Optimal Control of Multiple Quadrotors for Transporting a Cable Suspended Payload

In this thesis, the main aim is to improve the flight control performance for a cable suspended payload with single and two quadrotors based on optimised control techniques. The study utilised optimal controllers, such as the Linear Quadratic Regulator LQR, the Iterative based LQR (ILQR), the Model Predictive Control MPC and the dynamic game controller to solve tracking control problems in terms of stabilisation, accuracy, constraints and collision avoidance. The LQR control was applied to the system as the first control method and compared with the classical Proportional-Derivative controller PD. It was used to achieve the load path tracking performance for single and two quadrotors with a cable slung load. The second controller was ILQR, which was developed based on the LQR control method to deal with the model nonlinearity. The MPC technique was also applied to the linearised nonlinear model LMPC of two quadrotors with a payload suspended by cables and compared with a nonlinear MPC (NMPC). Both MPC controllers LMPC and NMPC considered the constraints imposed on the system states and control inputs. The dynamic game control method was developed based on an incentive strategy for a leader-follower framework with the consideration of different optimal cost functions. It was applied to the linearised nonlinear model. Selecting these control techniques led to a number of achievements. Firstly, they improved the system performance in terms of achieving the system stability and reducing the steady-state errors. Secondly, the system parameter uncertainties were taken into consideration by utilising the ILQR controller. Thirdly, the MPC controllers guaranteed the handling of constraints and external disturbances in linear and nonlinear systems. Finally, avoiding collision between the leader and follower robots was achieved by applying the dynamic game controller. The controllers were tested in MATLAB simulation and verified for various desired predefined trajectories. In real experiments, these controllers were used as high-level controllers, which produce the optimised trajectory points. Then a low-level controller (PD controller) was used to follow the optimised trajectory points

Planning distribution network using the multi-agent game and distribution system operators

When planning the distribution network, the income of each market entity is calculated by a fixed price. How to take the price of power into account while developing the planning strategy for each organization in the actual power market is an urgent issue that needs to be addressed imminently. To address this problem, considering the continuous change in the market price due to the change in the supply–demand relationship in the actual power market, this article proposes a distribution network planning method which considers the distribution system operator (DSO) and multi-agent game. First, the planning decision model of distribution network companies and power users with different interest subjects is constructed with grid planning and DG operation as decision variables. Second, DSO is introduced to the game framework. Based on the distribution locational marginal pricing (DLMP), a price accounting model is being developed. Then, the transfer relationships and game behavior among the distribution company, power users, and DSO are analyzed. Finally, an iterative search algorithm is used to solve a multi-agent game planning model of a distribution network that takes into account price signals in the power market. Examples based on IEEE 33-bus systems validate the suggested method’s validity and efficacy

Energy Sharing Models for Renewable Energy Integration: Subtransmission Level, Distribution Level, and Community Level

Distributed energy resources (DERs) are being embedded rapidly and widely in the power grid and promoting the transformation of the centralized power industry to a more deregulated mode. However, how to safely and efficiently consume renewable energy is becoming a major concern. In this regard, energy sharing at both grid-scale and community-scale has emerged as a new solution to encourage participants to actively bid instead of acting as price takers and has the potential to accelerate the integration of DERs and decrease energy costs. At the grid level, two risk-averse energy sharing models are developed to safely integrate renewable energy by considering the network constraints and overbidding risk. A risk-averse two-stage stochastic game model is proposed for the regional energy sharing market (ESM). The sample average approximation (SAA) method is used to approximate the stochastic Cournot-Nash equilibrium. In addition, a data-driven joint chance-constrained game is developed for energy sharing in the local energy market (LEM). This model considers the maximum outputs of renewable energy aggregators (REAs) are random variables whose probability distributions are unknown, but the decision-maker has access to finite samples. Case studies show that the proposed game models can effectively increase the profit of reliable players and decrease the overbidding risk. At the community level, a community server enables energy sharing among users based on the Bayesian game-based pricing mechanism. It can also control the community energy storage system (CESS) to smooth the load based on the grid's price signal. A communication-censored ADMM for sharing problems is developed to decrease the communication cost between the community and the grid. Moreover, a co-optimization model for the plan and operation of the shared CESS is developed. By introducing the price uncertainty and degradation cost, the proposed model could more accurately evaluate the performance of the CESS and tap more economic potential. This thesis provides proof of the Nash equilibrium of all game models and the convergence of all market clearing algorithms. The proposed models and methods present performance improvement compared with existing solutions. The work in this thesis indicates that energy sharing is possible to implement at different levels of the power system and could benefit the participants and promote the integration of DERs

