This monograph explores the design of controllers that suppress oscillations and instabilities in congested traffic flow using PDE backstepping methods. The first part of the text is concerned with basic backstepping control of freeway traffic using the Aw-Rascle-Zhang (ARZ) second-order PDE model. It begins by illustrating a basic control problem - suppressing traffic with stop-and-go oscillations downstream of ramp metering - before turning to the more challenging case for traffic upstream of ramp metering. The authors demonstrate how to design state observers for the purpose of stabilization using output-feedback control. Experimental traffic data are then used to calibrate the ARZ model and validate the boundary observer design. Because large uncertainties may arise in traffic models, adaptive control and reinforcement learning methods are also explored in detail. Part II then extends the conventional ARZ model utilized until this point in order to address more complex traffic conditions: multi-lane traffic, multi-class traffic, networks of freeway segments, and driver use of routing apps. The final chapters demonstrate the use of the Lighthill-Whitham-Richards (LWR) first-order PDE model to regulate congestion in traffic flows and to optimize flow through a bottleneck. In order to make the text self-contained, an introduction to the PDE backstepping method for systems of coupled first-order hyperbolic PDEs is included. Traffic Congestion Control by PDE Backstepping is ideal for control theorists working on control of systems modeled by PDEs and for traffic engineers and applied scientists working on unsteady traffic flows. It will also be a valuable resource for researchers interested in boundary control of coupled systems of first-order hyperbolic PDEs.

With the rapid growth of Internet, E-learning systems have become more and more popular because they can enable learners to study at any time and any location, so the need of learning resources and instructional design is also increasing rapidly. Many international standards have been proposed to model the structures and guiding rules of learning activities for the purpose of sharing and reusing. However, there are still no effective models to describe complex instructional design knowledge, and the effective authoring systems of instructional design and learning content navigating sequence are also required. Moreover, without an intelligent knowledge management scheme, the huge amount of learning resources will confuse the teachers in learning activity design. It implies that how to visualise and integrate the heterogeneous instructional design and navigating sequence knowledge in a learning platform becomes an important issue. Therefore, the author proposes an Intelligent Assisted Instructional Design System (IAIDS), which is an instructional design expert system consisting of Knowledge Representation (KR) module to model the instructional design graphically, knowledge acquisition (KA) module to assist teachers to edit instructional design and navigating sequence efficiently, Knowledge Management (KM) module to support teachers to manage and retrieve learning contents in repositories, and Knowledge Inference (KI) module to display the learning activity in a portable and modifiable platform.