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Calibri 83ffff̙̙3f3fff3f3f33333f33333.TU Delft Repositoryg Kuuidrepository linktitleauthorcontributorpublication yearabstract
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departmentresearch group programmeprojectcoordinates)uuid:680681ae59db4b9fafa13df1f74900bfDhttp://resolver.tudelft.nl/uuid:680681ae59db4b9fafa13df1f74900bfOFast optimizationbased control and estimation using operator splitting methodsStathopoulos, G.Keviczky, T. (mentor)jThe size of modern technological systems has grown significantly and the need for fast, online optimizationbased control is a necessity. The complexity of these systems along with the need for increased accuracy in the computed outcome dictate the usage of optimization methods for the computation of the solution. Besides this, new data arrive in real time and in fast rates, that have to be incorporated efficiently in the control policy under design. Exploiting the stateoftheart, multicore, computing architectures, we can achieve fast online solutions of optimal control problems by splitting a large problem into several smaller ones that can be solved in parallel. In this thesis, we apply an operator splitting technique to a generic convex optimal control problem. The resulting algorithm alternates between a quadratic regulator iteration, and a step in which singleperiod optimization problems are solved in parallel. Depending on the constraints and nonquadratic objective terms, these singleperiod problems can be solved extremely quickly, or even analytically in many cases. In the timeinvariant case, precomputing the gain matrices in the quadratic regulator problem gives another speedup, as well as an algorithm that requires no division, and is therefore suitable for implementation with fixedpoint processor. The method is demonstrated on several examples arising in different application areas. Furthermore, we develop an extension to this algorithm so that it can handle more generic optimal control problems and propose a different decomposition approach that achieves an even higher level of parallelization.+System & Control; Operator splitting methoden
master thesis
20120717.Mechanical, Maritime and Materials Engineering$Delft Center for Systems and Control)uuid:dd0c766a5f1647998c8439abbddbce9eDhttp://resolver.tudelft.nl/uuid:dd0c766a5f1647998c8439abbddbce9eiCooperative adaptive cruise control: Using information from multiple predecessors in combination with MPCKreuzen, C.De Schutter, B. (mentor)Cooperative adaptive cruise control (CACC) makes the vehicle follow its predecessor at a close but safe distance, and uses information received from other vehicles to accomplish this task. In literature and in practice, the control method mostly applied for CACC is proportional integral derivative (PID) control, possibly with some refinement for gear shifting or comfort. The control method called model predictive control (MPC) can also be used for CACC, and from literature it appears to be more promising than PID, because of its ability to anticipate future situations and to implement constraints directly into the control algorithm. MPC applies the first input of a control input sequence that optimises a performance index calculated from predicted system behaviour, based on a prediction model, subject to operational constraints, in a receding horizon approach. Furthermore, literature has shown that with PID the use of state information from the second predecessor or the platoon leader, in addition to the direct predecessor s states, can improve the CACC performance. Therefore, in this thesis the approach of using such additional communicated information from either the second predecessor or the platoon leader is combined with the use of MPC as control method. The goal is to investigate whether any of these two configurations give an increase in performance compared with similar configurations with PID as control method, and compared with a more basic configuration that uses just the direct predecessor s state < information with either MPC or PID. Also, the possibly added value of using communicated predicted states, in addition to current states, with MPC is investigated. The CACC controllers are designed to control the throttle, the brakes, and the gears, subject to operational constraints on acceleration, velocity, and vehicletovehicle distance. The PIDbased CACC controller contains a proportional feedback of the errors in velocity, position, and acceleration, combined with an automatic transmission scheme, and the control input is restricted at time instants at which a constraint is (almost) violated. The MPCbased CACC controller at each time step minimises the expected errors in position and velocity and the corresponding input variation. The MPC prediction model is obtained by approximating a nonlinear vehicle model by a piecewise affine (PWA) model, and converting the MPC optimisation problem into a mixed integer linear programming (MILP) problem. In this project, tuning is done by applying simulated annealing for a scenario involving four CACCcontrolled vehicles following a platoon leader. Then, the tuned controllers are implemented in a validation scenario comprising a larger platoon undergoing a longer simulation. The results from simulating this validation scenario show that the PIDbased CACC controller has a low responsiveness, compared