Prof. Dr. Diego Eckhard

Data-driven methodologies have recently emerged as an important paradigm alternative to model-based controller design and several such methodologies are formulated as an H2 performance optimization. This book presents a comprehensive theoretical treatment of the H2 approach to data-driven control design. The fundamental properties implied by the H2 problem formulation are analyzed in detail, so that common features to all solutions are identified. Direct methods (VRFT) and iterative methods (IFT, DFT, CbT) are put under a common theoretical framework. The choice of the reference model, the experimental conditions, the optimization method to be used, and several other designer?s choices are crucial to the quality of the final outcome, and firm guidelines for all these choices are derived from the theoretical analysis presented. The practical application of the concepts in the book is illustrated with a large number of practical designs performed for different classes of processes: thermal, fluid processing and electromechanical.

This paper proposes the use of regularization on the multivariable formulation of the Virtual Reference Feedback Tuning (VRFT). When the process to be controlled has a significant amount of noise, the standard VRFT approach, that uses the instrumental variable technique, provides estimates with very poor statistical properties. To cope with that, this paper considers the use of regularization on the estimation procedure, reducing the covariance error at the cost of inserting a small bias. Also, this paper explains different types of regularization matrices and presents the methodology to tune these matrices. In order to demonstrate the benefits of the proposed formulation, a numerical example is presented.

Virtual Reference Feedback Tuning (VRFT) is a data-driven technique used to design controllers without the need of a process model, only input-output data is utilized. When the process has non-minimum phase (NMP) zeros, the original method usually presents poor performance, because scarcely the reference model has the same NMP zeros as the process. To overcome this problem, a flexible criterion has been proposed to the VRFT method, in a way that both the controller parameters and the NMP zeros of the process are estimated together. In this paper we present the application of the VRFT method with flexible criterion to the level control of a MIMO pilot plant. We show that a sequential controller design may incorporate NMP behavior to the process. We then use the VRFT method with flexible criterion to design the controller using only closed-loop data from the process.

This paper considers the design and implementation of a discrete-time fast tracking controller for quadrotor vehicles subject to perturbations. The proposed controller consists of a model-based disturbance observer and a Composite Nonlinear Feedback (CNF) controller. The CNF control law introduces nonlinear damping to the system so that it possesses a fast rise time without overshoot. The least square identification method is applied to develop a model based disturbance observer, thus decoupling the problems of track following and disturbance rejection. Experimental results are provided in order to validate the proposed approach.

In this work a modified Resonant Controller is proposed to deal with the tracking/rejection problem of periodic signals robust to period variations and parametric uncertainties in the plant. The control strategy is based on a resonant structure in series with a notch filter, which will be responsible to improve the robustness to period variation. A robust state feedback controller is designed by solving a linear matrix inequality (LMI) optimization problem guaranteeing the robust stability of the closed loop system. A numerical example is presented to illustrate the method.

Iterative Feedback Tuning (IFT) is a data-driven method used to tune parameters of feedback controllers minimising an H2 criterion. The method uses data from experiments to estimate the gradient of the criterion, and uses iterative quasinewton algorithms to adjust the controllers. When the method is used in cascade systems, usually the inner loop is firstly adjusted, and after the outer loop. In this article we describe an extension to the IFT method that adjusts both inner and outer loop at the same time using only data from closed-loop experiments at each iteration.

Quadcopter is a type of Unmanned Aerial Vehicle which is lifted and propelled by four rotors. The vehicle has a complex non-linear dynamic which makes the tuning of the roll and pitch controllers difficult. Usually the control design is based on a mathematical model which is strongly related to physical components of vehicle: mass, moment of inertia and aerodynamic. When a tool is attached to the vehicle, a new model must be computed to redesign the controllers. In this article we will adjust the controllers of a real experimental quadcopter using the Cascade Iterative Feedback Tuning method. The method is data-driven, so it does not uses a model for the vehicle; all it uses is input-output data collect from the closed-loop system. The method minimizes the \{H2\} error between the desired response and the actual response of the vehicle angle using the Newton-Raphson algorithm. The method achieves the desired performance without the need of the vehicle model, with low cost and low complexity.

Wireless networks are gaining space in industrial environments due to the low installation costs and low maintenance. Robustness is also one of the main requirements for these systems to be adopted, and, in this context, WirelessHART (WH), ISA SP100.11a, and WIA-PA protocols met these characteristics. In order to provide low maintenance, these protocols must provide reliable radio links while keeping low power consumption to allow battery powered devices. Unfortunately, the standards of these protocols do not impose any RF power modulation technique, which is a form to increase even more the battery endurance of a wireless field device. Instead, RF power levels are fixed and selected by commissioning, and must allow the longest link per device. In this case, devices in closer ranges waste energy during transmissions, as they could save energy by modulating the RF power. This paper presents a RF power modulation technique that employs a proportional-integral controller and allows reduction of energy consumption while keeping the robustness of RF links. A proof of concept of the power modulation technique is implemented and verified showing good results and proving that the proposed controller is feasible. The proposal has the advantage to be fully compatible with the standard.

The wireless sensor networks (WSN) are gradually gaining attention because it is a key technology for the Internet of Things. For most of these networks, the data is usually collected in a manual way, by removing a memory unit or connecting the collector node to a personal computer. This is a constraint, because it demands the manipulation of the collector radio by the operator, which consists in a problem in practical applications. The main goal of this work is to present a non-invasive alternative way to collect the data by means of Bluetooth technology. The approach allows the development of hermetic devices, which is a desirable feature for practical deployment of the sensor nodes.

