Universal approximation using radial-basis-function networks
Neural Computation
Optimal estimation theory for dynamic systems with set membership uncertainty: an overview
Automatica (Journal of IFAC)
Nonlinear black-box modeling in system identification: a unified overview
Automatica (Journal of IFAC) - Special issue on trends in system identification
A Neural Network-Based Direct Inverse Control for Active Control of Vibrations of Mechanical Systems
SBRN '00 Proceedings of the VI Brazilian Symposium on Neural Networks (SBRN'00)
Computation of local radius of information in SM-IBC identification of nonlinear systems
Journal of Complexity
Brief paper: The filter design from data (FD2) problem: Nonlinear Set Membership approach
Automatica (Journal of IFAC)
Technical communique: Input-output finite time stabilization of linear systems
Automatica (Journal of IFAC)
Brief Paper: Convergence Properties of the Membership Set
Automatica (Journal of IFAC)
Set Membership identification of nonlinear systems
Automatica (Journal of IFAC)
Greed is good: algorithmic results for sparse approximation
IEEE Transactions on Information Theory
Recovery of exact sparse representations in the presence of bounded noise
IEEE Transactions on Information Theory
Stable recovery of sparse overcomplete representations in the presence of noise
IEEE Transactions on Information Theory
Just relax: convex programming methods for identifying sparse signals in noise
IEEE Transactions on Information Theory
IEEE Transactions on Neural Networks
Hi-index | 22.14 |
We propose an approach for the direct design from data of controllers finalized at solving tracking problems for nonlinear systems. This approach, called Direct FeedbacK (DFK) design, overcomes relevant problems typical of the standard design methods, such as modeling errors, non-trivial parameter identification, non-convex optimization, and difficulty in nonlinear control design. Considering a Set Membership (SM) approach, we provide three main contributions. The first one is a theoretical framework for the stability analysis of nonlinear feedback control systems, in which the controller f@^ is an approximation identified from data of an ideal inverse model f"o. In this framework, we derive sufficient conditions under which f@^ stabilizes the closed-loop system. The second contribution is a technique for the direct design of an approximate controller f^* from data, having suitable optimality, stability, and sparsity properties. In particular, we show that f^* is an almost-optimal controller (in a worst-case sense), and we derive a guaranteed accuracy bound, which can be used to quantify the performance level of the DFK control system. We also show that, when the number of data used for control design tends to infinity and these data are dense in the controller domain, the closed-loop stability is guaranteed for a set of trajectories of interest. The technique is based on convex optimization and sparse identification methods, and thus avoids the problem of local minima and allows an efficient online controller implementation in real-world applications. The third contribution is a simulation study, regarding the application of DFK to the challenging problem of control design for a class of airborne wind energy generators.