京都大学 / 新制・課程博士 / 博士(情報学) / 甲第24266号 / 情博第810号 / 新制||情||136(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 石井 信, 教授 森本 淳 / 学位規則第4条第1項該当 / Doctor of Informatics / Kyoto University / DFAM

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There are increasing economic incentives within the chemical process industry towards demand driven operation with product diversification, requiring flexible operation in responsive plants. In continuous processes, this is realized through steady-state transitions but requires consideration of process dynamics arising from operation that is inherently transient in nature. The steady-state economic optimum is typically defined at the intersection of constraints, and requires multivariable control with optimal constraint handling capabilities. Thus, constrained model predictive control is well-suited to realize the profit potential at the economic optimum. In this thesis, feasible and optimal steady-state transitions are achieved using reference management with consideration of the closed-loop dynamics of constrained model predictive control. The supervisory control scheme is used to determine the optimal setpoint trajectory which is subsequently tracked by regulatory control, incorporating feedback for the rejection of high frequency disturbances and eliminating steady-state offset in the presence of model mismatch. The separation of economic and control objectives enables the lower level to be tuned for stability and the upper level to be tuned for performance. The mathematical formulation results in a multi-level optimization problem with an economic objective function at the upper level, and a series of control performance objective functions arising from constrained model predictive control at the lower levels. The solution strategy proposed converts the multi-level optimization problem into a single-level optimization problem using the Karush-Kuhn-Tucker conditions, and solves the resulting complementarity conditions using an interior point approach. Alternative objective formulations are investigated based on maximizing profit during transient operation. The first formulation is typically based on a quadratic objective function minimizing the transition time, indirectly improving economic operation by reducing the amount of off-specification product produced. The second formulation is based on the explicit consideration of economics. The profit calculated during transient operation is based on the difference between the revenue generated by the production of acceptable product within specified univariate product quality bands, and the operational costs of raw materials and utilities. The resulting linear objective function is further extended to incorporate control performance considerations to improve conditioning for gradient based optimization. The proposed methodology is applied to a single-input single-output linear system, demonstrating the potential benefits of simultaneous rather than sequential optimization in terms of computational efficiency and solution reliability. Alternative objective function and constraint formulations are investigated, and the effect on the optimal solution assessed. In particular, the possibility of indeterminacy is shown and handled using hierarchical optimization. The methodology is also demonstrated on additional examples including non-minimum phase systems and multi-input multi-output linear systems. Application to a multi-input multi-output nonlinear system corresponding to styrene polymerization using the proposed methodology is detailed. The set of differential and algebraic equations defining the process is discretized using orthogonal collocation on finite elements. The optimal operation during grade transitions based on explicit consideration of economics is determined, and additional improvements realized by manipulating the production rate. Finally, reference management with online re-optimization is investigated for a single-input single-output linear system based on a bias update, and the improvement in closed-loop performance assessed for output disturbances and model mismatch. The methodology is also demonstrated on a multi-input multi-output system based on a linear model when applied to the nonlinear process. The proposed methodology developed for steady-state transitions may also be applied to batch operation, startups and shutdowns. Future extensions include analysis of closed-loop stability due to the incorporation of feedback within the cascade control scheme, and the explicit consideration of uncertainty. / Thesis / Master of Applied Science (MASc)

The rigorous quality criterion and intricate shapes of plastic injection molded parts require molders to improve process control systems in order to keep their competitive status in the market. In recent research, various advanced control algorithms are employed to develop in-line process controllers. In modem controllers design, in-mold process variables play a very important role in connecting machine variables and quality variables. Model-based predictive control (MPC) is used to investigate the controllability of cavity pressure and cavity temperature within a cycle or cycle-to-cycle. The objective of the present work is to demonstrate a procedure to develop MPC controllers based on simulation results. Moldflow® was used to simulate the injection molding process for a thin-wall cell phone cover. Cavity pressure profiles and part surface temperature profiles were extracted to develop the dynamic model for controller design. Thermal analysis for the cooling stage was investigated by ANSYS® FEM software. Mold surface temperature profiles were used for controller design. Dynamic matrix control, a type of MPC control, was developed by using Matlab® MPC Toolbox. A single-input/single-output MPC controller was developed to control cavity pressure in filling stage by manipulating injection flow rate. Simulation studies were then used to develop a MPC controller to implement a closed-loop control. The controller performed very well to control the pressure profile to trace the set-point, even with melt temperature or mold temperature change. Two MPC controllers were developed to control cavity surface cycle average temperature by manipulating coolant flow rate and coolant temperature. Both controllers show good controllability for cycle average temperature control. A two-input/two-output DMC controller was implemented to control cavity pressure and part surface temperature in the packing stage. Packing pressure and mold temperature were manipulated to trace the controlled profile set-points in each sampling time. Results shows that the controller was able to meet the set-point very well, for an unmeasured disturbance, based on a closed-loop test. All the controllers were developed based on simulation results, which will have some differences with real production data. Therefore, the model parameter and controller tuning parameter should be validated and modified if needed before real-time application. / Thesis / Master of Applied Science (MASc)

