Reinforcement Learning Approaches to Power System Scheduling

One major component of power system operation is generation scheduling. The objective of the work is to develop efficient control strategies to the power scheduling problems through Reinforcement Learning approaches. The three important active power scheduling problems are Unit Commitment, Economic Dispatch and Automatic Generation Control. Numerical solution methods proposed for solution of power scheduling are insufficient in handling large and complex systems. Soft Computing methods like Simulated Annealing, Evolutionary Programming etc., are efficient in handling complex cost functions, but find limitation in handling stochastic data existing in a practical system. Also the learning steps are to be repeated for each load demand which increases the computation time.Reinforcement Learning (RL) is a method of learning through interactions with environment. The main advantage of this approach is it does not require a precise mathematical formulation. It can learn either by interacting with the environment or interacting with a simulation model. Several optimization and control problems have been solved through Reinforcement Learning approach. The application of Reinforcement Learning in the field of Power system has been a few. The objective is to introduce and extend Reinforcement Learning approaches for the active power scheduling problems in an implementable manner. The main objectives can be enumerated as:(i) Evolve Reinforcement Learning based solutions to the Unit Commitment Problem.(ii) Find suitable solution strategies through Reinforcement Learning approach for Economic Dispatch. (iii) Extend the Reinforcement Learning solution to Automatic Generation Control with a different perspective. (iv) Check the suitability of the scheduling solutions to one of the existing power systems.First part of the thesis is concerned with the Reinforcement Learning approach to Unit Commitment problem. Unit Commitment Problem is formulated as a multi stage decision process. Q learning solution is developed to obtain the optimwn commitment schedule. Method of state aggregation is used to formulate an efficient solution considering the minimwn up time I down time constraints. The performance of the algorithms are evaluated for different systems and compared with other stochastic methods like Genetic Algorithm.Second stage of the work is concerned with solving Economic Dispatch problem. A simple and straight forward decision making strategy is first proposed in the Learning Automata algorithm. Then to solve the scheduling task of systems with large number of generating units, the problem is formulated as a multi stage decision making task. The solution obtained is extended in order to incorporate the transmission losses in the system. To make the Reinforcement Learning solution more efficient and to handle continuous state space, a fimction approximation strategy is proposed. The performance of the developed algorithms are tested for several standard test cases. Proposed method is compared with other recent methods like Partition Approach Algorithm, Simulated Annealing etc.As the final step of implementing the active power control loops in power system, Automatic Generation Control is also taken into consideration.Reinforcement Learning has already been applied to solve Automatic Generation Control loop. The RL solution is extended to take up the approach of common frequency for all the interconnected areas, more similar to practical systems. Performance of the RL controller is also compared with that of the conventional integral controller.In order to prove the suitability of the proposed methods to practical systems, second plant ofNeyveli Thennal Power Station (NTPS IT) is taken for case study. The perfonnance of the Reinforcement Learning solution is found to be better than the other existing methods, which provide the promising step towards RL based control schemes for practical power industry.Reinforcement Learning is applied to solve the scheduling problems in the power industry and found to give satisfactory perfonnance. Proposed solution provides a scope for getting more profit as the economic schedule is obtained instantaneously. Since Reinforcement Learning method can take the stochastic cost data obtained time to time from a plant, it gives an implementable method. As a further step, with suitable methods to interface with on line data, economic scheduling can be achieved instantaneously in a generation control center. Also power scheduling of systems with different sources such as hydro, thermal etc. can be looked into and Reinforcement Learning solutions can be achieved.

Studies Using Computer Simulation Models to Solve Problems Related to Logistics Terminals

This thesis deals with the use of simulation as a problem-solving tool to solve a few logistic system related problems. More specifically it relates to studies on transport terminals. Transport terminals are key elements in the supply chains of industrial systems. One of the problems related to use of simulation is that of the multiplicity of models needed to study different problems. There is a need for development of methodologies related to conceptual modelling which will help reduce the number of models needed. Three different logistic terminal systems Viz. a railway yard, container terminal of apart and airport terminal were selected as cases for this study. The standard methodology for simulation development consisting of system study and data collection, conceptual model design, detailed model design and development, model verification and validation, experimentation, and analysis of results, reporting of finding were carried out. We found that models could be classified into tightly pre-scheduled, moderately pre-scheduled and unscheduled systems. Three types simulation models( called TYPE 1, TYPE 2 and TYPE 3) of various terminal operations were developed in the simulation package Extend. All models were of the type discrete-event simulation. Simulation models were successfully used to help solve strategic, tactical and operational problems related to three important logistic terminals as set in our objectives. From the point of contribution to conceptual modelling we have demonstrated that clubbing problems into operational, tactical and strategic and matching them with tightly pre-scheduled, moderately pre-scheduled and unscheduled systems is a good workable approach which reduces the number of models needed to study different terminal related problems.

