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This paper proposes a shifting feedback linearization controller for nonlinear systems under input saturation. The suggested approach for determining a linear parameter-varying (LPV) state-feedback controller is based on employing a linear matrix inequality (LMI)-based methodology, the input-output feedback linearization approach, and the shifting paradigm concept. The resultant controller deals with a full linearized version of the nonlinear system under state-dependent input constraints, and it modifies the closed-loop convergence speed of the system in accordance with changes in the system’s region of linearity. The proposed approach is validated by means of an experimental example., This work has been partially funded by the Spanish State Research Agency (AEI) and the European Regional Development Fund (ERFD) through the project SaCoAV (ref. PID2020-114244RB-I00). This work has also been partially funded by AGAUR of Generalitat de Catalunya through the Advanced Control Systems (SAC) group grant (2017 SGR 482) and by the University of Stavanger through the project IN-12267. Adrián Ruiz is also supported by the Secretaria d'Universitats i Recerca de la Generalitat de Catalunya, the European Social Fund (ESF) and AGAUR under a FI SDUR grant (ref. 2020 FI-SDUR 00097). The authors would like to thank Didrik Efjestad Fjereide from the University of Stavanger (UiS) for his technical support., Peer Reviewed, Postprint (published version)

(English) Although robotic platforms have been used for a wide range of purposes, recent advances in robot autonomy raise the opportunity of bringing them closer to humans. This implies that, under any circumstance, robots have to remain safe --i.e. not harm humans, the environment or themselves-- while embedding the required adaptation means to deal with the unstructured nature of anthropic domains. Hence, initial solutions delivered reactive behaviours in order to avoid collisions, which is not suitable for those tasks where the robot is required to initiate, maintain or regulate contact over physical interaction with a human, an environment or both. At control level, which represents the earliest reaction mechanism, this calls for the development of solutions that address the new sources of hazard in physical interactions that might jeopardise robot safety while providing the required level of adaptation. This is the starting point of this Thesis, focused on the use of advances in control theory to benefit safety and adaptation in solutions for physical interaction tasks. For this purpose, recent state-of-art control solutions are analysed through a novel architecture conceived for physical interaction tasks. From the perspective of safety and adaptation concepts, it shows an absence of a systematic framework to set guarantees on operation beyond stability. Moreover, existing approaches come at the cost of restrictions on adaptation strategies, which might deteriorate robot performance, or increase its complexity, which hinders their deployment. Hence, this Thesis proposes the use of a model-based approach using Linear Parameter Varying (LPV) paradigm to formulate solutions and systems in combination with the formulation of conditions in terms of Linear Matrix Inequalities (LMI). The LPV representation provides linear-like descriptions through a set of varying parameters. In such way, a depiction of its complete range of operation can be defined by only consideri, (Español) A pesar de que las plataformas robóticas han sido usadas con múltiples propósitos, los recientes avances en su autonomía plantean la oportunidad de acercarlas a los humanos. Esto implica que, bajo cualquier circunstancia, los robots tienen que mantenerse seguros --es decir, no dañar a los humanos, al entorno o a ellos mismo-- a la vez que incorporar los medios de adaptación adecuados para lidiar con la naturaleza no estructurada de los entornos antrópicos. Por consiguiente, las primeras soluciones ofrecían comportamientos reactivos con el fin de evitar colisiones, lo cual no es apto para aquellas tareas donde el robot tiene que iniciar, mantener o regular la interacción física con un humano, el entorno o ambos. A nivel de control, que representa el mecanismo de reacción más inmediato, esto requiere el desarrollo de soluciones dirigidas a las nuevos riesgos que surjan en las interacción físicas que puedan poner en peligro la seguridad del robot así como proveer el nivel de adaptación requerido. Este es el punto de partida de esta Tesis, centrada en el uso de técnicas avanzadas en la teoría de control que beneficien la seguridad y adaptación de las soluciones para tareas con interacción física. Con este propósito se ha realizado un análisis de la literatura reciente sobre soluciones de control a través de una nueva arquitectura concebida para tareas de interacción física. Considerando los conceptos de seguridad y adaptación se muestra la ausencia de un marco sistemático para establecer garantías en la operación más allá de la estabilidad. Además, los enfoques existentes limitan las estrategias de adaptación, deteriorando el desempeño del robot, o incrementando su complejidad, que dificulta su implementación. Por ello, esta Tesis propone usar el paradigma de Variación Lineal de Parámetros (VLP) para formular soluciones y sistemas en combinación con la definición de condiciones en términos de Desigualdades Matriciales Lineales (DML). La formulación VLP proporc, Postprint (published version)

