Applying a genetic neuro-model reference adaptive controller in drilling optimization

Motivated by rising drilling operation costs, the oil industry has shown a trend toward real-time measurements and control. In this scenario, drilling control becomes a challenging problem for the industry, especially due to the difficulty associated with parameters modeling. One of the drillbit performance evaluators, the Rate Of Penetration (ROP), has been used as a drilling control parameter. However, relationships between operational variables affecting the ROP are complex and not easily modeled. This work presents a neuro-genetic adaptive controller to treat this problem. It is based on an auto-regressive with extra input signals, or ARX model and on a Genetic Algorithm (GA) to control the ROP. © [2006] IEEE.

A genetic neuro-model reference adaptive controller for petroleum wells drilling operations

Motivated by rising drilling operation costs, the oil industry has shown a trend towards real-time measurements and control. In this scenario, drilling control becomes a challenging problem for the industry, especially due to the difficulty associated to parameters modeling. One of the drill-bit performance evaluators, the Rate of Penetration (ROP), has been used in the literature as a drilling control parameter. However, the relationships between the operational variables affecting the ROP are complex and not easily modeled. This work presents a neuro-genetic adaptive controller to treat this problem. It is based on the Auto-Regressive with Extra Input Signals model, or ARX model, to accomplish the system identification and on a Genetic Algorithm (GA) to provide a robust control for the ROP. Results of simulations run over a real offshore oil field data, consisted of seven wells drilled with equal diameter bits, are provided. © 2006 IEEE.

A Feedfordward Adaptive Controller to Reduce the Imaging Time of Large-Sized Biological Samples with a SPM-Based Multiprobe Station

The time required to image large samples is an important limiting factor in SPM-based systems. In multiprobe setups, especially when working with biological samples, this drawback can make impossible to conduct certain experiments. In this work, we present a feedfordward controller based on bang-bang and adaptive controls. The controls are based in the difference between the maximum speeds that can be used for imaging depending on the flatness of the sample zone. Topographic images of Escherichia coli bacteria samples were acquired using the implemented controllers. Results show that to go faster in the flat zones, rather than using a constant scanning speed for the whole image, speeds up the imaging process of large samples by up to a 4x factor.

Geometric Structure of the Adaptive Controller of the Human Arm

The objects with which the hand interacts with may significantly change the dynamics of the arm. How does the brain adapt control of arm movements to this new dynamic? We show that adaptation is via composition of a model of the task's dynamics. By exploring generalization capabilities of this adaptation we infer some of the properties of the computational elements with which the brain formed this model: the elements have broad receptive fields and encode the learned dynamics as a map structured in an intrinsic coordinate system closely related to the geometry of the skeletomusculature. The low--level nature of these elements suggests that they may represent asset of primitives with which a movement is represented in the CNS.

Automatic tuning of PID controllers using model reference adaptive control techniques

This paper discusses the application of model reference adaptive control concepts to the automatic tuning of PID controllers. The effectiveness of the proposed method is shown through simulated applications. The gradient approach and simulated examples are provided.

PID autotuning using neural networks and model reference adaptive control

This paper describes the application of artificial neural networks for automatic tuning of PID controllers using the Model Reference Adaptive Control approach. The effectiveness of the proposed method is shown through a simulated application.

Comments, with reply on 'Continuous time relay-controlled model reference adaptive-system' by A. Abdulkareem and R. Nagarajam

An adaptive scheme is shown by the authors of the above paper (ibid. vol. 71, no. 2, pp. 275-276, Feb. 1983) for continuous time model reference adaptive systems (MRAS), where relays replace the usual multipliers in the existing MRAS. The commenter shows an error in the analysis of the hyperstability of the scheme, such that the validity of this configuration becomes an open question.

Projecto de Controladores usando Redes Neuronais

Neste documento descreve-se o projeto desenvolvido na unidade curricular de Tese e Dissertação durante o 2º ano do Mestrado de Engenharia Eletrotécnica e de Computadores no ramo de Automação e Sistemas, no Departamento de Engenharia Eletrotécnica (DEE) do Instituto Superior de Engenharia do Porto (ISEP). O projeto escolhido teve como base o uso da tecnologia das redes neuronais para implementação em sistemas de controlo. Foi necessário primeiro realizar um estudo desta tecnologia, perceber como esta surgiu e como é estruturada. Por último, abordar alguns casos de estudo onde as redes neuronais foram aplicadas com sucesso. Relativamente à implementação, foram consideradas diferentes estruturas de controlo, e entre estas escolhidas a do sistema de controlo estabilizador e sistema de referência adaptativo. No entanto, como o objetivo deste trabalho é o estudo de desempenho quando aplicadas as redes neuronais, não se utilizam apenas estas como controlador. A análise exposta neste trabalho trata de perceber em que medida é que a introdução das redes neuronais melhora o controlo de um processo. Assim sendo, os sistemas de controlo utilizados devem conter pelo menos uma rede neuronal e um controlador PID. Os testes de desempenho são aplicados no controlo de um motor DC, sendo realizados através do recurso ao software MATLAB. As simulações efetuadas têm diferentes configurações de modo a tirar conclusões o mais gerais possível. Assim, os sistemas de controlo são simulados para dois tipos de entrada diferentes, e com ou sem a adição de ruído no sensor. Por fim, é efetuada uma análise das respostas de cada sistema implementado e calculados os índices de desempenho das mesmas.