Development and Evaluation of Blind Identification Techniques for Nonlinear Systems

Identification and Control of Non‐linear dynamical systems are challenging problems to the control engineers.The topic is equally relevant in communication,weather prediction ,bio medical systems and even in social systems,where nonlinearity is an integral part of the system behavior.Most of the real world systems are nonlinear in nature and wide applications are there for nonlinear system identification/modeling.The basic approach in analyzing the nonlinear systems is to build a model from known behavior manifest in the form of system output.The problem of modeling boils down to computing a suitably parameterized model,representing the process.The parameters of the model are adjusted to optimize a performanace function,based on error between the given process output and identified process/model output.While the linear system identification is well established with many classical approaches,most of those methods cannot be directly applied for nonlinear system identification.The problem becomes more complex if the system is completely unknown but only the output time series is available.Blind recognition problem is the direct consequence of such a situation.The thesis concentrates on such problems.Capability of Artificial Neural Networks to approximate many nonlinear input-output maps makes it predominantly suitable for building a function for the identification of nonlinear systems,where only the time series is available.The literature is rich with a variety of algorithms to train the Neural Network model.A comprehensive study of the computation of the model parameters,using the different algorithms and the comparison among them to choose the best technique is still a demanding requirement from practical system designers,which is not available in a concise form in the literature.The thesis is thus an attempt to develop and evaluate some of the well known algorithms and propose some new techniques,in the context of Blind recognition of nonlinear systems.It also attempts to establish the relative merits and demerits of the different approaches.comprehensiveness is achieved in utilizing the benefits of well known evaluation techniques from statistics. The study concludes by providing the results of implementation of the currently available and modified versions and newly introduced techniques for nonlinear blind system modeling followed by a comparison of their performance.It is expected that,such comprehensive study and the comparison process can be of great relevance in many fields including chemical,electrical,biological,financial and weather data analysis.Further the results reported would be of immense help for practical system designers and analysts in selecting the most appropriate method based on the goodness of the model for the particular context.

Techniques for Enhancing Wind Energy Generation - A CFD Based Multibody Dynamics Approach in Horizontal Axis Wind Turbines

Wind energy has emerged as a major sustainable source of energy.The efficiency of wind power generation by wind mills has improved a lot during the last three decades.There is still further scope for maximising the conversion of wind energy into mechanical energy.In this context,the wind turbine rotor dynamics has great significance.The present work aims at a comprehensive study of the Horizontal Axis Wind Turbine (HAWT) aerodynamics by numerically solving the fluid dynamic equations with the help of a finite-volume Navier-Stokes CFD solver.As a more general goal,the study aims at providing the capabilities of modern numerical techniques for the complex fluid dynamic problems of HAWT.The main purpose is hence to maximize the physics of power extraction by wind turbines.This research demonstrates the potential of an incompressible Navier-Stokes CFD method for the aerodynamic power performance analysis of horizontal axis wind turbine.The National Renewable Energy Laboratory USA-NREL (Technical Report NREL/Cp-500-28589) had carried out an experimental work aimed at the real time performance prediction of horizontal axis wind turbine.In addition to a comparison between the results reported by NREL made and CFD simulations,comparisons are made for the local flow angle at several stations ahead of the wind turbine blades.The comparison has shown that fairly good predictions can be made for pressure distribution and torque.Subsequently, the wind-field effects on the blade aerodynamics,as well as the blade/tower interaction,were investigated.The selected case corresponded to a 12.5 m/s up-wind HAWT at zero degree of yaw angle and a rotational speed of 25 rpm.The results obtained suggest that the present can cope well with the flows encountered around wind turbines.The areodynamic performance of the turbine and the flow details near and off the turbine blades and tower can be analysed using theses results.The aerodynamic performance of airfoils differs from one another.The performance mainly depends on co-efficient of performnace,co-efficient of lift,co-efficient of drag, velocity of fluid and angle of attack.This study shows that the velocity is not constant for all angles of attack of different airfoils.The performance parameters are calculated analytically and are compared with the standardized performance tests.For different angles of ,the velocity stall is determined for the better performance of a system with respect to velocity.The research addresses the effect of surface roughness factor on the blade surface at various sections.The numerical results were found to be in agreement with the experimental data.A relative advantage of the theoretical aerofoil design method is that it allows many different concepts to be explored economically.Such efforts are generally impractical in wind tunnels because of time and money constraints.Thus, the need for a theoretical aerofoil design method is threefold:first for the design of aerofoil that fall outside the range of applicability of existing calalogs:second,for the design of aerofoil that more exactly match the requirements of the intended application:and third,for the economic exploration of many aerofoil concepts.From the results obtained for the different aerofoils,the velocity is not constant for all angles of attack.The results obtained for the aerofoil mainly depend on angle of attack and velocity.The vortex generator technique was meticulously studies with the formulation of the specification for the right angle shaped vortex generators-VG.The results were validated in accordance with the primary analysis phase.The results were found to be in good agreement with the power curve.The introduction of correct size VGs at appropriate locations over the blades of the selected HAWT was found to increase the power generation by about 4%

