Control of quantized linear systems: an l/sub 1/-optimal control approach

Abstract

In many practical situations the outputs of a plant are not measured exactly, but are corrupted by quantization errors. Often the effect of the quantization error is neglected in the control design phase, which can lead to undesirable effects like limit cycles and even chaotic behavior once the controller has been implemented. In this paper we present a method based on 11 optimal control that minimizes the amplitude of the oscillations in the to-be-controlled variables. Analytical and numerical examples illustrate the elegance of the Il-theory in this setting.

Stability Analysis of Networked Control Systems Using a Switched Linear Systems Approach

“…Similar inequalities can be derived that bound the intersample behaviour for the state evolution x c (t) of (2a) and for the network-induced error given by (7). Therefore, by using the bounds on h k and τ k , the continuous-time NCS (1), (2a) or (2b), (3), and (7) is LUGBT.…”

Adaptive Feedback Stabilization with Quantized State Measurements for a Class of Chaotic Systems

“…In fact, the states to be used as the input signals of controller are often needed to be on-line reexamined by some additional information-processing devices such as sensors, encoders and transmitting instruments. As mentioned in [11], the states are never measured precisely because the devices always introduce certain types of inaccuracies. One origin of measurement errors is related to the fact that only quantized information of the states is available, i.e., the input signals of controller can be only accessed at a finite number of quantization levels of states.…”

“…In the past decades, the static quantization schemes are utilized to stabilize the linear systems in Refs. [11][12][13]15]. A simple dynamic scaling method for a logarithmic quantizer based output feedback controller is designed for discrete-time linear systems in Ref.…”