The aim of this work is the design of a current control by means of state feedback, based on linear matrix inequalities (LMI), and it is applied in a DC-DC coupled inductors Buck-Boost modular converter, which has been widely used in distributed generation systems with renewable energy sources. This method considers constraints on the location of poles defined in the complex plane called d-stability. The closed-loop control system is implemented in Matlab® Simulink and validated under different test scenarios, where better dynamic performance is obtained, its operating range is extended, with shorter settling times and a better time-response, in contrast with the classical PI control technique., Este trabajo tiene como objetivo diseñar un control de corriente por realimentación de estados, basado en desigualdades matriciales lineales (LMI) aplicado en un convertidor modular DC-DC Buck-Boost de inductores acoplados, el cual ha sido ampliamente utilizado en sistemas de generación distribuida con fuentes de energías renovables. Este método considera restricciones en la ubicación de polos definidas en el plano complejo denominadas d-stability. El sistema de control en lazo cerrado se implementa en Simulink de Matlab® y se valida ante diferentes escenarios de prueba, donde se obtienen mejores prestaciones dinámicas, se amplía su rango de operación, con tiempos de establecimiento menores y una mejor respuesta temporal, en contraste con la técnica de control PI clásica.

The performance of fault detection filters relies on a high sensitivity to faults and a low sensitivity to disturbances. The aim of this paper is to develop an approach to directly shape these sensitivities, expressed in terms of minimum and maximum singular values. The developed method offers an alternative solution to the H -/H ∞ synthesis problem, building upon traditional multiobjective synthesis results. The result is an optimal filter synthesized via iterative convex optimization and the approach is particularly useful for fault diagnosis as illustrated by a numerical example.

The convergence and robustness rejecting parameters variations and external disturbance of the system are crucial for repetitive processes. In this paper, a two-dimensional robust fractional-order iterative learning control (FOILC) is proposed for the repetitive motion process to enhance the convergence and robustness. A fractional-order proportional derivative function (FOPDF) is designed as the control variable to replace the tracking error of the integer-order iterative learning control (IOILC) algorithm. The required dynamic output fractional-order iterative learning controller is constructed by solving a set of linear matrix inequalities (LMI), and the control parameters are adjusted according to the given specifications. Simulation and experimental results in robot torque control are given to prove the effectiveness and feasibility of the proposed design method. © 2023 by the authors.

In this article, we treat extended balancing for continuous-time linear time-invariant systems. We take a dissipativity perspective, thus, resulting in a characterization in terms of linear matrix inequalities. This perspective is useful for determining a priori error bounds. In addition, we address the problem of structure-preserving model reduction of the subclass of port-Hamiltonian systems. We establish sufficient conditions to ensure that the reduced-order model preserves a port-Hamiltonian structure. Moreover, we show that the use of extended Gramians can be exploited to get a small error bound and, possibly, to preserve a physical interpretation for the reduced-order model. We illustrate the results with a large-scale mechanical system example. Furthermore, we show how to interpret a reduced-order model of an electrical circuit again as a lower dimensional electrical circuit., Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public., Learning & Autonomous Control