with MPC, and lets the first two vehicles crash. With MPC several peaks and oscillations in throttle/brake input and acceleration occur, and it is expected that with the MPCbased CACC controllers as designed and tuned here, string stability will not always be achieved for increasing platoon lengths. It is expected that properly retuning will result in better performing controllers. However, due to limited time, this retuning could not be performed within the scope of this project, and is therefore left as a recommendation. Therefore, only preliminary conclusions can be formulated, which are that MPC should be preferred over PID as a control method for CACC, because it is safer. Moreover, with MPC it should be preferred to, in addition to the current states of the direct predecessor, at least use the current states of the second predecessor and/or the predicted future states from the direct predecessor, in order to achieve better string stability.*system & control; model predictive control)uuid:4e3e8583e791466a962fe323db12668bDhttp://resolver.tudelft.nl/uuid:4e3e8583e791466a962fe323db12668b?Receding Horizon Control of Perturbed Railway Network OperationKleijn, A.C.Van den Boom, A.J.J. (mentor)Railway networks, such as the one in the Netherlands, form an important means of transportation, both for passengers, as well as for transporting goods. Train services carrying passengers often run according to a predefined schedule or timetable. When those train services are delayed, for example due to accidents or malfunctioning rolling stock, the affected train may not be able to run according to schedule any longer. When the railway network is dense and hosts different kinds of services, such as local and intercity services, this initial delay is easily passed on to other train services in the network, due to different stopping patterns and drive speeds. Human dispatchers, possibly aided by computer systems, make temporary modifications to the way the network is used by the trains running in the region of the disturbance. However, due to the high complexity and the limited time, these decisions may be optimal for only the designated area of the dispatcher, but far from optimal from a network perspective. Therefore, a railway network operator could have a major benefit from a system able to compute globally optimal decisions in the case of disturbances. This thesis is written as part of the development of such a system. Specifically, this project aims at applying Model Predictive Control (MPC) to railway networks. In MPC, a model of the system is used to predict the future behaviour of the system within a prediction horizon. The principle of receding horizon control is employed to compute< an optimal future input sequence, such that a certain cost is minimized. This cost is the total delay in the network within the prediction horizon. The inputs of the controlled system are associated to the order of a train pair on a track. Train orders can be swapped at stations and junctions. As there are many of those points present in the network, swapping orders offers the most possibilities and is effective in a wide range of delay scenarios. Therefore, in this thesis only order swaps are considered. The system is modelled within a maxplus algebraic framework, which allows for a structured representation and systematic approach, where the latter is especially useful for future endeavours to exploit maxplus system theory, such that for example model reduction can be applied to the generally very large railway models. The algorithm presented in this thesis forms the basis for an MPC algorithm for railway networks. The development of a tailor made receding horizon control algorithm for railway networks, has not been carried out before. First an extension of the existing maxplus linear model is presented, such that a model which is uncertain in the parameters is obtained. This model already contains controllable train orders. The problem of finding the optimal order swaps, such that the total delay in the network is minimal, can be written as a Mixed Integer Linear Programming problem. Several test cases were derived to highlight the various aspects of the receding horizon control algorithm, such as how it copes with different parameter estimations at various points in time. Through the use of a timebased control horizon, a significant reduction in computation time was achieved an provides a very good method to overcome the computational complexity encountered during optimal control of railway networks. Although the prediction horizon is defined in the discrete event domain, the algorithm is easily modified to also contain a timebased prediction horizon, allowing for more freedom in tuning of the prediction horizon and thus also computation times.7System & Control; Mechinal Engineering; Horizon Control
20120525$Delft Centre for Systems and Control)uuid:3b5a38775a5743399145e4bef8019b4aDhttp://resolver.tudelft.nl/uuid:3b5a38775a5743399145e4bef8019b4a3Switched LQR control: Design of a general framework