There is a constant demand to use more advanced control methods in a wider field of applications. Model Predictive Control (MPC) is one such control method, based on recurrently solving an optimization problem for determining the optimal control signal. To solve an optimization problem can be a complex task, and it is difficult to determine beforehand how long time it will take. For a high-speed application with limited computational power, it is necessary to have an efficient algorithm to solve the optimization problem and an accurate estimation of the longest solution time. Recent research has given methods both to solve quadratic programs efficiently and to find an upper limit on the solution times. These methods are in this thesis applied to a control system based on linear MPC for the Crazyflie 2.0 nano quadcopter. The implementation is made completely online on the processor of the quadcopter, with limited computational power. A problem with the size of 36 optimization variables and 60 constraints is solved at a frequency of 100 Hz on the quadcopter. Apart from implementing MPC, a framework for computing an upper limit to the solution time has been tested. This gives a possibility to certify the formulation for real-time applications up to a well-defined maximum frequency. An implementation is shown where the framework has been used in practice to control a quadcopter flying with a real-time certified implementation of MPC. / Det finns en ständig efterfrågan för mer avancerade metoder för reglering. Modellprediktiv reglering (MPC) är en sådan avancerad metod som kräver att ett optimeringsproblem löses varje gång en ny styrsignal ska beräknas. Att lösa optimeringsproblem kan vara en komplicerad uppgift, och det är svårt att på förhand veta hur lång beräkningstid som krävs. För att MPC ska kunna användas i tillämpningar i hög hastighet och med begränsad beräkningskraft är det nödvändigt att ha en effektiv lösningsalgoritm, och även en korrekt uppskattning av den längsta lösningstiden som behövs. Aktuell forskning har gett metoder både för att effektivt lösa kvadratiska optimeringsproblem, samt för att kunna hitta en övre gräns på beräkningstiden. I den här rapporten appliceras dessa metoder på ett styrsystem baserat på MPC i en Crazyflie 2.0, vilket är en nanodrönare. Styrsystemet är implementerat helt och hållet på drönarens processor, med den begränsade datorkraft som det innebär. Ett problem med en storlek på 36 optimeringsvariabler och 60 bivillkor lösesmed en frekvens på 100 Hz. Förutom att implementera MPC har även en metod för att bestämma en övre gräns på beräkningstiden testats. Det ger en möjlighet att certifiera styrstytemetför att garanterat kunna beräkna en ny styrsignal inom den övre tiden, vilket i sin tur innebär att styrsytemet kan certificeras för realtidsanvändning i långsammare frekvenser än den övre gränsen. I rapporten visas en certifierad implementation, och data från flygning med en certifierad regulator finns med i resultatet.

Yes / A milk pasteurization process, a nonlinear process and multivariable interacting system, is difficult to control by the conventional on-off controllers since the on-off controller can handled the temperature profiles for milk and water oscillating over the plant requirements. The multi-variable control approach with model predictive control (MPC) is proposed in this study. The proposed algorithm was tested for control of a milk pasteurization process in three cases of simulation such as set point tracking, model mismatch, difference control and prediction horizons, and time sample. The results for the proposed algorithm show the well performance in keeping both the milk and water temperatures at the desired set points without any oscillation and overshoot and giving less drastic control action compared to the cascade generic model control (GMC) strategy.