Recently, the rapid development of Unmanned Aerial Vehicles (UAVs) enables ecological conservation, such as low-carbon and “green” transport, which helps environmental sustainability. In order to address control issues in a given region, UAV charging infrastructure is urgently needed. To better achieve this task, an investigation into the T–S fuzzy modeling for Sustainable Hypersonic Vehicles (SHVs) with Markovian jump parameters and H∞ attitude control in three channels was conducted. Initially, the reentry dynamics were transformed into a control–oriented affine nonlinear model. Then, the original T–S local modeling method for SHV was projected by primarily referring to Taylor's expansion and fuzzy linearization methodologies. After the estimation of precision and controller complexity was assumed, the fuzzy model for jump nonlinear systems mainly consisted of two levels: a crisp level and a fuzzy level. The former illustrates the jumps, and the latter a fuzzy level that represents the nonlinearities of the system. Then, a systematic method built in a new coupled Lyapunov function for a stochastic fuzzy controller was used to guarantee the closed–loop system for H∞ gain in the presence of a predefined performance index. Ultimately, numerical simulations were conducted to show how the suggested controller can be successfully applied and functioned in controlling the original attitude dynamics. © 2023 Elsevier Inc., Funding: The Ph.D. fund 20z14 was used to fund this research.

During the last decades, robust model-based fault detection and isolation (FDI) has received increasing interest in the literature. It is a fact that FDI methods can provide fault indicators for predictive maintenance and, consequently, preventing significant damages to the system components. Following this route, this doctorate thesis develops model-based fault detection and isolation techniques for linear discrete-time systems as well as for linear discrete-time descriptor systems. The proposed approach is based on the design of observer-based residual generators in order to detect and isolate actuator and/or sensor faults. Besides, a reference model is considered to describe the desired behavior from faults to residual, which allows for including requirements on fault decoupling as well as performance issues regarding robustness related to the optimization problem. Therefore, the residual generator is designed such that the system-filter connection approximately follows the fault to residual behavior provided by the reference model. A multi-objective optimization is designed involving different system criteria, such as $H_infty / H_-$ and $ extit{peak-norm} / H_-$ ,which depends on the class of inputs of the system (respectively, $ell_2$ or $ell_infty$ signals). The design conditions based on modified Lyapunov dissipation inequalities are cast in terms of Linear Matrix Inequalities (LMI) constraints. In order to illustrate the theoretical developments, numerical examples are provided as well as the application of the method to a simulated lithium-ion battery pack. There are four main contributions for this thesis. The design of FDI techniques integrated with a triangular reference model structure allows the fault detection and isolation when the faults do not occur simultaneously. Besides, the residual generator design conditions described in terms of LMI constraints made possible to consider mixed performance specifications and different classes of systems in a, Doctorat en Sciences de l'ingénieur et technologie, info:eu-repo/semantics/nonPublished

Diese Dissertation präsentiert neue Ergebnisse zur kooperativen Regelung von Fahrzeugen. Modulare Architekturen werden untersucht, die aus Netzwerken vereinfachter Modelle und komplexer Fahrzeuge bestehen, die die von diesen vereinfachten Modellen erzeugten Trajektorien verfolgen. Dissipativitätstheorie und \textit{integral quadratic constraints} werden verwendet, um hinreichende Bedingungen für Stabilität und Regelgüte zu finden, die höchstens linear mit der Netzwerkgröße skalieren. Konvexe und nicht-konvexe Interaktionen zwischen Fahrzeugen werden im Zusammenhang mit einem \textit{source-seeking} Problem betrachtet, bei dem Fahrzeuge das Minimum eines externen Skalarfelds lokalisieren müssen. Experimente mit Quadrotoren und Simulationsstudien veranschaulichen die Ergebnisse., This thesis presents results on cooperative control of vehicles by studying modular architectures consisting of networks of simplified models and complex vehicles tracking the trajectories generated by these simplified models. Convex intervehicle interactions from formation control and non-convex inter-vehicle interactions emerging from flocking dynamics are studied within the context of a source-seeking problem, where vehicles are required to locate the minimum of an external scalar field. The main analysis tools are dissipativity theory and integral quadratic constraints while the experimental results are with quadrotors.