Veneman, J.B.This thesis studies the Switched Linear Quadratic Regulator (SLQR) problem, over a hybrid (continuous and discrete) dynamical model known as "switched system". The problem is defined as computing the optimal continuous and discrete switching control to minimise a quadratic cost function that weights the states and the continuous controls. The original SLQR problem does not handle constraints on states, continuous or discrete controls, and there is no probabilistic behaviour. This thesis focuses on the discrete dynamics in a SLQR problem. The first part of the thesis describes the SLQR problem with discrete constraints, whereas the second part is dedicated to probabilistic switching behaviour. The problem with discrete constraints is described as finding the optimal hybrid switching policy that minimises a quadratic cost function, weighting states and continuous controls, without violating the discrete constraints. The problem with probabilistic switches is defined as finding the optimal hybrid switching policy that minimises an expected value of a quadratic cost function, weighting states and continuous controls. For the SLQR problem with discrete constraints a general relaxation framework is developed to simplify the representations of the value functions and the corresponding control strategies. It is shown that the closed loop performance of the obtained solution with the relaxation framework can be made arbitrarily close to the optimal solution. For the SLQR problem with probabilistic switches it is shown that a relaxation framework can only be developed when there are no discrete constraints involved. Finally, the thesis concludes with a few case studies to illustrate how the optimal hybrid control seq<}uence is computed.System & Control; Mechanical Engineering; LQR)uuid:3cfb667ec1094877a6340c83eeeb45d3Dhttp://resolver.tudelft.nl/uuid:3cfb667ec1094877a6340c83eeeb45d3PSoft Sensors Development for Model Based Control of Groundwater Treatment Plants
Chokshi, N.K.The drinking water in the Netherlands has been aimed to achieve high quality of water along with a low production cost. Inorder to produce such high quality of treated water, constant monitoring of the process variables at the end of each treatment step is recommended. These processes are monitored and operated by motivated and skilled operators and process technologists, which leads to an operator dependent, subjective, variable and possibly suboptimal operation of the treatment plants. Furthermore, the extensive automation of the treatment plants reduces the possible operator attention to the individual process units. The use of mathematical or blackbox process models might solve these problems. This thesis thus focusses on the development of soft sensors i.e models that can simulate the rapid sand filter (one of the drinking water processes) process and thus be able to estimate/predict the output variables of interest, such that optimal control schemes could be developed to control the quality of the produced water. Before an appropriate model can be designed, it is necessary to analyse the drinking water treatment processes of the pilot plant. In general the treatment processes are robust, but ignoring the typical process behaviour can hamper the optimal performance. One of the readily available mathematical/whitebox model is assessed, only to conclude that assessed whitebox model has few limitations hampering the main objective of the research, thus motivating us to use blackbox modelling approach instead. Using blackbox approach,the water quality parameters to be estimated are determined using subspace and parametric estimation schemes. The strategy for implementing these system identification schemes have been investigated. These techniques have been applied to experimental data collected for the rapid sand filter process and a good fit to the observed dynamics is obtained. Models obtained through these techniques have been discussed and compared using various validation tests.Bembedded systems; soft sensors; water treatment; systems & control
201205238Electrical Engineering, Mathematics and Computer ScienceEmbedded systems3mE/DCSC)uuid:321e450a7b254daf8ae52cf90e5651bcDhttp://resolver.tudelft.nl/uuid:321e450a7b254daf8ae52cf90e5651bcGModeling and control principles for a tentaclelike surgical instrumentShirokov, V.A.Babuka, R. (mentor)In this project a challenge in the field of minimally invasive surgery is addressed. Contemporary minimally invasive instruments usually consist of a rigid beam with an end effector on one side and a controlling mechanism on the other. These instruments work well for cases when the body can be entered in such a way that the target, on which to operate, is reachable in a straight line. When it is not possible to reach the target in a straight line, a new solution is necessary. This thesis proposes to replace the rigid beam with a robotic tentacle. The tentacle considered in this thesis is modeled after an Ionic PolymerMetal Composite (IPMC) which is an ElectroActive Polymer (EAP), a material which mechanically deforms once put in an electric field. Electromechanical properties of the IPMC are combined with a layered geometric configuration of strips of IPMC to construct the tentacle. An electromechanical simulation model is constructed followed by the design of a curvature controller. The performance of the controller is analyzed in simulation. The results obtained from the simulation prove the concept of the robotic tentacle made of strips of IPMC as a possible solution to improve reaching capabilities of contemporary minimally invasive surgical instruments.>System & Control; Mechanical Engineering; Modelling principles
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