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In this paper, the problem of robust set invariance and contractivity with respect to discrete-time dynamical systems is investigated. In contrast to the usual approach consisting in describing regions of system's space by their border surfaces, a dual description of sets in terms of a generator matrix and a, generally nonlinear, generating function is proposed. This leads to the establishment of...
A two-dimensional (2-D) system theory based iterative learning control (ILC) method for a class of linear discrete-time multivariable systems is presented in this paper. Practical ILC schemes comprise of a feed-forward learning controller along with feedback controllers for improved stability and convergence, termed as feedback assisted iterative learning control (FAILC). As a general format we consider...
A framework for robust stability of large scale systems consisting of linear time-invariant systems interconnected over a network will be surveyed and applied to solve heterogeneous consensus problems. The purpose of distributed consensus algorithms is to reach an agreement regarding a certain quantity of interest that depends on the state of all systems. In most of consensus literature, dynamics...
In this paper we propose a stochastic model predictive control (MPC) formulation based on scenario generation for linear systems affected by discrete multiplicative disturbances. By separating the problems of (1) stochastic performance, and (2) stochastic stabilization and robust constraints fulfillment of the closed-loop system, we aim at obtaining a less conservative control action with respect...
The induction motor is a MIMO, nonlinear and high coupling system. The reversibility of the induction motor is testified. Consequently, a pseudo-linear system is completed by constructing a neural network inverse (NNI) system and combining it with the motor system. The inverse can transform the MIMO nonlinear system into two SISO linear subsystems (i.e., rotor speed and flux subsystems). In order...
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