This article investigates the design of a robust fixed-order controller for single-input–single-output (SISO) polytopic systems with interval uncertainties, with the aim that the closed-loop stability is appropriately ensured and the performance specifications on sensitivity shaping are conformed in a specific finite frequency range. Utilizing the notion of generalized common Lyapunov strictly positive realness (CL-SPRness), the equivalence between strictly positive realness (SPRness) and strictly bounded realness (SBRness) is established; and then, the specifications on robust stability and performance are transformed into the SPRness of newly constructed systems and further characterized in the framework of linear matrix inequality (LMI) conditions. The proposed methodology avoids the tedious yet mandatory evaluations of the specifications on all vertices of the uncertain polytopic system in an explicit form. Instead, solving five LMIs exclusively suffices for ensuring the robust stability and performance regardless of the number of vertices, and thus, the typically heavy computational burden is considerably alleviated. It is also noteworthy that the proposed methodology additionally provides the necessary and sufficient conditions for this robust controller design with the consideration of a prescribed finite frequency range, and therefore, significantly less conservatism is attained in the system performance. IEEE

The performance of fault detection filters relies on a high sensitivity to faults and a low sensitivity to disturbances. The aim of this paper is to develop an approach to directly shape these sensitivities, expressed in terms of minimum and maximum singular values. The developed method offers an alternative solution to the H-/H∞ synthesis problem, building upon traditional multiobjective synthesis results. The result is an optimal filter synthesized via iterative convex optimization and the approach is particularly useful for fault diagnosis as illustrated by a numerical example., Team Jan-Willem van Wingerden

Mean square error optimal estimation requires the full correlation structure to be available. Unfortunately, it is not always possible to maintain full knowledge about the correlations. One example is decentralized data fusion where the cross-correlations between estimates are unknown, partly due to information sharing. To avoid underestimating the covariance of an estimate in such situations, conservative estimation is one option. In this paper the conservative linear unbiased estimator is formalized including optimality criteria. Fundamental bounds of the optimal conservative linear unbiased estimator are derived. A main contribution is a general approach for computing the proposed estimator based on robust optimization. Furthermore, it is shown that several existing estimation algorithms are special cases of the optimal conservative linear unbiased estimator. An evaluation verifies the theoretical considerations and shows that the optimization based approach performs better than existing conservative estimation methods in certain cases., Funding Agencies|Center for Industrial Information Technology at Linkoping University [17.12]; Industry Excellence Center LINK-SIC - Swedish Governmental Agency for Innovation Systems; Saab AB; Project Scalable Kalman filters - Swedish Research Council

Mean square error optimal estimation requires the full correlation structure to be available. Unfortunately, it is not always possible to maintain full knowledge about the correlations. One example is decentralized data fusion where the cross-correlations between estimates are unknown, partly due to information sharing. To avoid underestimating the covariance of an estimate in such situations, conservative estimation is one option. In this paper the conservative linear unbiased estimator is formalized including optimality criteria. Fundamental bounds of the optimal conservative linear unbiased estimator are derived. A main contribution is a general approach for computing the proposed estimator based on robust optimization. Furthermore, it is shown that several existing estimation algorithms are special cases of the optimal conservative linear unbiased estimator. An evaluation verifies the theoretical considerations and shows that the optimization based approach performs better than existing conservative estimation methods in certain cases., Funding Agencies|Center for Industrial Information Technology at Linkoping University [17.12]; Industry Excellence Center LINK-SIC - Swedish Governmental Agency for Innovation Systems; Saab AB; Project Scalable Kalman filters - Swedish Research Council

