Adaptive performance optimization for large-scale traffic control systems

In this paper, we study the problem of optimizing (fine-tuning) the design parameters of large-scale traffic control systems that are composed of distinct and mutually interacting modules. This problem usually requires a considerable amount of human effort and time to devote to the successful deployment and operation of traffic control systems due to the lack of an automated well-established systematic approach. We investigate the adaptive fine-tuning algorithm for determining the set of design parameters of two distinct mutually interacting modules of the traffic-responsive urban control (TUC) strategy, i.e., split and cycle, for the large-scale urban road network of the city of Chania, Greece. Simulation results are presented, demonstrating that the network performance in terms of the daily mean speed, which is attained by the proposed adaptive optimization methodology, is significantly better than the original TUC system in the case in which the aforementioned design parameters are manually fine-tuned to virtual perfection by the system operators

State-of-the-art on research and applications of machine learning in the building life cycle

Fueled by big data, powerful and affordable computing resources, and advanced algorithms, machine learning has been explored and applied to buildings research for the past decades and has demonstrated its potential to enhance building performance. This study systematically surveyed how machine learning has been applied at different stages of building life cycle. By conducting a literature search on the Web of Knowledge platform, we found 9579 papers in this field and selected 153 papers for an in-depth review. The number of published papers is increasing year by year, with a focus on building design, operation, and control. However, no study was found using machine learning in building commissioning. There are successful pilot studies on fault detection and diagnosis of HVAC equipment and systems, load prediction, energy baseline estimate, load shape clustering, occupancy prediction, and learning occupant behaviors and energy use patterns. None of the existing studies were adopted broadly by the building industry, due to common challenges including (1) lack of large scale labeled data to train and validate the model, (2) lack of model transferability, which limits a model trained with one data-rich building to be used in another building with limited data, (3) lack of strong justification of costs and benefits of deploying machine learning, and (4) the performance might not be reliable and robust for the stated goals, as the method might work for some buildings but could not be generalized to others. Findings from the study can inform future machine learning research to improve occupant comfort, energy efficiency, demand flexibility, and resilience of buildings, as well as to inspire young researchers in the field to explore multidisciplinary approaches that integrate building science, computing science, data science, and social science

Hydraulic pressure and flow control of injection moulding

SIGLEAvailable from British Library Document Supply Centre-DSC:DXN041811 / BLDSC - British Library Document Supply CentreGBUnited Kingdo

Intelligent instrumentation, control and monitoring of precision motion systems

Ph.DDOCTOR OF PHILOSOPH

CIR Parametric Rules Precocity For Ranging Error Mitigation In IR-UWB

The cutting-edge technology to support high ranging accuracy within the indoor environment is Impulse Radio Ultra Wide Band (IR-UWB) standard. Besides accuracy, IR-UWB’s low-complex architecture and low power consumption align well with mobile devices. A prime challenge in indoor IR-UWB based localization is to achieve a position accuracy under non-line-of-sight (NLOS) and multipath propagation (MPP) conditions. Another challenge is to achieve acceptable accuracy in the conditions mentioned above without any significant increase in latency and computational burden. This dissertation proposes a solution for addressing the accuracy and reliability problem of indoor localization system satisfying acceptable delay or computational complexity overhead. The proposed methodology is based on rules for identification of line-of-sight (LOS) and NLOS and the range error bias estimation and correction due to NLOS and MPP conditions. The proposed methodology provides accuracy for two major application domains, namely, wireless sensor networks (WSNs) and indoor tracking and navigation (ITN). This dissertation offers two different solutions for the localization problem. The first solution is a rules-based classification of LOS / NLOS and geometric-based range correction for WSN. In the first solution, the Boolean logic based classification is designed for identification of LOS/NLOS. The logic is based on channel impulse response (CIR) parameters. The second solution is based on fuzzy logic. The fuzzy based solution is appealing well for the stringent precision requirements in ITN. In this solution, the parametric Boolean logic from the first solution is converted and expanded into rules. These rules are implemented into a fuzzy logic based mechanism for designing a fuzzy inference system. The system estimates the ranging errors and correcting unmitigated ranges. The expanded rules and designed methodology are based on theoretical analysis and empirical observations of the parameters. The rules accommodate the parameters uncertainties for estimating the ranging error through the relationship between the input parameters uncertainties and ranging error using fuzzy inference mechanism. The proposed solutions are evaluated using real-world measurements in different indoor environments. The performance of the proposed solutions is also evaluated in terms of true classification rate, residual ranging errors’ cumulative distributions and probability density distributions, as well as outage probabilities. Evaluation results show that the true classification rate is more than 95%. Moreover, using the proposed fuzzy logic based solution, the residual errors convergence of 90% is attained for error threshold of 10 cm, and the reliability of the localization system is also more than 90% for error threshold of 15 cm

Online power management with embedded optimization for a multi-source hybrid with real time applications

