Optimal scheduling of connected and autonomous vehicles at a reservation-based intersection.

Reservation-based intersection control has been evaluated with better performance over traditional signal controls in terms of intersection safety, efficiency, and emission. Controlling connected and autonomous vehicles (CAVs) at a reservation-based intersection in terms of improving intersection efficiency is performed via two factors: trajectory (speed profile) and arrival time of CAVs at the intersection. In an early stage of the reservation-based intersection control, an intersection controller at the intersection may fail to find a feasible solution for both the trajectory and arrival time for a CAV at a certain planning horizon. Leveraging a deeper understanding of the control problem, reservation-based intersection control methods are able to optimize both trajectory and arrival time simultaneously while overcoming the infeasible condition. Furthermore, in order to achieve real-time control at the reservation-based intersection, a scheduling problem of CAV crossing the intersection has been widely modeled to optimize the intersection efficiency. Efficient solution algorithms have been proposed to overcome the curse of dimensionality. However, a control methodology consisting of trajectory planning and arrival time scheduling that can overcome the infeasible condition has not been explicitly explained and defined. Furthermore, an optimal control framework for joint control of the trajectory planning and arrival time scheduling in terms of global intersection efficiency has not been theoretically established and numerically validated; and mechanisms of how to reduce the time complexity meanwhile solving the scheduling problem to an optimal solution are not fully understood and rigorously defined. In this dissertation, a control method that eliminates the infeasible problem at any planning horizon is first explicitly explained and defined based on a time-speed-independent trajectory planning and scheduling model. Secondly, this dissertation theoretically defines the optimal control framework via analyzing various control methods in terms of intersection capacity, throughput and delay. Furthermore, this dissertation theoretically analyzes the mechanism of the scheduling problem and designs an exact algorithm to further reduce the time complexity. Through theoretical analyses of the properties of the scheduling problem, the reasons that the time complexity can be reduced are fundamentally explained. The results first validate that the defined control framework can adapt to extremely high traffic demand scenarios with feasible solutions at any planning horizon for all CAVs. Under extensive sensitivity analyses, the theoretical definition of the optimal control framework is validated in terms of maximizing the intersection efficiency. Moreover, numerical examples validate that a proposed scheduling algorithm finds an optimal solution with lower computation time and time complexity

A Gain Scheduling Optimization Method Using Genetic Algorithms

Gain scheduling. the traditional method of providing adaptive control to a nonlinear system, has long been an ad hoc design process. Until recently; little theoretical guidance directed this practitioners\u27 art. For this reason a systematic study of this design process and its potential for optimization has never been accomplished. Additionally, the nonlinearities and the large search space involved in gain scheduling also precluded such an optimization study. Traditionally, the gain scheduling process has been some variation of a linear interpolation between discrete design points. By using powerful non-traditional optimization tools such as genetic algorithms there are ways of improving this design process. This thesis utilizes the power of genetic algorithms to optimally design a gain schedule. First, a design methodology is validated on a simple pole placement problem, then demonstrated for an F-18 Super-maneuverable Fighter. From this experience, a general gain scheduling design process is developed and presented

Optimal economic planning and control for the management of ecosystems

In recent years the interest on sustainable systems has increased significantly. Among the many interested problems, creating and restoring sustainable ecosystems is a challenging and complex problem. One of the fundamental problems within this area is the imbalance between species that have a predator-prey relationship. Solutions involving management have become an integral player in many environments. Management systems typically use ad hoc methods to develop harvesting policies to control the populations of species to desired numbers. In order to amalgamate intelligence and structure, ecological systems require a diverse research effort from three primary fields: ecology, economics, and control theory. In this thesis, all three primary fields aforementioned are researched to develop a theoretical framework that includes an optimal trajectory planning system that exploits an ecosystem to maximize profits for the supporting community, and a robust control system design to track the optimal trajectories subjected to exogenous disturbances. Population ecology is used to select a model that identifies the key characteristics a management system needs to understand the behavior of the natural environment. A bioeconomic model is developed to relate the species populations to revenue. The nonlinear ecosystem is transformed into a linear parameter-varying (LPV) system that is then controlled using hinf synthesis and the gain scheduling methodology. The consequences of the results in this thesis are that optimal trajectories of an ecosystem can be obtained by constructing and solving a nonlinear programming problem (NLP), and the LPV based gain scheduling approach produces a robust controller that rejects disturbances and advises quality control policies to the manager an ecosystem. The LPV controller achieves comparable profits with satisfactory tracking performance while minding the induced costs of its high frequency output. Implications of constraining the control effort when designing for robustness are observed. Overall, the theoretical framework provides a solid foundation for future research on the understanding and improvement of ecosystem management