Multi-Agent Systems

A multi-agent system (MAS) is a system composed of multiple interacting intelligent agents. Multi-agent systems can be used to solve problems which are difficult or impossible for an individual agent or monolithic system to solve. Agent systems are open and extensible systems that allow for the deployment of autonomous and proactive software components. Multi-agent systems have been brought up and used in several application domains

Journal of Telecommunications and Information Technology, 2010, nr 2

kwartalni

Interaction-Aware Motion Planning for Automated Vehicles

Die Bewegungsplanung für automatisierte Fahrzeuge (AVs) in gemischtem Verkehr ist eine herausfordernde Aufgabe. Hierbei bezeichnet gemischter Verkehr, Verkehr bestehend aus von Menschen gefahrenen Fahrzeugen sowie automatisierten Fahrzeugen. Um die Komplexität der Aufgabe zu reduzieren, verwenden state-of-the-art Planungsansätze oft die vereinfachende Annahme, dass das zukünftige Verhalten umliegender Fahrzeuge unabhängig vom Plan des AVs vorhergesagt werden kann. Während die Trennung von Prädiktion und Planung für viele Verkehrssituationen eine hilfreiche Vereinfachung darstellt, werden hierbei Interaktionen zwischen den Verkehrsteilnehmern ignoriert, was besonders in interaktiven Verkehrssituationen zu suboptimalem, übermäßig konservativem Fahrverhalten führen kann. In dieser Arbeit werden zwei interaktionsbewusste Bewegungsplanungsalgorithmen vorgeschlagen, die in der Lage sind übermäßig konservatives Fahrverhalten zu reduzieren. Der Kernaspekt dieser Algorithmen ist, dass Prädiktion und Planung gleichzeitig gelöst werden. Mit diesen Algorithmen können anspruchsvolle Fahrmanöver, wie z. B. das Reißverschlussverfahren in dichtem Verkehr, durchgeführt werden, die mit state-of-the-art Planungsansätzen nicht möglich sind. Der erste Algorithmus basiert auf Methoden der Multi-Agenten-Planung. Interaktionen zwischen Verkehrsteilnehmern werden durch Optimierung gekoppelter Trajektorien mittels einer gemeinsamen Kostenfunktion approximiert. Das Kernstück des Algorithmus ist eine neuartige Multi-Agenten-Trajektorienplanungsformulierung, die auf gemischt-ganzzahliger quadratischer Programmierung (MIQP) basiert. Die Formulierung garantiert global optimale Lösungen und ist somit in der Lage das kombinatorische Problem zu lösen, welches kontinuierliche Methoden auf lokal optimale Lösungen beschränkt. Desweiteren kann durch den vorgestellten Ansatz ein manöverneutrales Verhalten erzeugt werden, das Manöverentscheidungen in ungewissen Situationen aufschieben kann. Der zweite Ansatz formuliert Interaktionen zwischen einem menschlichen Fahrer und einem AV als ein Stackelberg-Spiel. Im Gegensatz zu bestehenden Arbeiten kann der Algorithmus allgemeine nichtlineare Zustands- und Eingabebeschränkungen berücksichtigen. Desweiteren führen wir Mechanismen zur Integration von Kooperation und Rücksichtnahme in die Planung ein. Damit wird übermäßig aggressives Fahrverhalten verhindert, was in der Literatur als ein Problem interaktionsbewusster Planungsmethoden identifiziert wurde. Die Wirksamkeit, Robustheit und Echtzeitfähigkeit des Algorithmus wird durch numerische Experimente gezeigt

