimes. NNs play a key role as the principal element of adaptation to approximately cancel the effect of inversion error, which subsequently improves robustness to parametric uncertainty and unmodeled dynamics in nonlinear regimes. An adaptive control scheme previously named composite model reference adaptive control is further developed so that it can be applied to multi-input multi-output output feedback dynamic inversion. It can have adaptive elements in both the dynamic compensator (linear controller) part and/or in the conventional adaptive controller part, also utilizing state estimation information for NN adaptation. This methodology has more flexibility and thus hopefully greater potential than conventional adaptive designs for adaptive flight control in highly nonlinear flight regimes. The stability of the control system is proved through Lyapunov theorems, and validated with simulations. The control designs in this thesis also include the use of pseudo-control hedging techniques which are introduced to prevent the NNs from attempting to adapt to various actuation nonlinearities such as actuator position and rate saturations. Control allocation is introduced for the case of redundant control effectors including thrust vectoring nozzles. A thorough comparison study of conventional and NN-based adaptive designs for a system under a limit cycle, wing-rock, is included in this research, and the NN-based adaptive control designs demonstrate their performances for two highly maneuverable aerial vehicles, NASA F-15 ACTIVE and FQM-117B unmanned aerial vehicle (UAV), operated under various nonlinearities and uncertainties.

The role of vision as an additional sensing mechanism has received a lot of attention in recent years in the context of autonomous flight applications. Modern Unmanned Aerial Vehicles (UAVs) are equipped with vision sensors because of their light-weight, low-cost characteristics and also their ability to provide a rich variety of information of the environment in which the UAVs are navigating in. The problem of vision based autonomous flight is very difficult and challenging since it requires bringing together concepts from image processing and computer vision, target tracking and state estimation, and flight guidance and control. This thesis focuses on the adaptive state estimation, guidance and control problems involved in vision-based formation flight. Specifically, the thesis presents a composite adaptation approach to the partial state estimation of a class of nonlinear systems with unmodeled dynamics. In this approach, a linear time-varying Kalman filter is the nominal state estimator which is augmented by the output of an adaptive neural network (NN) that is trained with two error signals. The benefit of the proposed approach is in its faster and more accurate adaptation to the modeling errors over a conventional approach. The thesis also presents two approaches to the design of adaptive guidance and control (G&C) laws for line-of-sight formation flight. In the first approach, the guidance and autopilot systems are designed separately and then combined together by assuming time-scale separation. The second approach is based on integrating the guidance and autopilot design process. The developed G&C laws using both approaches are adaptive to unmodeled leader aircraft acceleration and to own aircraft aerodynamic uncertainties. The thesis also presents theoretical justification based on Lyapunov-like stability analysis for integrating the adaptive state estimation and adaptive G&C designs. All the developed designs are validated in nonlinear, 6DOF fixed-wing aircraft simulations. Finally, the thesis presents a decentralized coordination strategy for vision-based multiple-aircraft formation control. In this approach, each aircraft in formation regulates range from up to two nearest neighboring aircraft while simultaneously tracking nominal desired trajectories common to all aircraft and avoiding static obstacles.

The objective of this thesis is to design a relative navigation and guidance system for unmanned aerial vehicles (UAVs) for vision-based control applications. The vision-based navigation, guidance and control has been one of the most focused on research topics for the automation of UAVs. This is because in nature, birds and insects use vision as the exclusive sensor for object detection and navigation. In particular, this thesis studies the monocular vision-based navigation and guidance. Since 2-D vision-based measurements are nonlinear with respect to the 3-D relative states, an extended Kalman filter (EKF) is applied in the navigation system design. The EKF-based navigation system is integrated with a real-time image processing algorithm and is tested in simulations and flight tests. The first closed-loop vision-based formation flight has been achieved. In addition, vision-based 3-D terrain recovery was performed in simulations. A vision-based obstacle avoidance problem is specially addressed in this thesis. A navigation and guidance system is designed for a UAV to achieve a mission of waypoint tracking while avoiding unforeseen stationary obstacles by using vision information. A 3-D collision criterion is established by using a collision-cone approach. A minimum-effort guidance (MEG) law is applied for a guidance design, and it is shown that the control effort can be reduced by using the MEG-based guidance instead of a conventional guidance law. The system is evaluated in a 6 DoF flight simulation and also in a flight test. For monocular vision-based control problems, vision-based estimation performance highly depends on the relative motion of the vehicle with respect to the target. Therefore, this thesis aims to derive an optimal guidance law to achieve a given mission under the condition of using the EKF-based relative navigation. Stochastic optimization is formulated to minimize the expected cost including the guidance error and the control effort. A suboptimal guidance law is derived based on an idea of the one-step-ahead (OSA) optimization. Simulation results show that the suggested guidance law significantly improves the guidance performance. Furthermore, the OSA optimization is generalized as the n-step-ahead optimization for an arbitrary number of n, and their optimality and computational cost are investigated.

