京都大学 / 0048 / 新制・課程博士 / 博士(情報学) / 甲第22808号 / 情博第738号 / 新制||情||126(附属図書館) / 京都大学大学院情報学研究科システム科学専攻 / (主査)教授 大塚 敏之, 教授 加納 学, 教授 太田 快人 / 学位規則第4条第1項該当 / Doctor of Informatics / Kyoto University / DFAM

The process to induct air into engine cylinders, via the air inlet system and cylin-der port valves, is referred to as the "gas-exchange". Control is achieved by theturbo-charger, the intake throttle plate and the variable valve timing (VVT) sys-tem. These actuation systems traditionally use separate control with indepen-dent SISO feedback. There are however physical couplings that affect the con-trol performance. This thesis work looks at MPC control methods for a robustcontrol strategy. MPC methods are typically used for systems with slow dynam-ics, due to computational limits. But new advances in CPU performance shouldallow for real-time implementations for engine control. / <p>Redan framlagt exjobbet</p>

Statistics show that unstable articulated vehicles pose a serious threat to the occupants driving them as well as the occupants of the vehicles around them. An articulated vehicle typically experiences three types of instability: snaking, jack-knifing and rollover. An articulated vehicle subjected to any of these instabilities can result in major accidents. It is also known that many individuals are unaware of how to properly tow or pack a loaded articulated vehicle. These individuals are, therefore, at a high risk of causing the vehicle system to become unstable. It can hence be confidently said that a method in which an articulated vehicle can stabilise itself is a worthy research question. The method that is implemented in this study is to create a control system, using Nonlinear Model Predictive Control (NMPC), that has the capability of stabilising an articulated vehicle by applying torque vectoring to the trailer. In order for this control system to be applied, a nonlinear articulated vehicle MSC ADAMS model was constructed. The NMPC controller works by using a nonlinear explicit model to predict the future states of the vehicle and then finding the optimal left and right braking forces of the trailer by minimising the cost function using least squares minimisation. The cost function includes the towing vehicle yaw rate, trailer yaw rate and hitch angle and is minimised by minimising the error between the desired vehicle states and the actual states. It was found that the NMPC is capable of not only preventing instability but also causes the vehicle system to behave as if the trailer is unloaded. This conclusion means that this type of control system can be used on all types of articulated vehicles and shall ensure the safety of not only the vehicle occupants but other road users as well. Unfortunately, due to the impact of the 2020 COVID-19 pandemic, the experimental validation of the model had to be delayed significantly. It is for this reason that the experimental validation for the controller could not be done. / Dissertation (MEng (Mechanical Engineering))--University of Pretoria, 2021. / SATC VDG UP / Mechanical and Aeronautical Engineering / MEng (Mechanical Engineering) / Unrestricted

This thesis focuses on the possibilities of application of nonlinear model predictive control for electric drives. Specifically, for drives with a permanent magnet synchronous motor. The thesis briefly describes the properties of this type of drive and presents its mathematical model. After that, a nonlinear model of predictive control and methods of nonlinear optimization, which form the basis for the controller output calculation, are described. As it is used in the proposed algorithm, the Active set method is described in more detail. The thesis also includes simulation experiments focusing on the choice of the objective function on the ability to control the drive. The same effect is examined for the different choices of the length of the prediction horizon. The end of the thesis is dedicated to the comparison between the proposed algorithm and commonly used field oriented control. The computational demands of the proposed algorithm are also measured and compared to the used sampling time.

The concept of lights-out process control is explored in this work (specifically pertaining to the minerals processing industry). The term is derived from lights-out manufacturing, which is used in discrete component manufacturing to describe a fully automated production line, i.e. with no human intervention. Lights-out process control is therefore defined as the fully autonomous operation of a processing plant (as achieved through automatic process control), without operator interaction. / Thesis (PhD)--University of Pretoria, 2017. / National Research Foundation (NRF) / Electrical, Electronic and Computer Engineering / PhD / Unrestricted

