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In presence of the emerging technology of automated vehicles, it is anticipated that the future traffic system would be comprised of mixed traffic with both self-driving autonomous vehicles (AVs) and human-driven conventional vehicles. It is imperative to propose new traffic management measures to manage the future traffic system, as complement of the existing ones such as road pricing schemes. This study seeks to take advantage of the controllable property of AVs’ routing choices to develop a day-to-day routing allocation scheme for a certain number of autonomous vehicles so as to drive the mixed traffic system into a desired traffic state. Specifically, we assume that all travelers are bounded rational and AV users are willing to accept route allocation with route travel cost not exceeding HV users’ indifference band. Therefore, the best-case bounded rationality user equilibrium (BRUE) flow pattern is in principle the most desirable traffic state out of all the BRUE solutions. This study proposes a day-to-day AVs’ routing allocation scheme by which the traffic system would be directed to evolve towards the desired best-case BRUE. The day-to-day traffic dynamics of HVs are proposed to follow a general framework, which can be further proved to be the BRUE rational behavior adjustment process (BRUE-RBAP). The condition for convergence is investigated under general assumptions. Numerical examples are provided to demonstrate the effectiveness of this traffic management scheme. Ministry of Education (MOE) This work is supported by Singapore Ministry of Education Academic Research Fund MOE2021-T1-002-062.

This paper deals with the fault diagnosis of nonlinear mechatronic system using bond graph and particle swarm optimization. The mechatronic system under investigation includes a DC motor with a load part. Nonlinear phenomena such as friction and backlash are considered in the monitored system. The objective of fault diagnosis is to determine the fault source and estimate its magnitude. In order to realize the task in the application, the bond graph modeling tool is adopted to carry out fault detection and isolation. After the potential faults that could lead to the observed fault symptom are obtained, the particle swarm optimization is utilized for fault estimation purpose. Numerical simulations are performed to illustrate the effectiveness of the developed method.

In this paper, a nonlinear model for transport aircraft with continuous airdropping heavy cargo is presented and back-stepping controller is employed. For the attitude subsystem and the velocity subsystem, the dynamics are transformed into the strict-feedback form by using back-stepping scheme respectively. In the back-stepping controller, the virtual controllers are designed to compensate control system and ensure the stability and reliability. Moreover, to avoid "explosion of complexity", command filter design is used. With the proposed controller, the bounded stability analysis is presented. The simulations compare the accurate model with the model with uncertainties, and proof the effectiveness and robustness of back-stepping controller in continuous airdropping heavy cargos.

We propose a distributed algorithm for controlling traffic signals, allowing constraints such as periodic switching sequences of phases and minimum and maximum green time to be incorporated. Our algorithm is adapted from backpressure routing, which has been mainly applied to communication and power networks. We formally prove that our algorithm ensures global optimality as it leads to maximum network throughput even though the controller is constructed and implemented in a completely distributed manner., Comment: arXiv admin note: substantial text overlap with arXiv:1205.5938

In this paper, three discrete-time adaptive ILC approaches are discussed fully. In parallel with the continuous-time systems, a discrete-time adaptive ILC with time-difference estimator is first proposed for a class of discrete-time systems with constant parametric uncertainties. And then, a discrete-time adaptive ILC with hybrid-difference estimator is proposed for the other class of discrete-time systems with a mixture of time-varying and time-invariant uncertain parameters. The existing discrete-time adaptive ILC with iteration-difference is also reviewed for the convenience of comparison. Three cases are considered in the simulation to show the effectiveness of the proposed adaptive ILCs and to answer the question that which one is most suitable for the different controlled systems.

A flexible repetitive control (RC) scheme named "dual-mode structure repetitive control" (DMRC) is presented in this article. A robust stability criterion for DMRC systems is derived in terms of two parameters, odd-harmonic RC gain and even-harmonic RC gain. The general framework of DMRC of-fers the flexibility in the development of various RC controllers. Without additional complexity and loss of tracking accuracy, DMRC can achieve faster error convergence rate than conventional RCs. DMRC re-quires the same data memory size as that of conventional RC one. An application example of DMRC con-trolled PWM inverter illustrates the validity of our proposed DMRC scheme. Comparisons of DMRC, con-ventional RC and odd-harmonic RC highlight the advantages of the presented DMRC approach.

This project presents a discrete-time model reference learning control (MRLC) algorithm for a class of nonlinear and time varying discrete-time system.

The path planning is still a challenging problem even after the past 30 years' research. A global off-line path planning algorithm based on circuit grid approach is presented in this dissertation. This approach makes it easy to find the optimal feasible path. The generated path function can combine various environmental conditions such as hazardous, jamming and broad, etc. It overcomes the shortcoming of local optimum traps in some path planners. Also, this algorithm is always converging if a feasible path does exist. Master of Science (Computer Control and Automation)

Our main objective is to develop a measurement algorithm and software to process the laser sensor measurement data of jet engine turbine vane surfaces, and to reconstruct 3D profiles of these surfaces. In particular, the corrupted data due to the specular reflection are processed to obtain a nominal 3D surface profile. Master of Science (Computer Control and Automation)

The objective of the work is to widen the learnable frequency band of an ILC system for higher tracking precision. Doctor of Philosophy (EEE)

This dissertation focuses on policy based management for Internet quality of service. A system architecture for QoS/PBNM is proposed for heterogeneous networks in this dissertation. Master of Science (Computer Control and Automation)

The object of this doctoral thesis is to develop novel theoretical results and practical techniques on the feedback control of nonholonomic wheeled mobile robots. Firstly, a brief review of previous work on the nonholonomic systems and their feedback control is provided. This is followed by the presentation of mathematical preliminaries used in the thesis. Doctor of Philosophy (EEE)

In this report, several iterative learning control (ILC) schemes are investigated and compared. Continuous time system and sampled-data system are studied. Master of Science (Computer Control and Automation)