Cyber-physical systems (CPS), integrating advanced computation, communication, and control technologies with the physical process, are widely applied in industry applications such as smart production and manufacturing systems, robotic and automotive control systems, and smart grids. Due to possible exposure to unreliable networks and complex physical environments, CPSs may simultaneously face multiple cyber and physical issues including cyber threats (e.g., malicious cyber attacks) and resource constraints (e.g., limited networking resources and physical constraints). As one of the essential topics in designing efficient CPSs, the controller design for CPSs, aiming to achieve secure and resource-aware control objectives under such cyber and physical issues, is very significant yet challenging. Emphasizing optimality and system constraint handling, model predictive control (MPC) is one of the most widely used control paradigms, notably famous for its successful applications in chemical process industry. However, the conventional MPC methods are not specifically tailored to tackle cyber threats and resource constraints, thus the corresponding theory and tools to design the secure and resource-aware controller are lacking and need to be developed. This dissertation focuses on developing MPC-based methodologies to address the i) secure control problem and ii) resource-aware control problem for CPSs subject to cyber threats and resource constraints. In the resource-aware control problem of CPSs, the nonlinear system with additive disturbance is considered. By using an integral-type event-triggered mechanism and an improved robustness constraint, we propose an integral-type event-triggered MPC so that smaller sampling frequency and robustness to the additive disturbance can be obtained. The sufficient conditions for guaranteeing the recursive feasibility and the closed-loop stability are established. For the secure control problem of CPSs, two aspects are considered. Firstly, to achieve the secure control objective, we design a secure dual-mode MPC framework, including a modified initial feasible set and a new positively invariant set, for constrained linear systems subject to Denial-of-Service (DoS) attacks. The exponential stability of the closed-loop system is guaranteed under several conditions. Secondly, to deal with cyber threats and take advantage of the cloud-edge computing technology, we propose a model predictive control as a secure service (MPCaaSS) framework, consisting of a double-layer controller architecture and a secure data transmission protocol, for constrained linear systems in the presence of both cyber threats and external disturbances. The rigorous recursive feasibility and robust stability conditions are established. To simultaneously address the secure and resource-aware control problems, an event-triggered robust nonlinear MPC framework is proposed, where a new robustness constraint is introduced to deal with additive disturbances, and a packet transmission strategy is designed to tackle DoS attacks. Then, an event-triggered mechanism, which accommodates DoS attacks occurring in the communication network, is proposed to reduce the communication cost for resource-constrained CPSs. The recursive feasibility and the closed-loop stability in the sense of input-to-state practical stable (ISpS) are guaranteed under the established sufficient conditions. / Graduate

This thesis investigates autonomous landing of a micro air vehicle (MAV) on a nonstationary ground platform. Unmanned aerial vehicles (UAVs) and micro air vehicles (MAVs) are becoming every day more ubiquitous. Nonetheless, many applications still require specialized human pilots or supervisors. Current research is focusing on augmenting the scope of tasks that these vehicles are able to accomplish autonomously. Precise autonomous landing on moving platforms is essential for self-deployment and recovery of MAVs, but it remains a challenging task for both autonomous and piloted vehicles. Model Predictive Control (MPC) is a widely used and effective scheme to control constrained systems. One of its variants, output-feedback tube-based MPC, ensures robust stability for systems with bounded disturbances under system state reconstruction. This thesis proposes a MAV control strategy based on this variant of MPC to perform rapid and precise autonomous landing on moving targets whose nominal (uncommitted) trajectory and velocity are slowly varying. The proposed approach is demonstrated on an experimental setup.

