Eckhard, D, Campestrini L, Bergel ME, Bazanella AS.
2009.
Data-Based Control Design for a Process Class with Guaranteed Convergence to the Globally Optimum Controller. European Control Conference 2009. :993–998., Budapest: IEEE
AbstractThis 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).
Eckhard, D, Bazanella AS.
2010.
Data-based controller tuning: Improving the convergence rate. 49th IEEE Conference on Decision and Control. :4801–4806., Atlanta: IEEE
AbstractData-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.
Campestrini, L, Eckhard D, Bazanella AS, Gevers M.
2017.
Data-driven model reference control design by prediction error identification, April. Journal of the Franklin Institute. 354:2828–2647., Number 6
AbstractAbstract This paper deals with Data-Driven (DD) control design in a Model Reference (MR) framework. We present a new \{DD\} method for tuning the parameters of a controller with a fixed structure. Because the method originates from embedding the control design problem in the Prediction Error identification of an optimal controller, it is baptized as Optimal Controller Identification (OCI). Incorporating different levels of prior information about the optimal controller leads to different design choices, which allows to shape the bias and variance errors in its estimation. It is shown that the limit case where all available prior information is incorporated is tantamount to model-based design. Thus, this methodology also provides a framework in which model-based design and \{DD\} design can be fairly and objectively compared. This comparison reveals that \{DD\} design essentially outperforms model-based design by providing better bias shaping, except in the full order controller case, in which there is no bias and model-based design provides smaller variance. The practical effectiveness of the design methodology is illustrated with experimental results.
Gomes da Silva Jr., JM, Lescher F, Eckhard D.
2007.
Design of time-varying controllers for discrete-time linear systems with input saturation. IET Control Theory Applications. 1:155–162., Number 1
AbstractA method for computing time-varying dynamic output feedback controllers for discrete-time linear systems subject to input saturation is proposed. The method is based on a locally valid polytopic representation of the saturation term. From this representation, it is shown that, at each sampling time, the matrices of the stabilising time-varying controller can be computed from the current system output and from constant matrices obtained as a solution of some matrix inequalities. Linear matrix inequality-based optimisation problems are therefore proposed in order to compute the controller aiming at the maximisation of the basin attraction of the closed-loop system, as well as aiming at ensuring a level of {L2} disturbance tolerance and rejection.
Salton, AT, Eckhard D, Flores JV, Fernandes G, Azevedo G.
2016.
Disturbance observer and nonlinear damping control for fast tracking quadrotor vehicles, Sept. 2016 IEEE Conference on Control Applications (CCA). :705–710., Buenos Aires: IEEE
AbstractThis 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.
Gomes da Silva Jr., JM, Castelan EB, Corso J, Eckhard D.
2013.
Dynamic output feedback stabilization for systems with sector-bounded nonlinearities and saturating actuators. Journal of the Franklin Institute. 350:464–484., Number 3
AbstractIn the present work a systematic methodology for computing dynamic output stabilizing feedback control laws for nonlinear systems subject to saturating inputs is presented. In particular, the class of Lur'e type nonlinear systems is considered. Based on absolute stability tools and a modified sector condition to take into account input saturation effects, an \{LMI\} framework is proposed to design the controller. Asymptotic as well as input-to-state and input-to-output (in a L2 sense) stabilization problems are addressed both in regional (local) and global contexts. The controller structure is composed of a linear part, an anti-windup loop and a term associated to the output of the dynamic nonlinearity. Convex optimization problems are proposed to compute the controller considering different optimization criteria. A numerical example illustrates the potentialities of the methodology.