| 1. |
- Annergren, Mariette, 1982-
(författare)
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Application-Oriented Input Design and Optimization Methods Involving ADMM
- 2016
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- This thesis is divided into two main parts. The first part considers application-oriented input design, specifically for model predictive control (MPC). The second part considers alternating direction method of multipliers (ADMM) for ℓ1 regularized optimization problems and primal-dual interior-point methods.The theory of system identification provides methods for estimating models of dynamical systems from experimental data. This thesis is focused on identifying models used for control, with special attention to MPC. The objective is to minimize the cost of the identification experiment while guaranteeing, with high probability, that the obtained model gives an acceptable control performance. We use application-oriented input design to find such a model. We present a general procedure of implementing application-oriented input design to unknown, possibly nonlinear, systems controlled using MPC. The practical aspects of application-oriented input design are addressed and the method is tested in an experimental study.In addition, a MATLAB-based toolbox for solving application-oriented input design problems is presented. The purpose of the toolbox is threefold: it is used in research; it facilitates communication of research results; it helps an engineer to use application-oriented input design.Several important problems in science can be formulated as convex optimization problems. As such, there exist very efficient algorithms for finding the solutions. We are interested in methods that can handle optimization problems with a very large number of variables. ADMM is a method capable of handling such problems. We derive a scalable and efficient algorithm based on ADMM for two ℓ1 regularized optimization problems: ℓ1 mean and covariance filtering, and ℓ1 regularized MPC. The former occurs in signal processing and the latter is a specific type of model based control.We are also interested in optimization problems with certain structural limitations. These limitations inhibit the use of a central computational unit to solve the problems. We derive a distributed method for solving them instead. The method is a primal-dual interior-point method that uses ADMM to distribute all the calculations necessary to solve the optimization problem at hand.
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| 2. |
- Barenthin Syberg, Märta, 1979-
(författare)
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Complexity Issues, Validation and Input Design for Control in System Identification
- 2008
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- System identification is about constructing and validating modelsfrom measured data. When designing system identificationexperiments in control applications, there are many aspects toconsider. One important aspect is the choice of model structure.Another crucial issue is the design of input signals. Once a modelof the system has been estimated, it is essential to validate theclosed loop performance if the feedback controller is based onthis model. In this thesis we consider the prediction-erroridentification method. We study model structure complexity issues,input design and model validation for control. To describe real-life systems with high accuracy, models of veryhigh complexity are typically needed. However, the variance of themodel estimate usually increases with the model order. In thisthesis we investigate why system identification, despite thisrather pessimistic observation, is successfully applied in theindustrial practise as a reliable modelling tool. It is shown thatby designing suitable input signals for the identificationexperiment, we obtain accurate estimates of the frequency functionalso for very complex systems. The input power spectrum can beused to shape the model quality. A key tool in input design is tointroduce a linear parametrization of the spectrum. With thisparametrization, several optimal input design problems can berewritten as convex optimization problems. Another problem considered is to design controllers withguaranteed robust stability and prescribed robust performanceusing models identified from experimental data. These models areuncertain due to process noise, measurement noise and unmodelleddynamics. In this thesis we only consider errors due tomeasurement noise. The model uncertainty is represented byellipsoidal confidence regions in the model parameter space. Wedevelop tools to cope with these ellipsoids for scalar andmultivariable models. These tools are used for designing robustcontrollers, for validating the closed loop performance and forimproving the model with input design. Therefore this thesis ispart of the research effort to connect prediction-erroridentification methods and robust control theory. The stability of the closed loop system can be validated using thesmall gain theorem. A critical issue is thus to have an accurateestimate of the L2-gain of the system. The key tosolve this problem is to find the input signal that maximizes thegain. One approach is to use a model of the system to design theinput signal. An alternative approach is to let the system itselfdetermine a suitable input sequence in repeated experiments. Insuch an approach no model of the system is required. Proceduresfor gain estimation of linear and nonlinear systems are discussedand compared.
