Major advancements over the last few decades in communication networks gave rise to the
new paradigm of Networked Control Systems (NCSs). Within this paradigm, sensing and
actuation signals are exchanged among various parts of a single system or among many
subsystems via communication networks. Although this enables one to perform more complex
tasks than traditional control paradigms, it comes at the cost of complicating the design phase
and the required analysis tools. One of the major challenges when considering a network is
quantization effect which affects the performance of any control laws that were designed
without taking the network effects into account.
Even if the NCS paradigm is well established, few works are available on adaptive methods for
NCSs: this MSc thesis establishes novel adaptive control approaches that attain asymptotic
tracking for linear systems and switched linear systems with parametric uncertainties, when
input measurements are quantized due to the presence of a communication network closing
the control loop. In addition to enlarging the class of systems for which the adaptive quantized
control can be solved, a hybrid control policy is applied to a novel dynamic quantizer with
dynamic offset to address the tracking problem.
The MSc thesis is split into two parts: in the first part we consider the model reference
adaptive control of a linear uncertain system, where a Lyapunov-based approach is used to
derive the adaptive adjustments for the dynamic range, the dynamic offset and the control
parameters. In the second part, the approach is extended to switched uncertain linear systems
with dwell-time switching, where a new time-varying Lyapunov-like function is adopted: it
is proven analytically that the new Lyapunov function we introduce, overcomes the need
for zooming-out at every time instant in order to compensate the possible increment of the
Lyapunov function.
The proposed quantized adaptive control schemes are applied to two benchmark examples:
an electro-hydraulic system and the piecewise linear model of the NASA GTM aircraft, respectively.