In the study of multi-agent systems (MASs), cooperative control is one of the most fundamental issues. As it covers a broad spectrum of applications in many industrial areas, there is a desire to design cooperative control protocols for different system and network setups. Motivated by this fact, in this thesis we focus on elaborating consensus protocol design, via model predictive control (MPC), under two different scenarios: (1) general constrained linear MASs with bounded additive disturbance; (2) linear MASs with input constraints underlying distributed communication networks. In Chapter 2, a tube-based robust MPC consensus protocol for constrained linear MASs is proposed. For undisturbed linear MASs without constraints, the results on designing a centralized linear consensus protocol are first developed by a suboptimal linear quadratic approach. In order to evaluate the control performance of the suboptimal consensus protocol, we use an infinite horizon linear quadratic objective function to penalize the disagreement among agents and the size of control inputs. Due to the non-convexity of the performance function, an optimal controller gain is difficult or even impossible to find, thus a suboptimal consensus protocol is derived. In the presence of disturbance, the original MASs may not maintain certain properties such as stability and cooperative performance. To this end, a tube-based robust MPC framework is introduced. When disturbance is involved, the original constraints in nominal prediction should be tightened so as to achieve robust constraint satisfaction, as the predicted states and the actual states are not necessarily the same. Moreover, the corresponding robust constraint sets can be determined offline, requiring no extra iterative online computation in implementation. In Chapter 3, a novel distributed MPC-based consensus protocol is proposed for general linear MASs with input constraints. For the linear MAS without constraints, a pre-stabilizing distributed linear consensus protocol is developed by an inverse optimal approach, such that the corresponding closed-loop system is asymptotically stable with respect to a consensus set. Implementing this pre-stabilizing controller in a distributed digital setting is however not possible, as it requires every local decision maker to continuously access the state of their neighbors simultaneously when updating the control input. To relax these requirements, the assumed neighboring state, instead of the actual state of neighbors, is used. In our distributed MPC scheme, each local controller minimizes a group of control variables to generate control input. Moreover, an additional state constraint is proposed to bound deviation between the actual and the assumed state. In this way, consistency is enforced between intended behaviors of an agent and what its neighbors believe it will behave. We later show that the closed-loop system converges to a neighboring set of the consensus set thanks to the bounded state deviation in prediction. In Chapter 4, conclusions are made and some research topics for future exploring are presented. / Graduate / 2021-03-31

This thesis focuses on the development and extension of nonlinear evolutionary model predictive control (NEMPC), a control algorithm previously developed by Phil Hyatt of the BYU RaD Lab. While this controller and its variants are applicable to any high degree-of-freedom (DoF) robotic system, particular emphasis is given in this thesis to control of a soft robot continuum joint. First, speed improvements are presented for NEMPC. Second, a Python package is presented as a companion to NEMPC, as a method of establishing a common interface for dynamic simulators and approximating each system by a deep neural network (DNN). Third, a method of training a DNN approximation of a hardware system that is generalize-able to more complex hardware systems is presented. This method is shown to reduce median tracking error on a soft robot hardware platform by 88%. Finally, particle swarm model predictive control (PSOMPC), a variant of NEMPC, is presented and modified to model and account for uncertainty in a dynamic system. Control performance of NEMPC and PSOMPC are presented for a set of control trials on simulated systems with uncertainty in parameters, states, and inputs, as well as on a soft robot hardware platform. PSOMPC is shown to have an increased robustness to system uncertainty, reducing expected collisions by 71% for a three-link robot arm with parameter uncertainty, input disturbances, and state measurement error.

Traditional robots, while capable of being efficient and effective for the task they were designed, are dangerous when operating in unmodeled environments or around humans. The field of soft robotics attempts to increase the safety of robots thus enabling them to operate in environments where traditional robots should not operate. Because of this, soft robots were developed with different goals in mind than traditional robots and as such the traditional metrics used to evaluate standard robots are not effective for evaluating soft robots. New metrics need to be developed for soft robots so that effective comparison and evaluations can be made. This dissertation attempts to lay the groundwork for that process through a survey on soft robot metrics. Additionally we propose six soft robot actuator metrics that can be used to evaluate and compare characteristics and performance of soft robot actuators. Data from eight different soft robot rotational actuators (five distinct designs) were used to evaluate these soft robot actuator metrics and show their utility. New models, control methods and estimation methods also need to be developed for soft robots. Many of the traditional methods and assumptions for modeling and controlling robotic systems are not able to provide the fidelity that is needed for soft robots to effectively complete useful tasks. This dissertation presents specific developments in each of these areas of soft robot metrics, modeling, control and estimation. We show several incremental improvements to soft robot dynamic models as well as how they were used in control methods for more precise control. We also demonstrate a method for linearizing high degree of freedom models so it can be simplified for use in faster control methods for better performance. Lastly, we present an improved continuum joint configuration estimation method that uses a linear combination of length measurements. All these developments combine to help build the "fundamental engineering framework" that is needed for soft robotics as well as helping to move robots out of their confined spaces and bring them into new unmodeled/unstructured environments.