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| 3. |
- Lima, Pedro, 1990-
(författare)
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Predictive control for autonomous driving : With experimental evaluation on a heavy-duty construction truck
- 2016
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Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Autonomous vehicles is a rapidly expanding field, and promise to play an important role in society. In more isolated environments, vehicle automation can bring significant efficiency and production benefits and it eliminates repetitive jobs that can lead to inattention and accidents.The thesis addresses the problem of lateral and longitudinal dynamics control of autonomous ground vehicles with the purpose of accurate and smooth path following. Clothoids are used in the design of optimal predictive controllers aimed at minimizing the lateral forces and jerks in the vehicle.First, a clothoid-based path sparsification algorithm is proposed to efficiently describe the reference path. This approach relies on a sparseness regularization technique such that a minimal number of clothoids is used to describe the reference path.Second, a clothoid-based model predictive controller (MPCC) is proposed. This controller aims at producing a smooth driving by taking advantage of the clothoid properties. Third, we formulate the problem as an economic model predictive controller (EMPC). In EMPC the objective function contains an economic cost (here represented by comfort or smoothness), which is described by the second and first derivatives of the curvature. Fourth, the generation of feasible speed profiles, and the longitudinal vehicle control for following these, is studied. The speed profile generation is formulated as an optimization problem with two contradictory objectives: to drive as fast as possible while accelerating as little as possible. The longitudinal controller is formulated in a similar way, but in a receding horizon fashion.The experimental evaluation with the EMPC demonstrates its good performance, since the deviation from the path never exceeds 30 cm and in average is 6 cm. In simulation, the EMPC and the MPCC are compared with a pure-pursuit controller (PPC) and a standard MPC. The EMPC clearly outperforms the PPC in terms of path accuracy and the standard MPC in terms of driving smoothness.
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| 4. |
- Winqvist, Rebecka, 1996-
(författare)
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Learning in the Loop : On Neural Network-based Model Predictive Control and Cooperative System Identification
- 2023
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Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Inom reglerteknik har integrationen av maskininlärningsmetoder framträtt som en central strategi för att förbättra prestanda och adaptivitet hos styrsystem. Betydande framsteg har gjorts inom flera viktiga aspekter av reglerkretsen, såsom inlärningsbaserade metoder för systemidentifiering och parameterskattning, filtrering och brusreducering samt reglersyntes. Denna avhandling fördjupar sig i området inlärning för reglerteknik med särskild betoning på inlärningsbaserade regulatorer och identifieringsmetoder. Avhandlingens första del behandlar undersökningen av neuronnätsbaserad Modellprediktiv Reglering (MPC). Olika nätstrukturer studeras, både generella black box-nät och nät som väver in MPC-specifik information i sin struktur. Dessa nät jämförs och utvärderas med avseende på två prestandamått genom experiment på realistiska två- och fyrdimensionella system. Den huvudsakliga nyskapande aspekten är inkluderingen av gradientdata i träningsprocessen, vilket visar sig förbättra noggrannheten av de genererade styrsignalerna. Vidare påvisar de experimentella resultaten att en MPC-informerad nätstruktur leder till förbättrad prestanda när mängden träningsdata är begränsad. Med insikt om vikten av noggranna matematiska modeller av styrsystemet, riktar den andra delen av avhandlingen sitt fokus mot inlärningsbaserade identifieringsmetoder. Denna forskningsgren behandlar karakterisering och modellering av dynamiska system med hjälp av maskininlärning. Avhandlingen bidrar till området genom att introducera kooperativa systemidentifieringsmetoder för att förbättra parameterskattningen. Specifikt utnyttjas verktyg från Optimal Transport för att introducera en ny och mer generell formulering av ramverket Correctional Learning. Detta ramverk är baserat på en mästare-lärlingsmodell, där en expertagent (mästare) observerar och modifierar den insamlade data som används av en lärande agent (lärling), med syftet att förbättra lärlingens skattningsprocess. Genom att formulera correctional learning som ett optimal transport-problem erhålls ett mer flexibelt ramverk, bättre lämpat för skattning av komplexa systemegenskaper samt anpassning till alternativa handlingsstrategier.