A Study of the Physical and Engineering Behaviour of Cochin Marine Clays

With the increase in population, housing and construction of various facilities have been a problem with urbanization. Having exhausted all the trouble free hand, man is nowon the lookout for techniques to improve areas which were originally considered uninhabitable. Thus this study is based on the nature and engineering behavior of soft clays covering long stretches of coastal line and methods to improve their geotechnical properties .The main aim of the present investigation is to study in detail the physical and engineering behavior of the marine clays of Cochin. While it is well known that the marine clays have been posing numerous problems to foundation engineers all along, the relevant literature reveals that no systematic and comprehensive study has been attempted to date. The: knowledge gained through the study is suitably used to improve these properties with appropriate additives.

Studies on magnetic monopole solutions of non-abelian gauge theories and related problems

In 1931 Dirac studied the motion of an electron in the field of a magnetic monopole and found that the quantization of electric charge can be explained by postulating the mere existence of a magnetic monopole. Since 1974 there has been a resurgence of interest in magnetic monopole due to the work of ‘t’ Hooft and Polyakov who independently observed that monopoles can exist as finite energy topologically stable solutions to certain spontaneously broken gauge theories. The thesis, “Studies on Magnetic Monopole Solutions of Non-abelian Gauge Theories and Related Problems”, reports a systematic investigation of classical solutions of non-abelian gauge theories with special emphasis on magnetic monopoles and dyons which possess both electric and magnetic charges. The formation of bound states of a dyon with fermions and bosons is also studied in detail. The thesis opens with an account of a new derivation of a relationship between the magnetic charge of a dyon and the topology of the gauge fields associated with it. Although this formula has been reported earlier in the literature, the present method has two distinct advantages. In the first place, it does not depend either on the mechanism of symmetry breaking or on the nature of the residual symmetry group. Secondly, the results can be generalized to finite temperature monopoles.

Some problems of discrete function theory

An attempt is made by the researcher to establish a theory of discrete functions in the complex plane. Classical analysis q-basic theory, monodiffric theory, preholomorphic theory and q-analytic theory have been utilised to develop concepts like differentiation, integration and special functions.

Low-Cost Micromechanically Tunable Optical Devices: Strained Resonator Engineering, Technological Implementation and Characterization

The rapid growth of the optical communication branches and the enormous demand for more bandwidth require novel networks such as dense wavelength division multiplexing (DWDM). These networks enable higher bitrate transmission using the existing optical fibers. Micromechanically tunable optical microcavity devices like VCSELs, Fabry-Pérot filters and photodetectors are core components of these novel DWDM systems. Several air-gap based tunable devices were successfully implemented in the last years. Even though these concepts are very promising, two main disadvantages are still remaining. On the one hand, the high fabrication and integration cost and on the other hand the undesired adverse buckling of the suspended membranes. This thesis addresses these two problems and consists of two main parts: • PECVD dielectric material investigation and stress control resulting in membranes shape engineering. • Implementation and characterization of novel tunable optical devices with tailored shapes of the suspended membranes. For this purposes, low-cost PECVD technology is investigated and developed in detail. The macro- and microstress of silicon nitride and silicon dioxide are controlled over a wide range. Furthermore, the effect of stress on the optical and mechanical properties of the suspended membranes and on the microcavities is evaluated. Various membrane shapes (concave, convex and planar) with several radii of curvature are fabricated. Using this resonator shape engineering, microcavity devices such as non tunable and tunable Fabry-Pérot filters, VCSELs and PIN photodetectors are succesfully implemented. The fabricated Fabry-Pérot filters cover a spectral range of over 200nm and show resonance linewidths down to 1.5nm. By varying the stress distribution across the vertical direction within a DBR, the shape and the radius of curvature of the top membrane are explicitely tailored. By adjusting the incoming light beam waist to the curvature, the fundamental resonant mode is supported and the higher order ones are suppressed. For instance, a tunable VCSEL with 26 nm tuning range, 400µW maximal output power, 47nm free spectral range and over 57dB side mode suppresion ratio (SMSR) is demonstrated. Other technologies, such as introducing light emitting organic materials in microcavities are also investigated.