Mean square error optimal estimation requires the full correlation structure to be available. Unfortunately, it is not always possible to maintain full knowledge about the correlations. One example is decentralized data fusion where the cross-correlations between estimates are unknown, partly due to information sharing. To avoid underestimating the covariance of an estimate in such situations, conservative estimation is one option. In this paper the conservative linear unbiased estimator is formalized including optimality criteria. Fundamental bounds of the optimal conservative linear unbiased estimator are derived. A main contribution is a general approach for computing the proposed estimator based on robust optimization. Furthermore, it is shown that several existing estimation algorithms are special cases of the optimal conservative linear unbiased estimator. An evaluation verifies the theoretical considerations and shows that the optimization based approach performs better than existing conservative estimation methods in certain cases., Funding Agencies|Center for Industrial Information Technology at Linkoping University [17.12]; Industry Excellence Center LINK-SIC - Swedish Governmental Agency for Innovation Systems; Saab AB; Project Scalable Kalman filters - Swedish Research Council

In this paper, we propose a new mathematical model for power flow problem based on the linear and nonlinear matrix inequality theory. We start with rectangular model of power flow (PF) problem and then reformulate it as a Bilinear Matrix Inequality (BMI) model. A Theorem is proved which is able to convert this BMI model to a Linear Matrix Inequality (LMI) model along with One Nonconvex Quadratic Constraint (ONQC). Our proposed LMI-ONQC model for PF problem has only one single nonconvex quadratic constraint irrespective of the network size, while in the rectangular and BMI models the number of nonconvex constraints grows as the network size grows. This interesting property leads to reduced complexity level in our LMI-ONQC model which in turn makes it easier to solve for finding a PF solution. The non-conservativeness, iterative LMI solvability, well-defined and easy-to-understand geometry and pathwise connectivity of feasibility region are other important properties of proposed LMI-ONQC model which are discussed in this paper. An illustrative two-bus example is carefully studied to show different properties of our LMI-ONQC model. We have also tested our LMI-ONQC model on 30 different power-system cases including four ill-conditioned systems and compared it with a group of existing approaches. The numerical results show the promising performance of our LMI-ONQC model and its solution algorithm to find a PF solution., QC 20220404

We are interested in determining the worst performance exhibited by a given first-order optimization method on the class of quadratic functions. Since its introduction, the Performance Estimation Problem (PEP) methodology has allowed the computation of the exact worst-case performance of first-order optimization methods on several functions classes, including smooth convex, strongly convex or nonconvex functions. In this work, we extend the PEP framework to the class of quadratic functions, and apply it to analyze the difference of performance of the gradient method between convex quadratic and general smooth convex functions.

This paper considers the problem of the robust stabilisation of a class of continuous Linear Parameter Varying (LPV) systems under specifications. In order to guarantee the stabilisation of the plant with very large parameter uncertainties or variations, an output derivative estimation controller is considered. The design of such controller that guarantee desired  induced gain performance is examined. Furthermore, a simple procedure for achieving the  norm performance is proved for any all-poles single-input/single-output second order plant. The proof of stability is based on the polytopic representation of the closed loop under Lyapunov conditions and system transformations. Finally, the effectiveness of the proposed method is verified via a numerical example.

This paper studies periodic event-triggered actuation applied to PID, cascade, and decoupling control. We propose an event-triggered output feedback controller, in which the control command is actuated only when it exceeds its previous value by a certain threshold. An exponential stability condition is derived in the form of LMIs using a Lyapunov–Krasovskii functional based on Wirtinger's inequality. It is shown that an observer-based controller can reject an unknown step disturbance. Using this result, we propose a way to tune the event threshold subject to a given stability margin. We apply the proposed framework to PID, cascade, and decoupling control to illustrate how the event thresholds can be tuned in practice. Numerical examples show for these three control architectures how controller–actuator communication can be reduced without performance degradation., QC 20220921

[no abstract available]