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| 5. |
- Annergren, Mariette
(författare)
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ADMM for l1 Regularized Optimization Problems and Applications Oriented Input Design for MPC
- 2012
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Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- This licentiate thesis is divided into two main parts. The first part considers alternating direction method of multipliers (ADMM) for ℓ1 regularized optimization problems and the second part considers applications oriented input design for model predictive control (MPC).Many important problems in science and engineering can be formulated as convex optimization problems. As such, they have a unique solution and there exist very efficient algorithms for finding the solution. We are interested in methods that can handle big, in terms of the number of variables, optimization problems in an efficient way. Large optimization problems are common in many fields of research, for example, the problem of feature selection from huge medical data sets. ADMM is a method capable of handling such problems. We derive a scalable and efficient algorithm based on ADMM for two ℓ1 regularized optimization problems: ℓ1 mean and covariance filtering that occur in signal processing, and ℓ1 regularized MPC that is a specific type of model based control.System identification provides tools for estimating models of dynamical systems from experimental data. The application of such models can be divided into three main categories: prediction, simulation and control. We focus on identifying models used for control, with special attention to MPC. The objective is to minimize a cost related to the identification experiment while guaranteeing, with high probability, that the obtained model gives an acceptable control performance. We use applications oriented input design to find such a model. We present a general procedure of implementing applications oriented input design to unknown, and possibly nonlinear, systems controlled using MPC. In addition, we show that the input design problem obtained for output-error systems has the same simple structure as for finite impulse response systems.
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| 6. |
- de Miranda de Matos Lourenço, Inês, 1994-
(författare)
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Forward and Inverse Decision-Making in Adversarial, Cooperative, and Biologically-Inspired Dynamical Systems
- 2021
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Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Decision-making is the mechanism of using available information to develop solutions to given problems by forming preferences, beliefs, or selecting courses of action amongst several alternatives. It is the main focus of a variety of scientific fields such as robotics, finances, and neuroscience. In this thesis, we study the mechanisms that generate behavior in diverse decision-making settings (the forward problem) and how their characteristics can explain observed behavior (the inverse problem). Both problems take a central role in current research due to the desire to understand the features of system behavior, many times under situations of risk and uncertainty. We study decision-making problems in the three following settings.In the first setting, we consider a decision-maker who forms a private belief (posterior distribution) on the state of the world by filtering private information. Estimating private beliefs is a way to understand what drives decisions. This forms a foundation for predicting, and counteracting against, future actions. In the setting of adversarial systems, we answer the problems of i) how can an adversary estimate the private belief of the decision-maker by observing its decisions (under two different scenarios), and ii) how can the decision-maker protect its private belief by confusing the adversary. We exemplify the applicability of our frameworks in regime-switching Markovian portfolio allocation.In the second setting we shift from an adversarial to a cooperative scenario. We consider a teacher-student framework similar to that used in learning from demonstration and transfer learning setups. An expert agent (teacher) knows the model of a system and wants to assist a learner agent (student) in performing identification for that system but cannot directly transfer its knowledge to the student. For example, the teacher's knowledge of the system might be abstract or the teacher and student might be employing different model classes, which renders the teacher's parameters uninformative to the student. We propose correctional learning as an approach where, in order to assist the student, the teacher can intercept the observations collected from the system and modify them to maximize the amount of information the student receives about the system. We obtain finite-sample results for correctional learning of binomial systems.In the third and final setting we shift our attention to cognitive science and decision-making of biological systems, to obtain insight about the intrinsic characteristics of these systems. We focus on time perception - how humans and animals perceive the passage of time, and solve the forward problem by designing a biologically-inspired decision-making framework that replicates the mechanisms responsible for time perception. We conclude that a simulated robot equipped with our framework is able to perceive time similarly to animals - when it comes to their intrinsic mechanisms of interpreting time and performing time-aware actions. We then focus on the inverse problem. Based on the empirical action probability distribution of the agent, we are able to estimate the parameters it uses for perceiving time. Our work shows promising results when it comes to drawing conclusions regarding some of the characteristics present in biological timing mechanisms.