Real-Time Rollover prevention of Unmanned Ground Vehicle (UGV) is very paramount to its reliability and survivability mostly when operating on unknown and rough terrains like mines or other planets.Therefore this research presents the method of real-time rollover prevention of UGVs making use of Adaptive control techniques based on Recursive least Squares (RLS) estimation of unknown parameters, in order to enable the UGVs to adapt to unknown hush terrains thereby increasing their reliability and survivability. The adaptation is achieved by using indirect adaptive control technique where the controller parameters are computed in real time based on the online estimation of the plant’s (UGV) parameters (Rollover index and Roll Angle) and desired UGV’s performance in order to appropriately adjust the UGV speed and suspension actuators to counter-act the vehicle rollover. A great challenge of indirect adaptive control system is online parameter identification, where in this case the RLS based estimator is used to estimate the vehicles rollover index and Roll Angle from lateral acceleration measurements and height of the centre of gravity of the UGV. RLS is suitable for online parameter identification due to its nature of updating parameter estimate at each sample time. The performance of the adaptive control algorithms and techniques is evaluated using Matlab Simulink® system model with the UGV Model built using SimMechanics physical modelling platform and the whole system runs within Simulink environment to emulate real world application. The simulation results of the proposed adaptive control algorithm based on RLS estimation, show that the adaptive control algorithm does prevent or minimize the likely hood of vehicle rollover in real time. / Electrical and Mining Engineering / M. Tech. (Electrical Engineering)

Indiana University-Purdue University Indianapolis (IUPUI) / A gradient free function optimization technique, namely particle swarm optimization (PSO) algorithm, is utilized in parameter identification of the electrochemical model of a Lithium-Ion battery having a LiCoO2 chemistry. Battery electrochemical model parameters are subject to change under severe or abusive operating conditions resulting in, for example, Navy over-discharged battery, 24-hr over-discharged battery, and over-charged battery. It is important for a battery management system to have these parameters changes fully captured in a bank of battery models that can be used to monitor battery conditions in real time. In this work, PSO methodology has been used to identify four electrochemical model parameters that exhibit significant variations under severe operating conditions. The identified battery models were validated by comparing the model output voltage with the experimental output voltage for the stated operating conditions. These identified conditions of the battery were then used to monitor condition of the battery that can aid the battery management system (BMS) in improving overall performance. An adaptive estimation technique, namely multiple model adaptive estimation (MMAE) method, was implemented for this purpose. In this estimation algorithm, all the identified models were simulated for a battery current input profile extracted from the hybrid pulse power characterization (HPPC) cycle simulation of a hybrid electric vehicle (HEV). A partial differential algebraic equation (PDAE) observer was utilized to obtain the estimated voltage, which was used to generate the residuals. Analysis of these residuals through MMAE provided the probability of matching the current battery operating condition to that of one of the identified models. Simulation results show that the proposed model based method offered an accurate and effective fault diagnosis of the battery conditions. This type of fault diagnosis, which is based on the models capturing true physics of the battery electrochemistry, can lead to a more accurate and robust battery fault diagnosis and help BMS take appropriate steps to prevent battery operation in any of the stated severe or abusive conditions.