<div>Collision avoidance design plays an essential role in autonomous vehicle technology. It's an attractive research area that will need much experimentation in the future. This research area is very important for providing the maximum safety to automated vehicles, which have to be tested several times under different circumstances for safety before use in real life. This thesis proposes a method for designing and presenting a collision avoidance maneuver by using a model predictive controller with a moving obstacle for automated vehicles. It consists of a plant model, an adaptive MPC controller, and a reference trajectory. The proposed strategy applies a dynamic bicycle model as the plant model, adaptive model predictive controller for the lateral control, and a custom reference trajectory for the scenario design. The model was developed using the Model Predictive Control Toolbox and Automated Driving Toolbox in Matlab. Builtin tools available in Matlab/Simulink were used to verify the modeling approach and analyze the performance of the system. The major contribution of this thesis work was implementing a novel dynamic obstacle avoidance control method for automated vehicles. The study used validated parameters obtained from previous research. The novelty of this research was performing the studies using a MPC based controller instead of a sliding mode controller, that was primarily used in other studies. The results obtained from the study are compared with the validated models. The comparisons consisted of the lateral overlap,lateral error, and steering angle simulation results between the models. Additionally,this study also included outcomes for the yaw angle. The comparisons and other outcomes obtained in this study indicated that the developed control model produced reasonably acceptable results and recommendations for future studies.</div>

Integrating renewable energy and energy storage systems provides a way of operating the electrical grid system more energy efficiently and stably. Thermal storage and batteries are the most common devices for integration. One approach to integrating thermal storage on site is to use ice in combination with the cooling system. The use of ice storage can enable a change in the time variation of electrical usage for cooling in response to variations in PV availability, utility prices, and cooling requirements.A number of studies can be found in the literature that address optimal operation of onsite PV systems with batteries or ice storage. However, although it is a natural and practical question, it is not clear which integrated storage system performs better in terms of overall economics. Ice storage has low initial and maintenance costs, but there is an efficiency penalty for charging of storage and it can only shift electrical loads associated with building cooling requirements. A battery’s round-trip efficiency,on the other hand, is quite consistent and batteries can be used to shift both HVAC and non-HVAC loads. However, batteries have greater initial costs and a significantly shorter life. This research presents a tool and provides a case study for comparing life-cycle economics of battery and ice storage systems for a commercial building that has chillers for cooling and an on-site photovoltaic system. A model predictive control algorithm was developed and implemented in simulation for the two systems in order to compare optimal costs. The effect of ice storage and battery sizing were studied in order to determine the best storage sizes from an economic perspective and to provide a fair comparison

Small Unmanned Aircraft Systems (UAS) are considered the stepping stone for the integration of civil unmanned vehicles in the National Airspace System (NAS) because of their low cost and risk. Such systems are aimed at a variety of applications including search and rescue, surveillance, communications, traffic monitoring and inspection of buildings, power lines and bridges. Amidst these systems, small helicopters play an important role because of their capability to hold a position, to maneuver in tight spaces and to take off and land from virtually anywhere. Nevertheless civil adoption of such systems is minimal, mostly because of regulatory problems that in turn are due to safety concerns. This dissertation examines the risk to safety imposed by UAS in general and small helicopters in particular, focusing on accidents resulting in a ground impact. To improve the performance of small helicopters in this area, the use of autonomous autorotation is proposed. This research goes beyond previous work in the area of autonomous autorotation by developing an on-line, model-based, real-time controller that is capable of handling constraints and different cost functions. The approach selected is based on a non-linear model-predictive controller, that is augmented by a neural network to improve the speed of the non-linear optimization. The immediate benefit of this controller is that a class of failures that would otherwise result in an uncontrolled crash and possible injuries or fatalities can now be accommodated. Furthermore besides simply landing the helicopter, the controller is also capable of minimizing the risk of serious injury to people in the area. This is accomplished by minimizing the kinetic energy during the last phase of the descent. The presented research is designed to benefit the entire UAS community as well as the public, by allowing for safer UAS operations, which in turn also allow faster and less expensive integration of UAS in the NAS.

This dissertation is focused on the modeling and robust model-based control of high degree-of-freedom (DoF) systems. While most of the contributions are applicable to any difficult-to-model system, this dissertation focuses specifically on applications to large-scale soft robots because their many joints and pressures constitute a high-DoF system and their inherit softness makes them difficult to model accurately. First a joint-angle estimation and kinematic calibration method for soft robots is developed which is shown to decrease the pose prediction error at the end of a 1.5 m robot arm by about 85\%. A novel dynamic modelling approach which can be evaluated within microseconds is then formulated for continuum type soft robots. We show that deep neural networks (DNNs) can be used to approximate soft robot dynamics given training examples from physics-based models like the ones described above. We demonstrate how these machine-learning-based models can be evaluated quickly to perform a form of optimal control called model predictive control (MPC). We describe a method of control trajectory parameterization that enables MPC to be applied to systems with more DoF and with longer prediction horizons than previously possible. We show that this parameterization decreases MPC's sensitivity to model error and drastically reduces MPC solve times. A novel form of MPC is developed based on an evolutionary optimization algorithm that allows the optimization to be parallelized on a computer's graphics processing unit (GPU). We show that this evolutionary MPC (EMPC) can greatly decrease MPC solve times for high DoF systems without large performance losses, especially given a large GPU. We combine the ideas of machine learned DNN models of robot dynamics, with parameterized and parallelized MPC to obtain a nonlinear version of EMPC which can be run at higher rates and find better solutions than many state-of-the-art optimal control methods. Finally we demonstrate an adaptive form of MPC that can compensate for model error or changes in the system to be controlled. This adaptive form of MPC is shown to inherit MPC's robustness to completely unmodeled disturbances and adaptive control's ability to decrease trajectory tracking errors over time.