This dissertation aims to reduce urban traffic congestion with street traffic signal control. The traffic signal controllers in the U.S. follow the National Electrical Manufacturing Association Standards (NEMA Standards). In a NEMA controller, the control parameters for a coordinated control are cycle, green splits, and offset. This dissertation proposed a virtual phase-link concept and developed a macroscopic model to describe the dynamics of a traffic network. The coordinated optimal splits control problem was solved using model predictive control. The outputs of the solution are the green splits that can be used in NEMA controllers. I compared the proposed method with a state-of-the-practice signal timing software under coordinated-actuated control settings. It was found that the proposed method significantly outperformed the benchmarking method. I compared the proposed NEMA-based virtual phase-link model and a Max Pressure controller model using Vissim. It was found that the virtual phase-link method outperformed two control strategies and performed close, but not as good as, the Max Pressure control strategy. The disadvantage of the virtual phase-link method stemmed from the waste of green time during a fixed control cycle length and the delay which comes from the slowing down of platoon during a road link to allow vehicles to switch lanes. Compared to the Max Pressure control strategy, the virtual phase-link method can be implemented by any traffic controller that follows the NEMA standards. The real-time requirement of the virtual phase-link method is not as strict as the Max Pressure control strategy. I introduced the offsets optimization into the virtual phase-link method. I modeled the traffic arrival pattern based on the optimization results from the virtual phase-link control method. I then derived a phase delay function based on the traffic arrival pattern. The phase delay function is a function of the offset between two consecutive intersections. This phase delay function was then used for offsets optimization along an arterial. I tested the offsets optimization method against a base case using microscopic simulations. It was found that the proposed offset optimization method can significantly reduce vehicle delays. / Doctor of Philosophy / The goal of this work is to reduce traffic congestion by providing optimized signal timing plans to controllers. Knowing that the controllers in the U.S. follow National Electrical Manufacturing Association (NEMA) Standards, I proposed a virtual phase-link concept and modeled the road traffic network under NEMA controllers’ control as a set of virtual phase-links. Each virtual phase-link corresponds to a NEMA phase at an intersection. I then proposed a NEMA-based virtual phase-link street traffic model. The control variables are the green time allocated to each phase. I compared the proposed NEMA-based virtual phase-link control method with a state-of-the-practice signal timing software using simulation experiments. It was found that the proposed control methods significantly outperformed the signal timing software. I implemented a state-of-the-art adaptive control strategy, Max Pressure control. I compared the proposed NEMA-based virtual phase-link control method with the Max Pressure control strategy. I found that the virtual phase-link control method performed close, but not as good as, the Max Pressure control strategy. The disadvantage of the virtual phase-link method stemmed from the waste of green time during a fixed control cycle length and the delay which comes from the slowing down of platoon during a road link to allow vehicles to switch lanes. The Max Pressure control needs non-conventional controllers which can potentially switch to any phase at any time. Compared to the Max Pressure control strategy, the virtual phase-link method can be implemented by any traffic controller that follows the NEMA standards. The real-time requirement of the virtual phase-link method is not as strict as the Max Pressure control strategy. I then augmented the virtual phase-link method with optimal offsets control. The offsets are the time differences of the coordinated phases comparing to a reference point in a control cycle. I derived a phase delay function and used that function to optimize the offsets by minimizing the associated delays. The simulation experiments showed that the proposed offsets optimization method could reduce the delay along the coordinated path significantly.

Traditional generators have been decommissioned or replaced by renewable energy generation due to utility long-standing goals. However, instead of flattening the entire plant, the rotating mass of generator can be utilized as a storage unit (inertia resource) to mitigate the frequency swings during transient caused by the renewables. The goal of this work is to design a control strategy utilizing the decommissioned generator interfaced with power grid via a back-to-back converter to provide inertia support. This is referred to as decoupled synchronous machine system (DSMS). On top of that, the grid-side converter is capable of providing reactive power as an auxiliary voltage controller. However, in a practical setting, for power utilities, the detailed state equations of such device as well as the complicated nonlinear power system are usually unobtainable making the controller design a challenging problem. Therefore, a model free, purely data-driven strategy for the nonlinear controller design using Koopman operator-based framework is proposed. Besides, the time delay embedding technique is adopted together with Koopman operator theory for the nonlinear system identification. Koopman operator provides a linear representation of the system and thereby the classical linear control algorithms can be applied. In this work, model predictive control is adopted to cope with the constraints of the control signals. The effectiveness and robustness of the proposed system are demonstrated in Kundur two-area system and IEEE 39-bus system. / Doctor of Philosophy / Power system is facing an energy transformation from the traditional fuel to sustainable renewable such as solar, wind and so on. Unlike the traditional fuel energized generators, the renewable has very little inertia to maintain frequency stability. Therefore, this work proposes a new system referred to as decoupled synchronous machine system (DSMS) to support the grid frequency. DSMS consists of the rotating mass of generator and a back-to-back converter which can be utilized as an inertia resource to mitigate the frequency oscillations. In addition, the grid-side converter can provide reactive power to improve voltage performance during faults. This work aims to design a control strategy utilizing DSMS to support grid frequency and voltage. However, an explicit mathematical model of such device is unobtainable in a practical setting making data-driven control the only option. A data-driven technique which is Koopman operator-based framework together with time delay embedding algorithm is proposed to obtain a linear representation of the system. The effectiveness and robustness of the proposed system are demonstrated in Kundur two-area system and IEEE 39-bus system.

