This paper is concerned with the reinforcement learning methods for the discrete time descriptor systems. An algorithm, as well as its theoretical basis, is presented. The algorithm can generate the optimal controller for the target descriptor system only by the measured input and output data, with no need of the information about the system state and system matrices. The algorithm can work well not only when the system index is equal or less than one, but also can work well when the index is greater than one. Simulation indicates that the presented method can solve the optimal control problem well for descriptor systems when the system model is not exactly known, but the input and output data can be measured. © 2013 IEEE
Learning systems represent an approach to optimal control law design for situations where initial mo...
Control engineering researchers are increasingly embracing data-driven techniques like reinforcement...
Bunjaku, Drilon/0000-0003-4090-4170Control problems for plants that are described with models employ...
AbstractIn this paper, for a given descriptor system, we construct a feedback in order to obtain a n...
The analysis of industrial processes, modelled as descriptor systems, is often computationally hard ...
The analysis of industrial processes, modelled as descriptor systems, is often computationally hard ...
AbstractThis paper surveys numerical techniques for the regularization of descriptor (generalized st...
This paper investigates the application of associative reinforcement learning techniques to the opti...
This paper surveys numerical techniques for the regularization of descriptor (generalized state-spac...
The control and verification of industrial processes, modeled as discrete-time descriptor systems, i...
Implicit dynamic-algebraic equations, known in control theory as descriptor systems, arise naturally...
The control and verification of industrial processes, modeled as discrete-time descriptor systems, i...
summary:A model following control system (MFCS) can output general signals following the desired one...
which permits unrestricted use, distribution, and reproduction in any medium, provided the original ...
The linear discrete-time descriptor noncausal multirate system is considered for the presentation of...
Learning systems represent an approach to optimal control law design for situations where initial mo...
Control engineering researchers are increasingly embracing data-driven techniques like reinforcement...
Bunjaku, Drilon/0000-0003-4090-4170Control problems for plants that are described with models employ...
AbstractIn this paper, for a given descriptor system, we construct a feedback in order to obtain a n...
The analysis of industrial processes, modelled as descriptor systems, is often computationally hard ...
The analysis of industrial processes, modelled as descriptor systems, is often computationally hard ...
AbstractThis paper surveys numerical techniques for the regularization of descriptor (generalized st...
This paper investigates the application of associative reinforcement learning techniques to the opti...
This paper surveys numerical techniques for the regularization of descriptor (generalized state-spac...
The control and verification of industrial processes, modeled as discrete-time descriptor systems, i...
Implicit dynamic-algebraic equations, known in control theory as descriptor systems, arise naturally...
The control and verification of industrial processes, modeled as discrete-time descriptor systems, i...
summary:A model following control system (MFCS) can output general signals following the desired one...
which permits unrestricted use, distribution, and reproduction in any medium, provided the original ...
The linear discrete-time descriptor noncausal multirate system is considered for the presentation of...
Learning systems represent an approach to optimal control law design for situations where initial mo...
Control engineering researchers are increasingly embracing data-driven techniques like reinforcement...
Bunjaku, Drilon/0000-0003-4090-4170Control problems for plants that are described with models employ...