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| 7. |
- de Miranda de Matos Lourenço, Inês, 1994-
(författare)
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Learning from Interactions : Forward and Inverse Decision-Making for Autonomous Dynamical Systems
- 2023
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Decision-making is the mechanism of using available information to generate solutions to given problems by forming preferences, beliefs, and selecting courses of action amongst several alternatives. In this thesis, we study the mechanisms that generate behavior (the forward problem) and how their characteristics can explain observed behavior (the inverse problem). Both problems play a pivotal role in contemporary research due to the desire to design sophisticated autonomous agents that serve as the building blocks for a smart society, amidst complexity, risk, and uncertainty. This work explores different parts of the autonomous decision-making process where agents learn from interacting with each other and the environment that surrounds them. We address fundamental problems of behavior modeling, parameter estimation in the form of beliefs, distributions, and reward functions, and then finally interactions with other agents; which lay the foundation for a complete and integrative framework for decision-making and learning. The thesis is divided into four parts, each featuring a different information exchange paradigm.First, we model the forward problem of how a decision-maker forms beliefs about the world and the inverse problem of estimating these beliefs from the agent’s behavior. The private belief (posterior distribution) on the state of the world is formed according to a hidden Markov model by filtering private information. The ability to estimate private beliefs forms a foundation for predicting and counteracting against future actions. We answer the problems of i) how the private belief of the decision-maker can be estimated by observing its decisions (under two different scenarios), and ii) how the decision-maker can protect its private belief from an adversary by confusing it. We exemplify the applicability of our frameworks in regime-switching Markovian portfolio allocation.In the second part, we study forward decision-making of biological systems and the inverse problem of how to obtain insight into their intrinsic characteristics. We focus on time perception – how humans and animals perceive the passage of time – and design a biologically-inspired decision-making framework using reinforcement learning that replicates timing mechanisms. We show that a simulated robot equipped with our framework is able to perceive time similarly to animals, and that by analyzing its performed actions we are able to estimate the parameters of timing mechanisms.Next, we consider teacher-student settings where a teacher agent can intervene with the decision-making process of a student agent to assist it in performing a task. In the third part, we propose correctional learning as an approach where the teacher can intercept the observations the student collects from the system and modify them to improve the estimation process of the student. We provide finite-sample results for batch correctional learning in system identification, generalize it to more complex systems using optimal transport, and lower-bound improvements on the estimate’s variance for the online case.Decision-making in teacher-student settings like the previous one requires both agents to have aligned models of understanding of each other. In the fourth and last part of this thesis, the teacher can, instead, alter the decisions of the decision-maker in a human-robot interaction setting. We use a confidence-based misalignment detection method that enables the robot to update its knowledge proportionally to its confidence in the human corrections and propose a framework to disambiguate between misalignment caused by incorrectly learned features that do not generalize to new environments and features entirely missing from the robot’s model. We demonstrate the proposed framework in a 7 degrees-of-freedom robot manipulator with physical human corrections and show how to initiate the model realignment process once misalignment is detected.
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| 8. |
- Ebadat, Afrooz, 1986-
(författare)
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Experiment Design for Closed-loop System Identification with Applications in Model Predictive Control and Occupancy Estimation
- 2017
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- The objective of this thesis is to develop algorithms for application-oriented input design. This procedure takes the model application into account when designing experiments for system identification.This thesis is divided into two parts. The first part considers the theory of application-oriented input design, with special attention to Model Predictive Control (MPC). We start by studying how to find a convex approximation of the set of models that result in acceptable control performance using analytical methods when controllers with no closed-form control law, for e.g., MPC are employed. The application-oriented input design is formulated in time domain to enable handling of signals constraints. The framework is extended to closed-loop systems where two cases are considered i.e., when the plant is controlled by a general but known controller and for the case of MPC. To this end, an external stationary signal is designed via graph theory. Different sources of uncertainty in application-oriented input design are investigated and a robust application-oriented input design framework is proposed.The second part of this thesis is devoted to the problem of estimating the number of occupants based on the information available to HVAC systems in buildings. The occupancy estimation is first formulated as a two-tier problem. In the first tier, the room dynamic is identified using temporary measurements of occupancy. In the second tier, the identified model is employed to formulate the problem as a fused-lasso problem. The proposed method is further developed to be used as a multi-room estimator using a physics-based model. However, since it is not always possible to collect measurements of occupancy, we proceed by proposing a blind identification algorithm which estimates the room dynamic and occupancy, simultaneously. Finally, the application-oriented input design framework is employed to collect data that is informative enough for occupancy estimation purposes.
