Shipboard Power System (SPS) is known as an independent controlled small electric network powered by the distributed onboard generation system. Since many electric components are tightly coupled in a small space and the system is not supported with a relatively stronger grid, SPS is more susceptible to unexpected disturbances and physical damages compared to conventional terrestrial power systems. Among different distribution configurations, power-electronic based DC distribution is considered the trending technology for the next-generation U.S. Navy fleet design to replace the conventional AC-based distribution. This research presents appropriate control management frameworks to improve the Medium-Voltage DC (MVDC) shipboard power system performance. Model Predictive Control (MPC) is an advanced model-based approach which uses the system model to predict the future output states and generates an optimal control sequence over the prediction horizon. In this research, at first, a centralized MPC is developed for a nonlinear MVDC SPS when a high-power pulsed load exists in the system. The closed-loop stability analysis is considered in the MPC optimization problem. A comparison is presented for different cases of load prediction for MPC, namely, no prediction, perfect prediction, and Autoregressive Integrated Moving Average (ARIMA) prediction. Another centralized MPC controller is also designed to address the reconfiguration problem of the MVDC system in abnormal conditions. The reconfiguration goal is to maximize the power delivered to the loads with respect to power balance, generation limits and load priorities. Moreover, a distributed control structure is proposed for a nonlinear MVDC SPS to develop a scalable power management architecture. In this framework, each subsystem is controlled by a local MPC using its state variables, parameters and interaction variables from other subsystems communicated through a coordinator. The Goal Coordination principle is used to manage interactions between subsystems. The developed distributed control structure brings out several significant advantages including less computational overhead, higher flexibility and a good error tolerance behavior as well as a good overall system performance. To demonstrate the efficiency of the proposed approach, a performance analysis is accomplished by comparing centralized and distributed control of global and partitioned MVDC models for two cases of continuous and discretized control inputs.

Model predictive control (MPC) for switched reluctance machine (SRM) drives is studied in this thesis. The objective is to highlight the benefits of implementing MPC to overcome the main drawbacks of SRMs and position them as an attractive alternative among electrical drives. A comprehensive literature review of MPC for SRM is presented, detailing its current trends as an application still at an early stage. The different features of MPC are highlighted and paired with the most challenging and promising control objectives of SRMs. A vision of future research trends and applications of MPC-driven SRMs is proposed, thus drawing a road-map of future projects, barriers to overcome and potential developments. Several important applications can take advantage of the improved features that SRM can get with MPC, especially from the possibility of defining a unified control technique with the flexibility to adapt to different system requirements. The most important cluster for SRM drives is the high- and ultrahigh-speed operative regions where conventional machines cannot work efficiently. SRMs with MPC can complement then the existing demand for electrical drives with high performance under challenging conditions. Three techniques based on the finite control set model predictive control (FCS-MPC) approach are developed out of the proposed road-map. The first one defines a virtual-flux current tracking technique that improves the existing ones in operating at different speeds and more than one quadrant operation. The method is validated for low- and high- power SRMs in simulations and diverse types of current waveform, making it easy to adapt to existing current shaping techniques. It is also validated experimentally for different operating conditions and robustness against parameter variation. The second technique proposed a predictive torque control that bases its model on static-maps, thus avoiding complex analytical expressions. It improves its estimation through a Kalman filter. The third technique uses a virtual-flux predictive torque control, similar to the first technique for current tracking. The techniques are validated at a wide speed range, thus evidencing superiority in performance without modification on the control structure. / Thesis / Doctor of Philosophy (PhD)