Mean square error optimal estimation requires the full correlation structure to be available. Unfortunately, it is not always possible to maintain full knowledge about the correlations. One example is decentralized data fusion where the cross-correlations between estimates are unknown, partly due to information sharing. To avoid underestimating the covariance of an estimate in such situations, conservative estimation is one option. In this paper the conservative linear unbiased estimator is formalized including optimality criteria. Fundamental bounds of the optimal conservative linear unbiased estimator are derived. A main contribution is a general approach for computing the proposed estimator based on robust optimization. Furthermore, it is shown that several existing estimation algorithms are special cases of the optimal conservative linear unbiased estimator. An evaluation verifies the theoretical considerations and shows that the optimization based approach performs better than existing conservative estimation methods in certain cases., Funding Agencies|Center for Industrial Information Technology at Linkoping University [17.12]; Industry Excellence Center LINK-SIC - Swedish Governmental Agency for Innovation Systems; Saab AB; Project Scalable Kalman filters - Swedish Research Council

The stabilizability of a linear dynamical system (LDS) refers to the existence of control inputs that drive the system state to zero. In this article, we analyze both the theoretical and algorithmic aspects of the stabilizability of an LDS using sparse control inputs with potentially time-varying supports. We show that an LDS is stabilizable using sparse control inputs if and only if it is stabilizable (using unconstrained inputs). For a stabilizable LDS, we present an algorithm to determine the sparse control inputs that steer the system state to zero. We show that all stabilizable LDSs are also sparse mean square stabilizable when the process noise has zero mean and bounded second moment. For such an LDS, we devise a method to sequentially estimate the sparse control inputs to stabilize the LDS in the mean square sense. We prove that a detectable and stabilizable LDS is sparse stabilizable through output feedback and develop an algorithm for finding the corresponding sparse control inputs. Finally, we analyze the stabilizability of an LDS using sparse control inputs with common support. Our results shed light on the conditions under which a given LDS is stabilizable using sparse control inputs and the design of the corresponding control inputs., Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public., Signal Processing Systems

This paper considers the problem of the robust stabilisation of a class of continuous Linear Parameter Varying (LPV) systems under specifications. In order to guarantee the stabilisation of the plant with very large parameter uncertainties or variations, an output derivative estimation controller is considered. The design of such controller that guarantee desired  induced gain performance is examined. Furthermore, a simple procedure for achieving the  norm performance is proved for any all-poles single-input/single-output second order plant. The proof of stability is based on the polytopic representation of the closed loop under Lyapunov conditions and system transformations. Finally, the effectiveness of the proposed method is verified via a numerical example.

This article proposes a systematic approach for optimal reset law design of a class of nonlinear systems. By using the guaranteed cost control method, sufficient conditions for the design of optimal reset law are derived in terms of linear matrix inequalities. In an offline design procedure, the reset law is computed that minimizes the upper bound of a quadratic cost function. The proposed method can be implemented for real-time applications even with small sampling time. The simulation results verify the efficacy and effectiveness of the proposed theoretical results., Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public., Mechatronic Systems Design

In network estimation and control systems like sensor networks or industrial robotic systems,there are often restrictions or uncertainties that must be taken into account. For example, there are often bandwidth and communication constraints on the estimators or controllers. Additionally, the dynamics model is not always known. Lastly, noise or exogenous disturbances can adversely affect your system. This thesis addresses three problems in sensing and control in both the H2 and risk-sensitivecontrol setting. The first problem stems from restrictions on the communications and battery life of sensors. Because of these restrictions, when estimating a state in a system we must cleverly schedule which sensors can be active. The second problem also stems from communication restrictions. In this setting, the sensors and actuators can only communicate with a small number of neighboring sensors. Therefore, we must solve a distributed control problem. The third problem stems from the dynamics of a system being unknown. In this regard, we must solve a control problem using simulated data instead of a fixed model. The research in this thesis, utilizes tools from optimization, estimation, control, and dynamic programming.