Offline Emergence Engineering For Agent Societies

Many examples for emergent behaviors may be observed in self-organizing physical and biological systems which prove to be robust, stable, and adaptable. Such behaviors are often based on very simple mechanisms and rules, but artificially creating them is a challenging task which does not comply with traditional software engineering. In this article, we propose a hybrid approach by combining strategies from Genetic Programming and agent software engineering, and demonstrate that this approach effectively yields an emergent design for given problems.

Verfahren zur echtzeitfähigen Simulation dynamischer Emissionsverläufe ausgewählter Schadstoffe von Ottomotoren

Im Rahmen dieser Arbeit werden Modellbildungsverfahren zur echtzeitfähigen Simulation wichtiger Schadstoffkomponenten im Abgasstrom von Verbrennungsmotoren vorgestellt. Es wird ein ganzheitlicher Entwicklungsablauf dargestellt, dessen einzelne Schritte, beginnend bei der Ver-suchsplanung über die Erstellung einer geeigneten Modellstruktur bis hin zur Modellvalidierung, detailliert beschrieben werden. Diese Methoden werden zur Nachbildung der dynamischen Emissi-onsverläufe relevanter Schadstoffe des Ottomotors angewendet. Die abgeleiteten Emissionsmodelle dienen zusammen mit einer Gesamtmotorsimulation zur Optimierung von Betriebstrategien in Hybridfahrzeugen. Im ersten Abschnitt der Arbeit wird eine systematische Vorgehensweise zur Planung und Erstellung von komplexen, dynamischen und echtzeitfähigen Modellstrukturen aufgezeigt. Es beginnt mit einer physikalisch motivierten Strukturierung, die eine geeignete Unterteilung eines Prozessmodells in einzelne überschaubare Elemente vorsieht. Diese Teilmodelle werden dann, jeweils ausgehend von einem möglichst einfachen nominalen Modellkern, schrittweise erweitert und ermöglichen zum Abschluss eine robuste Nachbildung auch komplexen, dynamischen Verhaltens bei hinreichender Genauigkeit. Da einige Teilmodelle als neuronale Netze realisiert werden, wurde eigens ein Verfah-ren zur sogenannten diskreten evidenten Interpolation (DEI) entwickelt, das beim Training einge-setzt, und bei minimaler Messdatenanzahl ein plausibles, also evidentes Verhalten experimenteller Modelle sicherstellen kann. Zum Abgleich der einzelnen Teilmodelle wurden statistische Versuchs-pläne erstellt, die sowohl mit klassischen DoE-Methoden als auch mittels einer iterativen Versuchs-planung (iDoE ) generiert wurden. Im zweiten Teil der Arbeit werden, nach Ermittlung der wichtigsten Einflussparameter, die Model-strukturen zur Nachbildung dynamischer Emissionsverläufe ausgewählter Abgaskomponenten vor-gestellt, wie unverbrannte Kohlenwasserstoffe (HC), Stickstoffmonoxid (NO) sowie Kohlenmono-xid (CO). Die vorgestellten Simulationsmodelle bilden die Schadstoffkonzentrationen eines Ver-brennungsmotors im Kaltstart sowie in der anschließenden Warmlaufphase in Echtzeit nach. Im Vergleich zur obligatorischen Nachbildung des stationären Verhaltens wird hier auch das dynami-sche Verhalten des Verbrennungsmotors in transienten Betriebsphasen ausreichend korrekt darge-stellt. Eine konsequente Anwendung der im ersten Teil der Arbeit vorgestellten Methodik erlaubt, trotz einer Vielzahl von Prozesseinflussgrößen, auch hier eine hohe Simulationsqualität und Ro-bustheit. Die Modelle der Schadstoffemissionen, eingebettet in das dynamische Gesamtmodell eines Ver-brennungsmotors, werden zur Ableitung einer optimalen Betriebsstrategie im Hybridfahrzeug ein-gesetzt. Zur Lösung solcher Optimierungsaufgaben bieten sich modellbasierte Verfahren in beson-derer Weise an, wobei insbesondere unter Verwendung dynamischer als auch kaltstartfähiger Mo-delle und der damit verbundenen Realitätsnähe eine hohe Ausgabequalität erreicht werden kann.