Optimal control theory has a long history and broad applications. Motivated by the goal of obtaining insights through unification and taking advantage of the abundant capability to generate data, this thesis introduces some suboptimal schemes via abstract dynamic programming models. As our first contribution, we consider deterministic infinite horizon optimal control problems with nonnegative stage costs. We draw inspiration from the learning model predictive control scheme designed for continuous dynamics and iterative tasks, and propose a rollout algorithm that relies on sampled data generated by some base policy. The proposed algorithm is based on value and policy iteration ideas. It applies to deterministic problems with arbitrary state and control spaces, and arbitrary dynamics. It admits extensions to problems with trajectory constraints, and a multiagent structure. In addition, abstract dynamic programming models are used to analyze $\lambda$-policy iteration with randomization algorithms. In particular, we consider contractive models with infinite policies. We show that well-posedness of the $\lambda$-operator plays a central role in the algorithm. The operator is known to be well-posed for problems with finite states, but our analysis shows that it is also well-defined for the contractive models with infinite states. Similarly, the algorithm we analyze is known to converge for problems with finite policies, but we identify the conditions required to guarantee convergence with probability one when the policy space is infinite regardless of the number of states. Guided by the analysis, we exemplify a data-driven approximated implementation of the algorithm for estimation of optimal costs of constrained linear and nonlinear control problems. Numerical results indicate the potentials of this method in practice. / Teorin om optimal reglering har en lång historia och breda tillämpningsområden.I denna avhandling, som motiveras av att få insikter genom att förena och dra nyttaav den goda möjligheten att generera data, introduceras några suboptimala systemvia abstrakta modeller för dynamisk programmering.I vårt första bidrag betraktar vi ett deterministiskt optimalt regleringsproblemmed oändlig horisont och icke-negativa stegkostnader. Vi hämtar inspiration frånmodellprediktiv reglering med inlärning, som är utformad för system med kontinuerligdynamik och iterativa uppgifter, och föreslår en utrullningsalgoritm som bygger påsamplade data som genereras av en viss baspolicy. Den föreslagna algoritmen byggerpå idéer om värde- och policyiteration. Den är tillämpningsbar för deterministiskaproblem med godtyckliga tillstånds- och kontrollrum samt för system med godtyckligdynamik. Slutligen kan den utvidgas till problem med trajektoriebegränsningar ochen struktur med flera agenter.Dessutom används abstrakta modeller för dynamisk programmering för attanalysera lambdapolicyiteration med randomiseringsalgoritmer. Vi betraktar merspecifikt kontraktiva modeller med oändliga strategier. Vi visar att lambdaoperatorns välbestämdhet spelar en central roll i algoritmen. Det är känt att operatorn ärväldefinierad för problem med ändliga tillstånd, men vår analys visar att den ocksåär väldefinierad för de studerade kontraktiva modellerna med oändliga tillstånd.På samma sätt är det känt att den algoritm vi analyserar konvergerar för problemmed ändliga strategier, men vi identifierar de villkor som krävs för att garanterakonvergens med sannolikhet ett när policyrummet är oändligt, oberoende av antalettillstånd. Med hjälp av analysen exemplifierar vi en datadriven approximativ implementering av algoritmen för uppskattning av optimala kostnader för begränsadelinjära och icke-linjära regleringsproblem. Numeriska resultat visar på potentialen iatt använda denna metod i praktiken. / <p>QC 20211129</p>

No description available.

Collision avoidance is an essential capability for autonomous and assisted-driving ground vehicles. In this work, we developed a novel model predictive control based intelligent collision avoidance (CA) algorithm for a multi-trailer industrial ground vehicle implemented on a General Purpose Graphical Processing Unit (GPGPU). The CA problem is formulated as a multi-objective optimal control problem and solved using a limited look-ahead control scheme in real-time. Through hardware-in-the-loop-simulations and experimental results obtained in this work, we have demonstrated that the proposed algorithm, using NVIDA’s CUDA framework and the NVIDIA Jetson TX2 development platform, is capable of dynamically assisting drivers and maintaining the vehicle a safe distance from the detected obstacles on-thely. We have demonstrated that a GPGPU, paired with an appropriate algorithm, can be the key enabler in relieving the computational burden that is commonly associated with model-based control problems and thus make them suitable for real-time applications.

This thesis investigates one option for improving the control of a reheating furnace used in heating steel slabs before hot rolling; an essential part of the steel manufacturing process. The furnace consumes a significant amount of energy, leading to high cost and high carbon dioxide emissions. The proposed solution is the implementation of a model predictive control (MPC) system to improve control and reduce fuel usage. The MPC system will be based on the use of system identification techniques to find a prediction model of the furnace, specifically using ARMAX models. An additional simulation model will be used to simulate the system, and to compare the performance of MPC and PID. The prediction model is found to have a normalized root mean squared error of over 91% for the first five minutes, suggesting that it has potential to be used for MPC. The simulation model has significant inaccuracies, due to the presence of unmeasured disturbances. The simulation results, although limited due to the inaccuracies of the simulation model, suggest that MPC is a viable option for improved control of the furnace. The use of MPC can potentially improve the repeatability of the heating process, resulting in improved steel quality and reduced defects. This thesis suggests that further investigation into the use of MPC for controlling reheating furnaces in the steel industry is worth pursuing, and could potentially bring significant benefits to both producers and the environment.

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