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This paper considers a linear discrete-time system operating in a repetitive mode to track a reference trajectory with a required accuracy. The control variable has a delay along the sample trajectory, and saturation-type constraints are imposed. We introduce a new method for designing an iterative learning control law that depends on the delay and ensures the required accuracy of tracking. A numerical example demonstrates the effectiveness of this method. [ABSTRACT FROM AUTHOR]

This paper considers a linear discrete-time system operating in a repetitive mode to track a reference trajectory with a given accuracy. The system parameters are incompletely known and are described by the affine uncertainty model. In addition, the system is subjected to random disturbances, and measurements are carried out with noise. During system operation, the reference trajectory changes after a given number of repetitions. The resulting transient error may cause a temporary loss of accuracy. We propose a new iterative learning control design method to compensate the transient error. An example illustrates the effectiveness of this method. [ABSTRACT FROM AUTHOR]

We consider the problem of optimal control of a linear time-invariant discrete-time system by inaccurate measurements of its output signals subject to guaranteed satisfaction of geometric constraints on the output signals. We study the case in which a minimal realization of the system in the state space is known and the case where the parametric model of the system is not known. A novel method is proposed for solving the problem in the case of an unknown model. The method is based on a single observed trajectory of the input and output signals of the system and allows omitting the stage of parametric identification of the system. [ABSTRACT FROM AUTHOR]

Discrete-time linear systems with switching in the repetitive mode are considered. The systems are subjected to random disturbances, and the measurements are corrupted by additive noises. Two iterative learning control design methods are proposed. Both of the methods involve an auxiliary 2D model in the form of a discrete repetitive process. The first method is based on the dissipativity conditions established for the auxiliary model with a special choice of the supply rate and storage function. This choice allows finding a control law (in the general case, nonlinear) that ensures the convergence of the learning process. The second method adopts a linear iterative learning control update law of a given form, while the convergence of the learning process is ensured by the stability conditions of the auxiliary 2D model. The structure of both control laws includes a stationary Kalman filter. The stability conditions are obtained using the divergent method of vector Lyapunov functions. An example is given to demonstrate the capabilities and features of the new method. [ABSTRACT FROM AUTHOR]

In this paper, discrete-time linear systems with parameter switching in the repetitive mode are considered. A new iterative learning control design method is proposed. This method is based on the construction of an auxiliary 2D model in the form of a discrete repetitive process; the stability of the auxiliary model guarantees the convergence of the learning process. Stability conditions are derived using the divergent method of Lyapunov vector functions. The concept of the average dwell time in pass direction is introduced. An example that demonstrates the capabilities and features of the new method is presented. [ABSTRACT FROM AUTHOR]