MIT Space Engineering Research Center

The Space Engineering Research Center (SERC) at MIT, started in Jul. 1988, has completed two years of research. The Center is approaching the operational phase of its first testbed, is midway through the construction of a second testbed, and is in the design phase of a third. We presently have seven participating faculty, four participating staff members, ten graduate students, and numerous undergraduates. This report reviews the testbed programs, individual graduate research, other SERC activities not funded by the Center, interaction with non-MIT organizations, and SERC milestones. Published papers made possible by SERC funding are included at the end of the report

Journal of Telecommunications and Information Technology

kwartalni

Fair relay selection in wireless rural networks using game theory

Access to Internet is the key to facilitate the economic growth and development of the rural communities and to bridge the digital-divide between the urban and rural population. The traditional broadband access technologies are not always suitable for the rural areas due to their difficult topography and sparsely populated communities. Specialized relay stations can be deployed to extend the coverage of a wireless rural network but they come with an inherited increase in the infrastructural cost. An alternative is to utilize the in-range users as relays to enhance the coverage range of the wireless rural network. In this thesis, the in-range ordinary users termed as primary users (PUs) are used to act as relays for the out-of-range users called the secondary users (SUs). Two relay selection solutions, the Fair Battery Power Consumption (FBPC) algorithm and the Credit based Fair Relay Selection (CF-RS) protocol have been proposed with the aim of providing fair chance to every PU to assist the SUs, thus resulting in fair utilization of battery power of all relays along with the coverage extension. The FBPC algorithm uses the concept of proportional fairness as the relay selection criterion. However, if only proportionally fair consumption of battery power is taken as the relay selection parameter, the FBPC algorithm may result in selecting relays with poor channel conditions. The rural network may also consist of selfish PUs which need to be incentivized to use their resources for the SUs. The CF-RS protocol is developed which takes into account both the achievable data rate and consumption of battery power for selection of a relay. The CF-RS protocol is formulated using Stackelberg game which employs a credit-based incentive mechanism to motivate the self-interested PUs to help the SUs by providing instantaneous as well as long term benefit to the PUs. A basic network model consisting of PUs and SUs has been simulated and the performance of the FBPC algorithm and the CF-RS protocol have been evaluated in terms of data rate and utility achievable at the SUs, dissipation of battery power of the PUs and Jain’s fairness index to determine fairness in utilization of battery power. The results obtained show that the FBPC algorithm achieves approximately 100% fairness for utilization of battery power of relays but compromises the data rate attainable by the SUs. Thus the FBPC algorithm shall be viewed as a trade-off between the fair battery power dissipation of relays and the data rate achievable by the SUs. Whereas, the CF-RS protocol provides 55% better utility and longer service time to the SUs without harming the attainable data rate and achieves 80% fairness. When the CF-RS protocol is used for relay selection, it is advantageous even for the self-interested users to participate in the relaying process to earn some benefit to utilize it when needed to buy assistance from other users

Models and algorithms for decomposition problems

This thesis deals with the decomposition both as a solution method and as a problem itself. A decomposition approach can be very effective for mathematical problems presenting a specific structure in which the associated matrix of coefficients is sparse and it is diagonalizable in blocks. But, this kind of structure may not be evident from the most natural formulation of the problem. Thus, its coefficient matrix may be preprocessed by solving a structure detection problem in order to understand if a decomposition method can successfully be applied. So, this thesis deals with the k-Vertex Cut problem, that is the problem of finding the minimum subset of nodes whose removal disconnects a graph into at least k components, and it models relevant applications in matrix decomposition for solving systems of equations by parallel computing. The capacitated k-Vertex Separator problem, instead, asks to find a subset of vertices of minimum cardinality the deletion of which disconnects a given graph in at most k shores and the size of each shore must not be larger than a given capacity value. Also this problem is of great importance for matrix decomposition algorithms. This thesis also addresses the Chance-Constrained Mathematical Program that represents a significant example in which decomposition techniques can be successfully applied. This is a class of stochastic optimization problems in which the feasible region depends on the realization of a random variable and the solution must optimize a given objective function while belonging to the feasible region with a probability that must be above a given value. In this thesis, a decomposition approach for this problem is introduced. The thesis also addresses the Fractional Knapsack Problem with Penalties, a variant of the knapsack problem in which items can be split at the expense of a penalty depending on the fractional quantity