<p>Commercial buildings not only have significant impacts on occupants’ well-being, but also contribute to more than 19% of the total energy consumption in the United States. Along with improvements in building equipment efficiency and utilization of renewable energy, there has been significant focus on the development of advanced heating, ventilation, and air conditioning (HVAC) system controllers that incorporate predictions (e.g., occupancy patterns, weather forecasts) and current state information to execute optimization-based strategies. For example, model predictive control (MPC) provides a systematic implementation option using a system model and an optimization algorithm to adjust the control setpoints dynamically. This approach automatically satisfies component and operation constraints related to building dynamics, HVAC equipment, etc. However, the wide adaptation of advanced controls still faces several practical challenges: such approaches involve significant engineering effort and require site-specific solutions for complex problems that need to consider uncertain weather forecast and engaging the building occupants. This thesis explores smart building operation strategies to resolve such issues from the following three aspects. </p> <p>First, the thesis explores a stochastic model predictive control (SMPC) method for the optimal utilization of solar energy in buildings with integrated solar systems. This approach considers the uncertainty in solar irradiance forecast over a prediction horizon, using a new probabilistic time series autoregressive model, calibrated on the sky-cover forecast from a weather service provider. In the optimal control formulation, we model the effect of solar irradiance as non-Gaussian stochastic disturbance affecting the cost and constraints, and the nonconvex cost function is an expectation over the stochastic process. To solve this optimization problem, we introduce a new approximate dynamic programming methodology that represents the optimal cost-to-go functions using Gaussian process, and achieves good solution quality. We use an emulator to evaluate the closed-loop operation of a building-integrated system with a solar-assisted heat pump coupled with radiant floor heating. For the system and climate considered, the SMPC saves up to 44% of the electricity consumption for heating in a winter month, compared to a well-tuned rule-based controller, and it is robust, imposing less uncertainty on thermal comfort violation.</p> <p>Second, this thesis explores user-interactive thermal environment control systems that aim to increase energy efficiency and occupant satisfaction in office buildings. Towards this goal, we present a new modeling approach of occupant interactions with a temperature control and energy use interface based on utility theory that reveals causal effects in the human decision-making process. The model is a utility function that quantifies occupants’ preference over temperature setpoints incorporating their comfort and energy use considerations. We demonstrate our approach by implementing the user-interactive system in actual office spaces with an energy efficient model predictive HVAC controller. The results show that with the developed interactive system occupants achieved the same level of overall satisfaction with selected setpoints that are closer to temperatures determined by the MPC strategy to reduce energy use. Also, occupants often accept the default MPC setpoints when a significant improvement in the thermal environment conditions is not needed to satisfy their preference. Our results show that the occupants’ overrides can contribute up to 55% of the HVAC energy consumption on average with MPC. The prototype user-interactive system recovered 36% of this additional energy consumption while achieving the same overall occupant satisfaction level. Based on these findings, we propose that the utility model can become a generalized approach to evaluate the design of similar user-interactive systems for different office layouts and building operation scenarios. </p> <p>Finally, this thesis presents an approach based on meta-reinforcement learning (Meta-RL) that enables autonomous optimal building controls with minimum engineering effort. In reinforcement learning (RL), the controller acts as an agent that executes control actions in response to the real-time building system status and exogenous disturbances according to a policy. The agent has the ability to update the policy towards improving the energy efficiency and occupant satisfaction based on the previously achieved control performance. In order to ensure satisfactory performance upon deployment to a target building, the agent is trained using the Meta-RL algorithm beforehand with a model universe obtained from available building information, which is a probability measure over the possible building dynamical models. Starting from what is learned in the training process, the agent then fine-tunes the policy to adapt to the target building based on-site observations. The control performance and adaptability of the Meta-RL agent is evaluated using an emulator of a private office space over 3 summer months. For the system and climate under consideration, the Meta-RL agent can successfully maintain the indoor air temperature within the first week, and result in only 16% higher energy consumption in the 3^rd month than MPC, which serves as the theoretical upper performance bound. It also significantly outperforms the agents trained with conventional RL approach. </p>