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>

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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

In recent years, the use of rotorcraft uninhabited aerial vehicles (UAVs) for cargo delivery has become of particular interest to private companies and humanitarian organizations, namely due to their reduced operational costs, ability to reach remote locations and to take off and land vertically. The slung configuration, where the cargo is suspended below the vehicle by a cable, is slowly being favoured for its ability to transport different sized loads without the need for the vehicle to land. However, such configurations require complex control systems in order to stabilize the swing of the suspended load. The goal of this research is to design a control system which will be able to bring a slung payload transported by a rotorcraft UAV back to its stable equilibrium in the event of a disturbance. A simple model of the system is first derived from first principles for the purpose of simulating a control algorithm. A controller based in model-free, policy-gradient reinforcement learning is then derived and implemented on the simulator in order to tune the learning parameters and reach a first stable solution for load stabilization in a single plane. An experimental testbed is then constructed to test the performance of the controller in a practical setting. The testbed consists of a quadcopter carrying a weight suspended on a string and of a newly designed on-board load-angle sensing device, to allow the algorithm to operate using only on-board sensing and computation. While the load-angle sensing design was found to be sensitive to the aggressive manoeuvres of the vehicle and require reworking, the proposed control algorithm was found to successfully stabilize the slung payload and adapt in real-time to the dynamics of the physical testbed, accounting for model uncertainties. The algorithm also works within the framework of the widely-used, open-source autopilot program ArduCopter, making it straightforward to implement on existing rotorcraft platforms. In the future, improvements to the load angle sensor should be made to enable the algorithm to run fully on-board and allow the vehicle to operate outdoors. Further studies should also be conducted to limit the amount of vehicle drift observed during testing of the load stabilization.

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No description available.

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.