Synthesis and characterisation of novel nitrogen and fluorine containing spirobifluorene derivatives for electronic applications and crystal engineering

Among organic materials, spirobifluorene derivatives represent a very attractive class of materials for electronic devices. These compounds have high melting points, glass transitions temperatures and morphological stability, which makes these materials suitable for organic electronic applications. In addition, some of spirobifluorenes can form porous supramolecular associations with significant volumes available for the inclusion of guests. These molecular associations based on the spirobifluorenes are noteworthy because they are purely molecular analogues of zeolites and other microporous solids, with potential applications in separation, catalysis, sensing and other areas.

Optical Characterization of Organic Gain Materials for UV-emitting Hybrid Complex Coupled VCSELs

In this thesis, optical gain measurement setup based on variable stripe length method is designed, implemented and improved. The setup is characterized using inorganic and organic samples. The optical gain of spiro-quaterphenyl is calculated and compared with measurements from the setup. Films with various thicknesses of spiro-quaterphenyl, methoxy-spiro-quaterphenyl and phenoxy-spiro-quaterphenyl are deposited by a vacuum vapor deposition technique forming asymmetric slab waveguides. The optical properties, laser emission threshold, optical gain and loss coefficient for these films are measured. Additionally, the photodegradation during pumping process is investigated.

Multi-Robot Task Allocation for Inspection Problems with Cooperative Tasks Using Hybrid Genetic Algorithms

In dieser Dissertation werden Methoden zur optimalen Aufgabenverteilung in Multirobotersystemen (engl. Multi-Robot Task Allocation – MRTA) zur Inspektion von Industrieanlagen untersucht. MRTA umfasst die Verteilung und Ablaufplanung von Aufgaben für eine Gruppe von Robotern unter Berücksichtigung von operativen Randbedingungen mit dem Ziel, die Gesamteinsatzkosten zu minimieren. Dank zunehmendem technischen Fortschritt und sinkenden Technologiekosten ist das Interesse an mobilen Robotern für den Industrieeinsatz in den letzten Jahren stark gestiegen. Viele Arbeiten konzentrieren sich auf Probleme der Mobilität wie Selbstlokalisierung und Kartierung, aber nur wenige Arbeiten untersuchen die optimale Aufgabenverteilung. Da sich mit einer guten Aufgabenverteilung eine effizientere Planung erreichen lässt (z. B. niedrigere Kosten, kürzere Ausführungszeit), ist das Ziel dieser Arbeit die Entwicklung von Lösungsmethoden für das aus Inspektionsaufgaben mit Einzel- und Zweiroboteraufgaben folgende Such-/Optimierungsproblem. Ein neuartiger hybrider Genetischer Algorithmus wird vorgestellt, der einen teilbevölkerungbasierten Genetischen Algorithmus zur globalen Optimierung mit lokalen Suchheuristiken kombiniert. Zur Beschleunigung dieses Algorithmus werden auf die fittesten Individuen einer Generation lokale Suchoperatoren angewendet. Der vorgestellte Algorithmus verteilt die Aufgaben nicht nur einfach und legt den Ablauf fest, sondern er bildet auch temporäre Roboterverbünde für Zweiroboteraufgaben, wodurch räumliche und zeitliche Randbedingungen entstehen. Vier alternative Kodierungsstrategien werden für den vorgestellten Algorithmus entworfen: Teilaufgabenbasierte Kodierung: Hierdurch werden alle möglichen Lösungen abgedeckt, allerdings ist der Suchraum sehr groß. Aufgabenbasierte Kodierung: Zwei Möglichkeiten zur Zuweisung von Zweiroboteraufgaben wurden implementiert, um die Effizienz des Algorithmus zu steigern. Gruppierungsbasierte Kodierung: Zeitliche Randbedingungen zur Gruppierung von Aufgaben werden vorgestellt, um gute Lösungen innerhalb einer kleinen Anzahl von Generationen zu erhalten. Zwei Umsetzungsvarianten werden vorgestellt. Dekompositionsbasierte Kodierung: Drei geometrische Zerlegungen wurden entworfen, die Informationen über die räumliche Anordnung ausnutzen, um Probleme zu lösen, die Inspektionsgebiete mit rechteckigen Geometrien aufweisen. In Simulationsstudien wird die Leistungsfähigkeit der verschiedenen hybriden Genetischen Algorithmen untersucht. Dazu wurde die Inspektion von Tanklagern einer Erdölraffinerie mit einer Gruppe homogener Inspektionsroboter als Anwendungsfall gewählt. Die Simulationen zeigen, dass Kodierungsstrategien, die auf der geometrischen Zerlegung basieren, bei einer kleinen Anzahl an Generationen eine bessere Lösung finden können als die anderen untersuchten Strategien. Diese Arbeit beschäftigt sich mit Einzel- und Zweiroboteraufgaben, die entweder von einem einzelnen mobilen Roboter erledigt werden können oder die Zusammenarbeit von zwei Robotern erfordern. Eine Erweiterung des entwickelten Algorithmus zur Behandlung von Aufgaben, die mehr als zwei Roboter erfordern, ist möglich, würde aber die Komplexität der Optimierungsaufgabe deutlich vergrößern.

