2 research outputs found
Exponential Estimates and Stabilization of Discrete-Time Singular Time-Delay Systems Subject to Actuator Saturation
This paper is concerned with exponential estimates and stabilization of a class of discrete-time singular systems
with time-varying state delays and saturating actuators. By constructing a decay-rate-dependent Lyapunov-Krasovskii
function and utilizing the slow-fast decomposition technique, an exponential admissibility condition, which not only
guarantees the regularity, causality, and exponential stability of the unforced system but also gives the corresponding
estimates of decay rate and decay coefficient, is derived in terms of linear matrix inequalities (LMIs). Under the
proposed condition, the exponential stabilization problem of discrete-time singular time-delay systems subject actuator
saturation is solved by designing a stabilizing state feedback controller and determining an associated set of safe initial
conditions, for which the local exponential stability of the saturated closed-loop system is guaranteed. Two numerical
examples are provided to illustrate the effectiveness of the proposed results
Reconstruction of gasoline engine in-cylinder pressures using recurrent neural networks
Knowledge of the pressure inside the combustion chamber of a gasoline
engine would provide very useful information regarding the quality and
consistency of combustion and allow significant improvements in its control,
leading to improved efficiency and refinement. While measurement using incylinder
pressure transducers is common in laboratory tests, their use in
production engines is very limited due to cost and durability constraints.
This thesis seeks to exploit the time series prediction capabilities of recurrent
neural networks in order to build an inverse model accepting crankshaft
kinematics or cylinder block vibrations as inputs for the reconstruction of
in-cylinder pressures. Success in this endeavour would provide information
to drive a real time combustion control strategy using only sensors already
commonly installed on production engines. A reference data set was
acquired from a prototype Ford in-line 3 cylinder direct injected, spark ignited
gasoline engine of 1.125 litre swept volume. Data acquired concentrated on
low speed (1000-2000 rev/min), low load (10-30 Nm brake torque) test
conditions. The experimental work undertaken is described in detail, along
with the signal processing requirements to treat the data prior to presentation
to a neural network.
The primary problem then addressed is the reliable, efficient training of a
recurrent neural network to result in an inverse model capable of predicting
cylinder pressures from data not seen during the training phase, this unseen
data includes examples from speed and load ranges other than those in the
training case. The specific recurrent network architecture investigated is the
non-linear autoregressive with exogenous inputs (NARX) structure. Teacher
forced training is investigated using the reference engine data set before a
state of the art recurrent training method (Robust Adaptive Gradient Descent
– RAGD) is implemented and the influence of the various parameters
surrounding input vectors, network structure and training algorithm are
investigated. Optimum parameters for data, structure and training algorithm
are identified