Electric power systems are undergoing a paradigm shift where an increasing number of variable renewable energy resources such as wind and solar power are being introduced to all levels of existing power grids. At the same time consumers are gaining a more active role where self energy production and home automation solutions are no longer uncommon. This challenges traditional power systems which were designed to serve as a centralized top-down solution for providing electricity to consumers. Demand response has risen as a promising solution to cope with some of the challenges that this shift is creating. In this thesis, control and scheduling studies using demand response, and consumer load models adapted to environments similar to Sweden are proposed and evaluated. The studies use model predictive control approaches for the purpose of providing ancillary and financial services to electricity market actors using thermal flexibility from detached houses. The approaches are evaluated on use-cases using data from Sweden for the purpose of reducing power imbalances of a balance responsible player and congestion management for a system operator. Simulations show promising results for reducing power imbalances by up to 30% and managing daily congestion of 5-19 MW using demand response. Moreover, a consumer load model of an office building is proposed using a gray-box modeling approach combining physical understanding of buildings with empirical data. Furthermore, the proposed consumer load model along with a similar model for detached houses are packaged and made freely available as MATLAB applications for other researchers and stakeholders working with demand response. The applications allow the user to generate synthetic electricity load profiles for heterogeneous populations of detached houses and office buildings down to 1-min resolution. The aim of this thesis has been to summarize and discuss the main highlights of the included articles. The interested reader is encouraged to investigate further details in the second part of the thesis as they provide a more comprehensive account of the studies and models proposed. / <p>QC 20171011</p>

Engine control becomes increasingly important in newer cars. It is therefore interesting to investigate if a relatively new control method as Model Predictive Control (MPC) can be useful in engine control in the future. One of the advantages of MPC is that it can handle contraints explicitly. In this thesis basics on turbocharged engines and the underlying theory of MPC is presented. Based on a nonlinear mean value engine model, linearized at multiple operating points, we then implement both a linear and a nonlinearMPC strategy and highlight implementation issues. The implemented MPC controllers calculate optimal wastegate position in order to track a requested torque curve and still make sure that the constraints on turbocharger speed and minimum and maximum opening of the wastegate are fulfilled.

Multilevel inverters (MLI) have been widely applied in medium and high voltage applications for their advantages in high quality of output waveforms. Among various multilevel topologies, cascaded H-bridge (CHB) inverters have attracted more attentions for its modular structure, which simplifies the design and implementation. In addition, the modularity of CHB also expands diverse power ratings without many changes in the hardware setup. In a CHB inverter, the AC output voltage can be produced at different voltage levels depending on the number power cells that are cascaded at the output. To produce the AC output voltage, different modulation schemes and control algorithms have been studied and applied to the CHB inverter. Model predictive control (MPC) has been widely employed among all control algorithms in multilevel topologies due to their advantages such as good dynamic performance, multiple control targets, inclusion of nonlinearity, and flexibility to add more performance objectives. However, one disadvantage of the MPC is that the switching frequency is variable compared with other modulation schemes. Therefore, a new MPC method called modulated model predictive control (M2PC) has been researched to obtain a fixed switching frequency, which improves the harmonic spectrum of load currents and simplifies the filter design. In the modulated model predictive control, the mathematical model is obtained by electrical model of the system. It means that the operation of the M2PC algorithm relies on the accuracy of the given parameters and model. If there is an error in parameters and model, the performance of the control will be affected negatively. To solve this problem, modulated model-free predictive control (M2FPC) algorithm has been introduced. With this method, the mathematical model is established with measured values instead of given values and model. Reliability is one of the most important issues in the design of power converters. However, the failure of power switches will lead to the distortion of load currents and voltage waveforms. Also, the distortion in load currents and voltage waveforms causes power imbalance between faulty and healthy phases. To reduce the negative effects of IGBT failure in power converters, the faulty power cells should be found and isolated. Therefore, fault detection and localization algorithm (FDL) should be introduced to detect the fault in power converters and localize the faulty power switches. FDL algorithm based on the given M2PC scheme is proposed in this thesis for the CHB inverter to make the system more reliable. The FDL algorithm utilizes the phase voltages and load currents to detect the open fault in the CHB inverter and localize the single and multiple open switches by measuring the expected and actual phase voltages. With the faulty information, the faulty power cell can be isolated, and the fault-tolerant control can be applied to make the system work normally even though there is an open fault. In this thesis, without losing the generality, a seven-level CHB inverter is considered where there are three power cells in each phase. The M2PC algorithm was introduced to obtain the fixed switching frequency with the design of possible voltage vector set and carrier phase-shifting modulation. Based on the proposed M2PC algorithm, the FDL algorithm is designed to detect and localize the open switches to improve the system reliability. The theoretical analysis and simulation results validate the feasibility of the proposed M2PC algorithms and open fault diagnosis scheme. All possible open-circuit scenarios in power cells are discussed and the M2PC-based FDL algorithm has been verified. Experimental results verify the feasibility of the proposed M2PC. The experimental result of M2PC algorithm is presented to verify its operation. Also, diverse open scenarios can be diagnosed in the experiments. / Thesis / Master of Applied Science (MASc)