Robust scheduled control of longitudinal flight with handling quality satisfaction

Classic flight control systems are still widely used in the industry because of acquired experience and good understanding of their structure. Nevertheless, with more stringent constraints, it becomes difficult to easily fulfil all the criteria with these classic control laws. On the other hand, modern methods can handle many constraints but fail to produce low order controllers. The following methodology proposed in this paper addresses both classic and modern flight control issues, to offer a solution that leverages the strengths of both approaches. First, an H∞ synthesis is performed in order to get controllers which satisfy handling qualities and are robust withrespect to mass and centre of gravity variations. These controllers are then reduced and structured by using robust modal control techniques. In conclusion, a self-scheduling technique is described that will schedule these controllers over the entire flight envelope

Adaption of structured analysis design techniques methodology for construction project planning

The construction industry has been heavily criticised by researchers and governmental organisations for its performance especially excessive delay. Ballard and Howell (2003) indicated that only about 50% of the tasks on weekly work plans are completed by the end of the plan week. This is a result of a lack of either effective project planning or effective production control. It therefore seems the traditional approach of planning is insufficient to meet the current demand and complexity of construction projects. This paper proposes to critically evaluate the adaptation of Structured Analysis Design Techniques (SADT) methodology as a tool for project planning. SADT which was further developed into IDEF (Integrated Definition) techniques claims to be a complete methodology to provide the means of understanding complex production systems and aid the implementation of change. The use of this methodology has led to process improvement. The research uses a literature review followed by interviews with academics and practitioners to investigate their knowledge and understanding of SADT (IDEFO). The results of the interviews indicated that SADT (IDEFO) methodology is seldom known and used in the construction industry. However, this study indicates that SADT methodology appears to be an effective project planning tool. This study contributes to the limited project planning techniques in construction industry by exploring the possible adaption of SADT

Suggestions to Improve Lean Construction Planning

The Last Planner System® has been one of the most popular lean construction tools that offers a solution to tackle the problems of production management on construction sites. Since its inception almost 20 years ago, construction companies across the world have implemented Last Planner with reported success. However, even as Last Planner was originally designed to address some shortcomings of the CPM method, a particular shortcoming – namely task continuity was not addressed directly. Also, excepting PPC and Reasons for Non Completion charts, there are no explicit visual tools offered by the Last Planner system. On the other hand, Line of Balance based approaches intrinsically support the consideration of task continuity, and offer a basic visual management approach in schedule representation. With some exceptions, Line of Balance is seen as a special technique applicable only in linear or repetitive work based schedules. The authors suggest that i) there is a need for a robust theory of planning and scheduling and ii) there is a need for a more suitable approach that addresses critical aspects of planning and scheduling function for example by integrating Line of Balance and Last Planner to provide a more robust support for construction scheduling

If CPM is so bad, why have we been using it so long?

Why has the Critical Path Method (CPM) been used so widely for so long given its inability to produce predictable outcomes? For shedding light on this paradox, the formative period of the CPM is analysed from two main angles. First, how was the CPM embedded into the construction management practice? Second, what was the methodological underpinning of the development of the CPM? These questions are researched through a literature review. In terms of embeddedness into practice, it turns out that the CPM morphed from being a way of production control, into a method for contract control. In consequence, the promotion of the CPM by owners has been crucial for pushing this method to be the mainstream approach to scheduling and production control. Regarding methodological underpinning, it turns out that the CPM was developed as a way of optimization, as part of the quantitative methods movement. This movement was largely based on the axiomatic approach to research. In good alignment with that approach, there was no attempt to empirically test quantitative models and their outcomes. In this context, the unrealistic assumptions and conceptualizations in CPM did not surface in forty years. These results are argued to be helpful in critical discussions on the role and merits of CPM and on the methodologies to be used in construction management research