The focus of this thesis is to develop a suitable power management optimization strategy for a three-source hybrid vehicle powertrain. This strategy takes into account the integration of optimized parameters that limit the battery and fuel cell current by utilizing a third power source, namely supercapacitor. The goal is to develop a modular structure with decoupled online and offline parts such that implementation in case of real driving conditions is feasible. Based on the literature review it can be concluded that providing optimal solutions in terms of multiple objectives online is an issue. Adaption of optimized control strategy to real driving data is another concern. The developed strategy employs an online rule-based control with embedded offline-optimized parameters. The parameters are optimized with respect to multiple and conflicting objectives such as fuel consumption and state-of-charge deviation minimization. By a suitable selection of parameters, operation of all three sources within desired working ranges is possible, keeping in mind the load demand. By varying the weights between the objectives, one or more objectives can be given more priority than others. The application of this concept to fuel cell-battery-supercapacitor hybrid is discussed in this thesis. Detailed modeling of all components along with verification and plausibility assessment is done. For the purpose of experimental validation, the real powertrain components are replaced by controllable power sources and sinks that emulate the dynamics of real components. Finally, a brief concept is presented to integrate the developed power management optimization in real driving scenarios. For validation/verification purposes, a driving simulator environment is connected to the experimental hybrid electric vehicle set-up and with the help of an illustrative example, the desired predicted optimal values are calculated online and displayed to the human driver by a suitable interface. The absence of online tuning of controller parameters in this example is counteracted by developing a concept based on literature. With the help of this concept, the adaption of the power management control concept, developed in this thesis, can be realized

Design and Implementation of Control Techniques of Power Electronic Interfaces for Photovoltaic Power Systems

The aim of this thesis is to scrutinize and develop four state-of-the-art power electronics converter control techniques utilized in various photovoltaic (PV) power conversion schemes accounting for maximum power extraction and efficiency. First, Cascade Proportional and Integral (PI) Controller-Based Robust Model Reference Adaptive Control (MRAC) of a DC-DC boost converter has been designed and investigated. Non-minimum phase behaviour of the boost converter due to right half plane zero constitutes a challenge and its non-linear dynamics complicate the control process while operating in continuous conduction mode (CCM). The proposed control scheme efficiently resolved complications and challenges by using features of cascade PI control loop in combination with properties of MRAC. The accuracy of the proposed control system’s ability to track the desired signals and regulate the plant process variables in the most beneficial and optimised way without delay and overshoot is verified. The experimental results and analysis reveal that the proposed control strategy enhanced the tracking speed two times with considerably improved disturbance rejection. Second, (P)roportional Gain (R)esonant and Gain Scheduled (P)roportional (PR-P) Controller has been designed and investigated. The aim of this controller is to create a variable perturbation size real-time adaptive perturb and observe (P&O) maximum power point tracking (MPPT) algorithm. The proposed control scheme resolved the drawbacks of conventional P&O MPPT method associated with the use of constant perturbation size that leads to a poor transient response and high continuous steady-state oscillations. The prime objective of using the PR-P controller is to utilize inherited properties of the signal produced by the controller’s resonant path and integrate it to update best estimated perturbation that represents the working principle of extremum seeking control (ESC) to use in a P&O algorithm that characterizes the overall system learning-based real time adaptive (RTA). Additionally, utilization of internal dynamics of the PR-P controller overcome the challenges namely, complexity, computational burden, implantation cost and slow tracking performance in association with commonly used soft computing intelligent systems and adaptive control strategies. The experimental results and analysis reveal that the proposed control strategy enhanced the tracking speed five times with reduced steady-state oscillations around maximum power point (MPP) and more than 99% energy extracting efficiency.Third, the interleaved buck converter based photovoltaic (PV) emulator current control has been investigated. A proportional-resonant-proportional (PR-P) controller is designed to resolve the drawbacks of conventional PI controllers in terms of phase management which means balancing currents evenly between active phases to avoid thermally stressing and provide optimal ripple cancellation in the presence of parameter uncertainties. The proposed controller shows superior performance in terms of 10 times faster-converging transient response, zero steady-state error with significant reduction in current ripple. Equal load sharing that constitutes the primary concern in multi-phase converters has been achieved with the proposed controller. Implementing of robust control theory involving comprehensive time and frequency domain analysis reveals 13% improvement in the robust stability margin and 12-degree bigger phase toleration with the PR-P controller. Fourth, a symmetrical pole placement Method-based Unity Proportional Gain Resonant and Gain Scheduled Proportional (PR-P) Controller has been designed and investigated. The proposed PR-P controller resolved the issues associated with the use of the PI controller which are tracking repeating control input signal with zero steady-state and mitigating the 3rd order harmonic component injected into the grid for single-phase PV systems. Additionally, the PR-P controller has overcome the drawbacks of frequency detuning in the grid and increase in the magnitude of odd number harmonics in the system that constitute the common concerns in the implementation of conventional PR controller. Moreover, the unprecedented design process based on changing notch filter dynamics with symmetrical pole placement around resonant frequency overcomes the limitations that are essentially complexity and dependency on the precisely modelled system. The verification and validation process of the proposed control schemes has been conducted using MATLAB/Simulink and implementing MATLAB/Simulink/State flow on dSPACE Real-time-interface (RTI) 1007 processor, DS2004 High-Speed A/D and CP4002 Timing and Digital I/O boards