京都大学 / 新制・課程博士 / 博士(情報学) / 甲第24743号 / 情博第831号 / 新制||情||139(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 東 俊一 / 学位規則第4条第1項該当 / Doctor of Informatics / Kyoto University / DFAM

In this work, a robust decentralized model predictive control regime for a team of cooperating robot systems is designed. Their assumed dynamics are in continuous time and non-linear. The problem involves agents whose dynamics are independent of one-another, and its solution couples their constraints as a means of capturing the cooperative behaviour required. Analytical proofs are given to show that, under the proposed control regime: (a) Subject to initial feasibility, the optimization solved at each step by each agent will always be feasible, irrespective of whether or not disturbances affect the agents. In the former case, recursive feasibility is established through successive restriction of each agent's constraints during the periodic solution to its respective optimization problem. (b) Each (sub)system can be stabilized to a desired configuration, either asymptotically when uncertainty is absent, or within a neighbourhood of it, when uncertainty is present, thus attenuating the affecting disturbance. In this context, disturbances are assumed to be additive and bounded. Simulations verify the efficacy of the proposed method over a range of different operating environments. / I detta arbete, en robust decentraliserad modell prediktiv kontroll regime förett lag av samverkande robotsystem är utformade. Deras antagnat dynamikär i kontinuerlig tid och olinjär. Problemet involverar agenter vars dynamik äroberoende av varandra, och sina lösning kopplar sina begränsningar som ettmedel för att fånga det kooperativa beteendet som krävs. Analytiska bevis gesför att visa att, enligt det föreslagna kontrollsystemet: (a) med förbehåll förförsta genomförbarhet, kommer optimeringen som löses vid varje steg av varjeagent alltid vara möjligt, oavsett huruvida störningar påverkar agenserna ellerinte. I det förre fallet är rekursiv genomförbarhet etablerad genom successivabegränsningar av varje agents inskränkning under den periodiska lösningentill dess respektive optimeringsproblem. (b) Varje (sub) system kan stabiliserastill en önskad konfiguration, antingen asymptotiskt när osäkerhet saknas,eller inom en närhet av det, när osäkerhet är närvarande, således dämparpåverkanstörning. I detta sammanhang antas störningar vara additiv och avgränsas.Simuleringar verifierar effekten av den föreslagna metoden över ettintervall av olika driftsmiljöer.

Soft robots are inherently safer than traditional robots due to their compliance and high power density ratio resulting in lower accidental impact forces. Thus they are a natural option for human-robot interaction. This thesis specifically looked at human-robot co-manipulation which is defined as a human and a robot working together to move an object too large or awkward to be safely maneuvered by a single agent. To better understand how humans communicate while co-manipulating an object, this work looked at haptic interaction of human-human dyadic co-manipulation trials and studied some of the trends found in that interaction. These trends point to ways robots can effectively work with human partners in the future. Before successful human-robot co-manipulation with large-scale soft robots can be achieved, low-level joint angle control is needed. Low-level model predictive control of soft robot joints requires a sufficiently accurate model of the system. This thesis introduces a recursive Newton-Euler method for deriving the dynamics that is sufficiently accurate and accounts for flexible joints in an intuitive way. This model has been shown to be accurate to a median absolute error of 3.15 degrees for a three-link three-joint six degree of freedom soft robot arm. Once a sufficiently accurate model was developed, a gain-based evolutionary model predictive control (MPC) technique was formulated based on a previous evolutionary MPC technique. This new method is referred to as model evolutionary gain-based predictive control or MEGa-PC. This control law is compared to nonlinear evolutionary model predictive control (NEMPC). The new technique allows intentionally decreasing the control frequency to 10 Hz while maintaining control of the system. This is proven to help MPC solve more difficult problems by having the ability to extend the control horizon. This new controller is also demonstrated to work well on a three-joint three-link soft robot arm. Although complete physical human-robot co-manipulation is outside the scope of this thesis, this thesis covers three main building blocks for physical human and soft robot co-manipulation: human-human haptic communication, soft robot modeling, and model evolutionary gain-based predictive control.