The focus of Location-Aware Adaptive Vehicle Dynamics System (LAAVDS) research is to develop a system to avoid situations in which the vehicle exceeds its handling capabilities. The proposed method is predictive, estimating the ability of the vehicle to successfully navigate upcoming terrain, and it is assumed that the future vehicle states and local driving environment is known. An Intervention Strategy must be developed such that the vehicle is navigated successfully along a road via modest changes to the driver's commands and do so in a manner that is in harmony with the driver's intentions and not in a distracting or irritating manner. Clearly this research relies on the numerous new technologies being developed to capture and convey information about the local driving environment (e.g., bank angle, elevation changes, curvature, and friction coefficient) to the vehicle and driver. / Ph. D.

Spacecraft relative motion modeling and control promises to enable or augment a wide range of missions for scientific research, military applications, and space situational awareness. This dissertation focuses on the development of novel, optimization-based, control design for some representative relative-motion-enabled missions. Spacecraft relative motion refers to two (or more) satellites in nearly identical orbits. We examine control design for relative configurations on the scale of meters (for the purposes of proximity operations) as well as on the scale of tens of kilometers (representative of science gathering missions). Realistic control design for satellites is limited by accurate modeling of the relative orbital perturbations as well as the highly constrained nature of most space systems. We present solutions to several types of optimal orbital maneuvers using a variety of different, realistic assumptions based on the maneuver objectives. Initially, we assume a perfectly circular orbit with a perfectly spherical Earth and analytically solve the under-actuated, minimum-energy, optimal transfer using techniques from optimal control and linear operator theory. The resulting open-loop control law is guaranteed to be a global optimum. Then, recognizing that very few, if any, orbits are truly circular, the optimal transfer problem is generalized to the elliptical linear and nonlinear systems which describe the relative motion. Solution of the minimum energy transfer for both the linear and nonlinear systems reveals that the resulting trajectories are nearly identical, implying that the nonlinearity has little effect on the relative motion. A continuous-time, nonlinear, sliding mode controller which tracks the linear trajectory in the presence of a higher fidelity orbit model shows that the closed-loop system is both asymptotically stable and robust to disturbances and un-modeled dynamics. Next, a novel method of computing discrete-time, multi-revolution, finite-thrust, fuel-optimal, relative orbit transfers near an elliptical, perturbed orbit is presented. The optimal control problem is based on the classical, continuous-time, fuel-optimization problem from calculus of variations, and we present the discrete-time analogue of this problem using a transcription-based method. The resulting linear program guarantees a global optimum in terms of fuel consumption, and we validate the results using classical impulsive orbit transfer theory. The new method is shown to converge to classical impulsive orbit transfer theory in the limit that the duration of the zero-order hold discretization approaches zero and the time horizon extends to infinity. Then the fuel/time optimal control problem is solved using a hybrid approach which uses a linear program to solve the fuel optimization, and a genetic algorithm to find the minimizing time-of-flight. The method developed in this work allows mission planners to determine the feasibility for realistic spacecraft and motion models. Proximity operations for robotic inspection have the potential to aid manned and unmanned systems in space situational awareness and contingency planning in the event of emergency. A potential limiting factor is the large number of constraints imposed on the inspector vehicle due to collision avoidance constraints and limited power and computational resources. We examine this problem and present a solution to the coupled orbit and attitude control problem using model predictive control. This control technique allows state and control constraints to be encoded as a mathematical program which is solved on-line. We present a new thruster constraint which models the minimum-impulse bit as a semi-continuous variable, resulting in a mixed-integer program. The new model, while computationally more expensive, is shown to be more fuel-efficient than a sub-optimal approximation. The result is a fuel efficient, trajectory tracking, model predictive controller with a linear-quadratic attitude regulator which tracks along a pre-computed ``safe'' trajectory in the presence of un-modeled dynamics on a higher fidelity orbital and attitude model. / Ph. D.