Os métodos de projeto de controladores baseado em dados são um conjunto de técnicas utilizadas para ajustar os ganhos de controladores, que não utilizam um modelo matemático do processo na sintonia dos parâmetros. Alguns destes métodos são o Iterative Feedback Tuning (IFT), Correlation based Tuning (CbT), Virtual Reference Feedback Tuning (VRFT) e Optimal Controller Identification (OCI). Apesar de algumas destas t{ écnicas existirem por mais de uma década, são encontrados poucos trabalhos na literatura que demonstram a aplicabilidade dos métodos em sistemas industriais. Neste trabalho duas técnicas de projeto de controladores baseado em dados não-iterativas (VRFT e OCI) são aplicadas em em um sistema de controle de nível industrial, que utiliza uma rede Foundation Fieldbus H1. O trabalho demostra que as técnicas apresentadas podem ser aplicadas com facilidade em sistemas industriais gerando repostas dinâmicas satisfatórias.

Industrial wireless networks are gradually being adopted in industry, especially due to the low installation costs and low maintenance provided by these systems. For the use in harsh environments, the communication system should provide reliable radio links and low power to allow battery powered devices. In this context, WirelessHART, ISA SP100.11a and WIA-PA protocols were developed to meet these requirements. The standards of these protocols define a minimum number of RF fixed power and selectable only by commissioning. Within this scenario, it is important to adapt RF power modulation, a procedure already used in several communication protocols to minimize the energy consumption of transceivers while maintaining link quality. In this paper, three ways to modulate RF power are analyzed within WirelessHART field devices. A specific approach that works in a decentralized way is developed and the results show it can save 50% of energy in certain cases. The proposal has the extra advantage to be compatible with the standard.

This work presents a non-linear identification of a bioreactor through the minimization of the prediction error, where the output data are the measurements of the methane gas generated by the process, during 37 days. Since the chosen model is non-linear, an iterative method is used to obtain the model parameters. This method depends on the cost function?s gradient, whose calculus is implemented recursively, since it does not have a closed form. The algorithm used in the minimization of the cost function is a combination of two methods: the gradient method and the Newton-Raphson method. The model obtained is validated with output data from the process and it reproduces the behavior of the bioreactor with good precision.

This work solves an experiment design problem for a linear regression problem using a reduced order model. The quality of the model is assessed using a mean square error measure that depends linearly on the parameters. The designed input signal ensures a predefined quality of the model while minimizing the input energy.

This paper studies a one-shot (non-iterative) data-based method for Model Reference (MR) control design. It shows that the optimal controller can be obtained as the solution of a Prediction Error (PE) identification problem that directly estimates the controller parameters through a reparametrization of the input-output model. The standard tools of PE Identification can thus be used to analyze the statistical properties (bias and variance) of the estimated controller. It also shows that, for MR control design, direct and indirect data-based methods are essentially equivalent.

The Prediction Error Method (PEM) is related to an optimization problem built on input/output data collected from the system to be identified. It is often hard to find the global solution of this optimization problem because the corresponding objective function presents local minima and/or the search space is constrained to a nonconvex set. The existence of local minima, and hence the difficulty in solving the optimization, depends mainly on the experimental conditions, more specifically on the spectrum of the input/output data collected from the system. It is therefore possible to avoid the existence of local minima by properly choosing the spectrum of the input; in this paper we show how to perform this choice. We present sufficient conditions for the convergence of PEM to the global minimum and from these conditions we derive two approaches to avoid the existence of nonglobal minima. We present the application of one of these two approaches to a case study where standard identification toolboxes tend to get trapped in nonglobal minima.

The Output Error Method is related to an optimization problem based on a multi-modal criterion. Iterative algorithms like the steepest descent are usually used to look for the global minimum of the criterion. This algorithms can get stuck at a local minimum. This paper presents sufficient conditions about the convergence of the steepest descent algorithm to the global minimum of the cost function. Moreover, it presents constraints to the input spectrum which ensure that the convergence conditions are satisfied. This constraints are convex and can easily be included in an experiment design approach to ensure the convergence of the iterative algorithms to the global minimum of the criterion.

This work addresses data-based control design. The properties inherent to data-based design are discussed under a common theoretical framework. The computational cost is estimated with relation to memory space and number of elementar operations. Simulations present a comparision between the studied methods.

Data-based control design methods most often consist of iterative adjustment of the controller's parameters towards the parameter values which minimize an H2 performance criterion. Typically, batches of input-output data collected from the system are used to feed directly a gradient descent optimization - no process model is used. The convergence to the global minimum of the performance criterion depends on the initial controller parameters, as well as on the size and direction of the steps taken at each iteration. This paper discusses these issues and provides a method for choosing the search direction and the step size at each optimization step so that convergence to the global minimum is obtained with high convergence rate.

This work addresses data-based (DB) control design; the properties and limitations inherent to DB design are discussed under a common theoretical framework and illustrated through experimental results. Theoretical results concerning the convergence and precision are discussed and specified for a particular class of processes. Two DB methods, representative of this design approach, are used to illustrate the general properties of DB design: the Virtual Reference Feedback Tuning (VRFT) and the Iterative Feedback Tuning (IFT).

In this article, a new approach to the problem of indoor navigation based on ultrasonic sensors is presented, where artificial neural networks (ANN) are used to estimate the position and orientation of a mobile robot. This approach proposes the use of three Radial Basis Function (RBF) Networks, where environment maps from an ultrasonic sensor and maps synthetically generated are used to estimate the robot localization. The mobile robot is mainly characterized by its real time operation based on the Matlab/Simulink environment, where the whole necessary tasks for an autonomous navigation are done in a hierarchical and easy reprogramming way. Finally, practical results of real time navigation related to robot localization in a known indoor environment are shown.