Estabilidade e robustez de um controlador adaptativo indireto por um modelo de referência e estrutura variável

In this thesis, it is developed the robustness and stability analysis of a variable structure model reference adaptive controller considering the presence of disturbances and unmodeled dynamics. The controller is applied to uncertain, monovariable, linear time-invariant plants with relative degree one, and its development is based on the indirect adaptive control. In the direct approach, well known in the literature, the switching laws are designed for the controller parameters. In the indirect one, they are designed for the plant parameters and, thus, the selection of the relays upper bounds becomes more intuitive, whereas they are related to physical parameters, which present uncertainties that can be known easier, such as resistances, capacitances, inertia moments and friction coefficients. Two versions for the controller algorithm with the stability analysis are presented. The global asymptotic stability with respect to a compact set is guaranteed for both cases. Simulation results under adverse operation conditions in order to verify the theoretical results and to show the performance and robustness of the proposed controller are showed. Moreover, for practical purposes, some simplifications on the original algorithm are developed

Controle adaptativo robusto para um modelo desacoplado de um robô móvel

This thesis presents a new structure of robust adaptive controller applied to mobile robots (surface mobile robot) with nonholonomic constraints. It acts in the dynamics and kinematics of the robot, and it is split in two distinct parts. The first part controls the robot dynamics, using variable structure model reference adaptive controllers. The second part controls the robot kinematics, using a position controller, whose objective is to make the robot to reach any point in the cartesian plan. The kinematic controller is based only on information about the robot configuration. A decoupling method is adopted to transform the linear model of the mobile robot, a multiple-input multiple-output system, into two decoupled single-input single-output systems, thus reducing the complexity of designing the controller for the mobile robot. After that, a variable structure model reference adaptive controller is applied to each one of the resulting systems. One of such controllers will be responsible for the robot position and the other for the leading angle, using reference signals generated by the position controller. To validate the proposed structure, some simulated and experimental results using differential drive mobile robots of a robot soccer kit are presented. The simulator uses the main characteristics of real physical system as noise and non-linearities such as deadzone and saturation. The experimental results were obtained through an C++ program applied to the robot soccer kit of Microrobot team at the LACI/UFRN. The simulated and experimental results are presented and discussed at the end of the text

Uma abordagem indireta para o Controlador em Modo Dual Adaptativo Robusto

In this work is proposed an indirect approach to the DualMode Adaptive Robust Controller (DMARC), combining the typicals transient and robustness properties of Variable Structure Systems, more specifically of Variable Structure Model Reference Adaptive Controller (VS-MRAC), with a smooth control signal in steady-state, typical of conventional Adaptive Controllers, as Model Reference Adaptive Controller (MRAC). The goal is to provide a more intuitive controller design, based on physical plant parameters, as resistances, inertia moments, capacitances, etc. Furthermore, with the objective to follow the evolutionary line of direct controllers, it will be proposed an indirect version for the Binary Model Reference Adaptive Controller (B-MRAC), that was the first controller attemptting to act as MRAC as well as VS-MRAC, depending on a pre-defined fixed parameter

Controle em modo dual adaptativo robusto aplicado a um sistema de geração de energia elétrica

Synchronous machines, widely used in energy generation systems, require constant voltage and frequency to obtain good quality of energy. However, for large load variati- ons, it is difficult to maintain outputs on nominal values due to parametric uncertainties, nonlinearities and coupling among variables. Then, we propose to apply the Dual Mode Adaptive Robust Controller (DMARC) in the field flux control loop, replacing the tradi- tional PI controller. The DMARC links a Model Reference Adaptive Controller (MRAC) and a Variable Structure Model Reference Adaptive Controller (VS-MRAC), incorpora- ting transient performance advantages from VS-MRAC and steady state properties from MRAC. Moreover, simulation results are included to corroborate the theoretical studies.

Adaptive control of Hydraulic Drive

Adaptive control systems are one of the most significant research directions of modern control theory. It is well known that every mechanical appliance’s behavior noticeably depends on environmental changes, functioning-mode parameter changes and changes in technical characteristics of internal functional devices. An adaptive controller involved in control process allows reducing an influence of such changes. In spite of this such type of control methods is applied seldom due to specifics of a controller designing. The work presented in this paper shows the design process of the adaptive controller built by Lyapunov’s function method for the Hydraulic Drive. The calculation needed and the modeling were conducting with MATLAB® software including Simulink® and Symbolic Math Toolbox™ etc. In the work there was applied the Jacobi matrix linearization of the object’s mathematical model and derivation of the suitable reference models based on Newton’s characteristic polynomial. The intelligent adaptive to nonlinearities algorithm for solving Lyapunov’s equation was developed. Developed algorithm works properly but considered plant is not met requirement of functioning with. The results showed confirmation that adaptive systems application significantly increases possibilities in use devices and might be used for correction a system’s behavior dynamics.