Ensuring constraint satisfaction in large-scale systems with hard constraints is vital in many safety critical systems. The challenge is to design controllers that are efficiently synthesized offline, easily implementable online, and provide formal correctness guarantees. We take a divide and conquer approach to design controllers for reachability and infinite-time/finite-time constraint satisfaction control problems given large-scale interconnected linear systems with polyhedral constraints on states, controls, and disturbances. Such systems are made of small subsystems with coupled dynamics. Our goals are to design controllers that are i) fully compositional and ii) decentralized, such that online implementation requires only local state information. We treat the couplings among the subsystems as additional disturbances and use assume-guarantee (AG) contracts to characterize these disturbance sets. For each subsystem, we design and implement a robust controller locally, subject to its own constraints and contracts. Our main contribution is a method to derive the contracts via a novel parameterization, and a corresponding potential function that characterizes the distance to the correct composition of controllers and contracts, where all contracts are held. We show that the potential function is convex in the contract parameters. This enables the subsystems to negotiate the contracts with the gradient information from the dual of their local synthesis optimization problems in a distributed way, facilitating compositional control synthesis that scales to large systems. We then incorporate Signal Temporal Logic (STL) specifications into our formulation. We develop a decentralized control method for a network of perturbed linear systems with dynamical couplings subject to STL specifications. We first transform the STL requirements into set containment problems, then we develop controllers to solve these problems. The set containment requirements and parameterized contracts are added to the subsystems’ constraints. We introduce a centralized optimization problem to derive the contracts, reachability tubes, and decentralized closed-loop control laws. We show that, when the STL formula is separable with respect to the subsystems, the centralized optimization problem can be solved in a distributed way, which scales to large systems. We present formal theoretical guarantees on robustness of STL satisfaction. We present numerical examples, including scalability studies on systems with tens of thousands of dimensions, and case studies on applying our method to a distributed Model Predictive Control (MPC) problem in a power system. / 2024-01-16T00:00:00Z

Inflatable robots with pneumatic actuation are naturally lightweight and compliant. Both of these characteristics make a robot of this type better suited for human environments where unintentional impacts will occur. The dynamics of an inflatable robot are complex and dynamic models that explicitly allow variable stiffness control have not been well developed. In this thesis, a dynamic model was developed for an antagonistic, pneumatically actuated joint with inflatable links.The antagonistic nature of the joint allows for the control of two states, primarily joint position and stiffness. First a model was developed to describe the position states. The model was used with model predictive control (MPC) and linear quadratic control (LQR) to control a single degree of freedom platform to within 3° of a desired angle. Control was extended to multiple degrees of freedom for a pick and place task where the pick was successful ten out of ten times and the place was successful eight out of ten times.Based on a torque model for the joint which accounts for pressure states that was developed in collaboration with other members of the Robotics and Dynamics Lab at Brigham Young University, the model was extended to account for the joint stiffness. The model accounting for position, stiffness, and pressure states was fit to data collected from the actual joint and stiffness estimation was validated by stiffness measurements.Using the stiffness model, sliding mode control (SMC) and MPC methods were used to control both stiffness and position simultaneously. Using SMC, the joint stiffness was controlled to within 3 Nm/rad of a desired trajectory at steady state and the position was controlled to within 2° of a desired position trajectory at steady state. Using MPC,the joint stiffness was controlled to within 1 Nm/rad of a desired trajectory at steady state and the position was controlled to within 2° of a desired position trajectory at steady state. Stiffness control was extended to multiple degrees of freedom using MPC where each joint was treated as independent and uncoupled. Controlling stiffness reduced the end effecter deflection by 50% from an applied load when high stiffness (50 Nm/rad) was used rather than low stiffness (35 Nm/rad).This thesis gives a state space dynamic model for an inflatable, pneumatically actuated joint and shows that the model can be used for accurate and repeatable position and stiffness control with stiffness having a significant effect.