We consider the problem of Adaptive Control of finite-dimensional, stable, Linear Time Invariant (LTI) plants. Amongst such plants, the subclass regarding which an upper bound on the order is not known or which are known to be nonminimum phase (zeros in the unstable region) pose formidable problems in their own right. On one hand, if an upper bound on the order of the plant is not known, adaptive control usually involves some form of order estimation. On the other hand, when the plant is allowed to be either minimum phase or nonminimum phase, the adaptive control problem, as is well-known, becomes considerably-less tractable. In this study, the class of unknown plants considered is such that no information is available on the upper bound of the plant order and, further, the plant may be either minimum phase or nonminimum phase. Albeit known to be stable, such plants throw myriads of challenges in the context of adaptive control. Adaptive control involving such plants has been addressed [79] in a Model Reference Adaptive Control (MRAC) framework. There, the inputs and outputs of the unknown plant are the only quantities available by measurement in terms of which any form of modeling of the unknown plant may be made. Inputs to the reference model have been taken from certain restricted classes of bounded signals. In particular, the three classes of inputs considered are piecewise continuous bounded functions which asymptotically approach • a nonzero constant, • a sinusoid, and • a sinusoid with a nonzero shift. Moreover, the control law is such that adaptation is carried out at specific instants separated by progressively larger intervals of time. The schemes there have been proved to be e-optimal in the sense of a suitably formulated optimality criterion. If, however, the reference model inputs be extended to the class of piecewise continuous bounded functions, that would compound the complexity of the adaptive control problem. Only one attempt [78] in adaptive control in such a setting has come to our notice. The problem there has been tackled by an application of the theory of Pade Approximations to time moments of an LTI system. Based on a time moments estimation procedure, a simple adaptive scheme for Single-Input Single-Output (SISO) systems with only a cascade compensator has been reported. The first chapter is essentially meant to ensure that the problem we seek to address in the field of adaptive control indeed has scope for research. Having defined Adaptive Control, we selectively scan through the literature on LTI systems, with focus on MRAC. We look out in particular for studies involving plants of which not much is known regarding their order and systems which are possibly nonminimum phase. We found no evidence to assert that the problem of adaptive control of stable LTI systems, not necessarily minimum phase and of unknown upper bound on the order, was explored enough, save two attempts involving SISO systems. Taking absence of evidence (of in-depth study) for evidence of absence, we make a case for the problem and formally state it. We preview the thesis. We set two targets before us in Chapter 2. The first is to review one of the existing procedures attacking the problem we intend to address. Since the approach is based on the notion of time moments of an LTI system, and as we are to employ Pade Approximations as a tool, we uncover these concepts to the limited extent of our requirement. The adaptive procedure, Plant Command Modifier Scheme (PCMS) [78], for SISO plants is reported in some detail. It stands supported on an algorithm specially designed to estimate the time moments of an LTI system given no more than its input and output. Model following there has been sought to be achieved by matching the first few time moments of the reference model by the corresponding ones of the overall compensated plant. The plant time moment estimates have been taken to represent the unknown plant. The second of the goals is to analyze PCMS critically so that it may serve as a forerunner to our work. We conclude the chapter after accomplishing these goals. In Chapter 3, we devise a time moment estimator for SISO systems from a perspective which is conceptually equivalent to, yet functionally different from, that appropriated in [78]. It is a recipe to obtain estimates of time moments of a system by computing time moment estimates of system input and output signals measured up to current time. Pade approximations come by handy for this purpose. The lacunae exposed by a critical examination of PCMS in Chapter 2 guide us to progressively refine the estimator. Infirmities in the control part of PCMS too have come to light on our probing into it. A few of these will be fixed by way of fabricating two exclusively cascade compensators. We encounter some more issues, traceable to the estimator, which need redressal. Instead of directly fine-tuning the estimator itself, as is the norm, we propose the idea of 'estimating' the lopsidedness of the estimator by using it on the fully known reference model. This will enable us to effect corrections and obtain admissible estimates. Next, we explore the possibility of incorporating feedback compensation in addition to the existing cascade compensation. With output error minimization in mind, we come up with three schemes in this category. In the process, we anticipate the risk of instability due to feedback and handle it by means of an instability preventer with an inbuilt instability detector. Extensive simulations with minimum and rionminimum phase unknown plants employing the various schemes proposed are presented. A systematic study of simulation results reveals a dyad of hierarchies of progressively enhanced overall performance. One is in the sequence of the proposed schemes and the other in going for matching more and more moments. Based on our experiments we pick one of the feedback schemes as the best. Chapter 4 is conceived of as a bridge between SISO and multivariable systems. A transition from SISO to Multi-Input Multi-Output (MIMO) adaptive control is not a proposition confined to the mathematics of dimension-enhancement. A descent from the MIMO to the SISO case is expected to be relatively simple, though. So to transit as smoothly and gracefully as possible, some issues have to be placed in perspective before exploring multivariable systems. We succinctly debate on the efforts in pursuit of the exact vis-a-vis the accurate, and their implications. We then set some notations and formulate certain results which serve to unify and simplify the development in the subsequent three chapters. We list a few standard results from matrix theory which are to be of frequent use in handling multivariable systems. We derive control laws for Single-Input Multi-Output (SIMO) systems in Chapter 5. Expectedly, SIMO systems display traits of observability and uncontrollability. Results of illustrative simulations are furnished. In Chapter 6, we formulate control laws for Multi-Input Single-Output (MISO) systems. Characteristics of unobservability and controllability stand out there. We present case studies. Before actually setting foot onto MIMO systems, we venture to conjecture on what to expect there. We work out all the cascade and feedback adaptive schemes for square and nonsquare MIMO systems in Chapter 7. We show that MIMO laws when projected to MISO, SIMO and SISO cases agree with the corresponding laws in the respective cases. Thus the generality of our treatment of MIMO systems over other multivariable and scalar systems is established. We report simulations of instances depicting satisfactory performance and highlight the limitations of the schemes in tackling the family of plants of unknown upper bound on the order and possibly nonminimum phase. This forms the culmination of our exercise which took off from the reported work involving SISO systems [78]. Up to the end of the 7th chapter, we are in pursuit of solutions for the problem as general as in §1.4. For SISO systems, with input restrictions, the problem has been addressed in [79]. The laws proposed there carry out adaptation only at certain discrete instants; with respect to a suitably chosen cost, the final laws are proved to be e>optimal. In Chapter 8, aided by initial suboptimal control laws, we finally devise two algorithms with continuous-time adaptation and prove their optimality. Simulations with minimum and nonminimum phase plants reveal the effectiveness of the various laws, besides throwing light on the bootstrapping and auto-rectifying features of the algorithms. In the tail-piece, we summarize the work and wind up matters reserved for later deliberation. As we critically review the present work, we decant the take-home message. A short note on applications followed by some loud thinking as a spin-off of this report will take us to finis.