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| 9. |
- Ebadat, Afrooz, 1986-
(författare)
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On Application Oriented Experiment Design for Closed-loop System Identification
- 2015
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Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- System identification concerns how to construct mathematical models of dynamic systems based on experimental data. A very important application of system identification is in model-based control design. In such applications it is often possible to externally excite the system during the data collection experiment. The properties of the exciting input signal influence the quality of the identified model, and well-designed input signals can reduce both the experimental time and effort. The objective of this thesis is to develop algorithms and theory for minimum cost experiment design for system identification while guaranteeing that the estimated model results in an acceptable control performance. We will use the framework of application oriented Optimal Input Design (OID). First, we study how to find a convex approximation of the set of models that results in acceptable control performance. The main contribution is analytical methods to determine application sets for controllers with no explicit control law, for instance Model Predictive Control (MPC). The application oriented OID problem is then formulated in time domain to enable the handling of signals constraints, which often comes from the physical limitations on the plant and actuators. The framework is the extended to closed-loopsystems. Here two different cases are considered. The first case assumes that the plant is controlled by a general (either linear or non-linear) but known controller. The main contribution here is a method to design an external stationary signal via graph theory such that the identification requirements and signal constraints are satisfied. In the second case application oriented OID problem is studied for MPC. The proposed approach here is a modification of a results where the experiment design requirements are integrated to the MPC as a constraint. The main idea is to back off from the identification requirements when the control requirements are violating from their acceptable bounds. We evaluate the effectiveness of all the proposed algorithms by several simulation examples.
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| 10. |
- González, Rodrigo A., 1992-
(författare)
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Consistency and efficiency in continuous-time system identification
- 2020
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Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Continuous-time system identification deals with the problem of building continuous-time models of dynamical systems from sampled input and output data. In this field, there are two main approaches: indirect and direct. In the indirect approach, a suitable discrete-time model is first determined, and then it is transformed into continuous-time. On the other hand, the direct approach obtains a continuous-time model directly from the sampled data. In both approaches there exists a dichotomy between discrete-time data and continuous-time models, which can induce robustness issues and complications in the theoretical analysis of identification algorithms. These difficulties are addressed in this thesis.First, we consider the indirect approach to continuous-time system identification. For a zero-order hold sampling mechanism, this approach usually leads to a transfer function estimate with relative degree one, independent of the relative degree of the strictly proper true system. Inspired by the indirect prediction error method, we propose an indirect-approach estimator that enforces the desired number of poles and zeros in the continuous-time transfer function estimate, and show that the estimator is consistent and asymptotically efficient. A robustification of this method is also developed, by which the estimates are also guaranteed to deliver stable models.In the second part of the thesis, we analyze asymptotic properties of the Simplified Refined Instrumental Variable method for Continuous-time systems (SRIVC), which is one of the most popular direct identification methods. This algorithm applies an adaptive prefiltering to the sampled input and output that requires assumptions on the intersample behavior of the signals. We present a comprehensive analysis on the consistency and asymptotic efficiency of the SRIVC estimator while taking into account the intersample behavior of the input signal. Our results show that the SRIVC estimator is generically consistent when the intersample behavior of the input is known exactly and subsequently used in the implementation of the algorithm, and we give conditions under which consistency is not achieved. In terms of statistical efficiency, we compute the asymptotic Cramér-Rao lower bound for an output error model structure with Gaussian noise, and derive the asymptotic covariance of the SRIVC estimates. We conclude that the SRIVC estimator is asymptotically efficient under mild conditions, and that this property can be lost if the intersample behavior of the input is not carefully accounted for in the SRIVC procedure.Moreover, we propose and analyze the statistical properties of an extension of SRIVC that is able to deal with input signals that cannot be interpolated exactly via hold reconstructions. The proposed estimator is generically consistent for any input reconstructed using zero or first-order-hold devices, and we show that it is generically consistent for continuous-time multisine inputs as well. Comparisons with the Maximum Likelihood technique and an analysis of the iterations of the method are provided, in order to reveal the influence of the intersample behavior of the output and to propose new robustifications to the SRIVC algorithm.
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