Efficient Characterisation and Optimal Control of Open Quantum Systems - Mathematical Foundations and Physical Applications

Since no physical system can ever be completely isolated from its environment, the study of open quantum systems is pivotal to reliably and accurately control complex quantum systems. In practice, reliability of the control field needs to be confirmed via certification of the target evolution while accuracy requires the derivation of high-fidelity control schemes in the presence of decoherence. In the first part of this thesis an algebraic framework is presented that allows to determine the minimal requirements on the unique characterisation of arbitrary unitary gates in open quantum systems, independent on the particular physical implementation of the employed quantum device. To this end, a set of theorems is devised that can be used to assess whether a given set of input states on a quantum channel is sufficient to judge whether a desired unitary gate is realised. This allows to determine the minimal input for such a task, which proves to be, quite remarkably, independent of system size. These results allow to elucidate the fundamental limits regarding certification and tomography of open quantum systems. The combination of these insights with state-of-the-art Monte Carlo process certification techniques permits a significant improvement of the scaling when certifying arbitrary unitary gates. This improvement is not only restricted to quantum information devices where the basic information carrier is the qubit but it also extends to systems where the fundamental informational entities can be of arbitary dimensionality, the so-called qudits. The second part of this thesis concerns the impact of these findings from the point of view of Optimal Control Theory (OCT). OCT for quantum systems utilises concepts from engineering such as feedback and optimisation to engineer constructive and destructive interferences in order to steer a physical process in a desired direction. It turns out that the aforementioned mathematical findings allow to deduce novel optimisation functionals that significantly reduce not only the required memory for numerical control algorithms but also the total CPU time required to obtain a certain fidelity for the optimised process. The thesis concludes by discussing two problems of fundamental interest in quantum information processing from the point of view of optimal control - the preparation of pure states and the implementation of unitary gates in open quantum systems. For both cases specific physical examples are considered: for the former the vibrational cooling of molecules via optical pumping and for the latter a superconducting phase qudit implementation. In particular, it is illustrated how features of the environment can be exploited to reach the desired targets.

Generative Temporal Planning with Complex Processes

Autonomous vehicles are increasingly being used in mission-critical applications, and robust methods are needed for controlling these inherently unreliable and complex systems. This thesis advocates the use of model-based programming, which allows mission designers to program autonomous missions at the level of a coach or wing commander. To support such a system, this thesis presents the Spock generative planner. To generate plans, Spock must be able to piece together vehicle commands and team tactics that have a complex behavior represented by concurrent processes. This is in contrast to traditional planners, whose operators represent simple atomic or durative actions. Spock represents operators using the RMPL language, which describes behaviors using parallel and sequential compositions of state and activity episodes. RMPL is useful for controlling mobile autonomous missions because it allows mission designers to quickly encode expressive activity models using object-oriented design methods and an intuitive set of activity combinators. Spock also is significant in that it uniformly represents operators and plan-space processes in terms of Temporal Plan Networks, which support temporal flexibility for robust plan execution. Finally, Spock is implemented as a forward progression optimal planner that walks monotonically forward through plan processes, closing any open conditions and resolving any conflicts. This thesis describes the Spock algorithm in detail, along with example problems and test results.