Mean square error optimal estimation requires the full correlation structure to be available. Unfortunately, it is not always possible to maintain full knowledge about the correlations. One example is decentralized data fusion where the cross-correlations between estimates are unknown, partly due to information sharing. To avoid underestimating the covariance of an estimate in such situations, conservative estimation is one option. In this paper the conservative linear unbiased estimator is formalized including optimality criteria. Fundamental bounds of the optimal conservative linear unbiased estimator are derived. A main contribution is a general approach for computing the proposed estimator based on robust optimization. Furthermore, it is shown that several existing estimation algorithms are special cases of the optimal conservative linear unbiased estimator. An evaluation verifies the theoretical considerations and shows that the optimization based approach performs better than existing conservative estimation methods in certain cases., Funding Agencies|Center for Industrial Information Technology at Linkoping University [17.12]; Industry Excellence Center LINK-SIC - Swedish Governmental Agency for Innovation Systems; Saab AB; Project Scalable Kalman filters - Swedish Research Council

Mean square error optimal estimation requires the full correlation structure to be available. Unfortunately, it is not always possible to maintain full knowledge about the correlations. One example is decentralized data fusion where the cross-correlations between estimates are unknown, partly due to information sharing. To avoid underestimating the covariance of an estimate in such situations, conservative estimation is one option. In this paper the conservative linear unbiased estimator is formalized including optimality criteria. Fundamental bounds of the optimal conservative linear unbiased estimator are derived. A main contribution is a general approach for computing the proposed estimator based on robust optimization. Furthermore, it is shown that several existing estimation algorithms are special cases of the optimal conservative linear unbiased estimator. An evaluation verifies the theoretical considerations and shows that the optimization based approach performs better than existing conservative estimation methods in certain cases., Funding Agencies|Center for Industrial Information Technology at Linkoping University [17.12]; Industry Excellence Center LINK-SIC - Swedish Governmental Agency for Innovation Systems; Saab AB; Project Scalable Kalman filters - Swedish Research Council

This article addresses the multiagent systems consensus control problem via a learning observer-based performance tuning control policy. Specifically, a novel learning observer is presented to reconstruct the compound nonlinear terms and system states simultaneously. Based on the learning observer’s reconstructed information, a novel performance tuning control policy is proposed to deal with the internal nonlinear terms and external disturbances acting on the system while providing prescribed consensus performance. The proposed learning observer can guarantee the uniformly ultimately bounded estimation while saving computing resources, which is beneficial to the multiagent system. The proposed control policy, combined with observer and performance tuning, ensures the robustness to nonideal perturbations and the high accuracy control performance simultaneously. Mathematical simulations verify the effectiveness of the control algorithm.

This paper presents a novel approach for designing reconfiguration blocks for fault hiding of linear systems subject to actuator faults based on the passivity/dissipativity theory. For this purpose, the concept of passivation block is used to design virtual actuators (VAs) which guarantee that the faulty plant achieves the desired passivity indices and consequently the stability. Linear matrix inequalities (LMI)-based conditions are provided for designing the proposed VAs for ensuring the stability recovery for linear systems. Finally, a numerical example is used for assessing the proposed approach., Peer Reviewed, Postprint (published version)

This paper considers the problem of the robust stabilisation of a class of continuous Linear Parameter Varying (LPV) systems under specifications. In order to guarantee the stabilisation of the plant with very large parameter uncertainties or variations, an output derivative estimation controller is considered. The design of such controller that guarantee desired  induced gain performance is examined. Furthermore, a simple procedure for achieving the  norm performance is proved for any all-poles single-input/single-output second order plant. The proof of stability is based on the polytopic representation of the closed loop under Lyapunov conditions and system transformations. Finally, the effectiveness of the proposed method is verified via a numerical example.

[EN] Coalitional control is a fairly new branch of distributed control where the agents merge dynamically into coalitions according to the enabled/disabled communication links at each time instant. Therefore, with these schemes there is a reduction of the communication burden without compromising the system performance. In this tutorial, the main features of these schemes will be introduced in the framework of cooperative game theory, being the game related to the cost function that is optimized by the control approach, and with the players corresponding to either the communication links or the agents involved. In this context, several cooperative game theory tools will be considered in order to: rank the players, impose constraints on them, provide more effcient ways of calculation, perform system partitioning, etc., hence analyzing the main features related to each tool., [ES] El control coalicional es una rama incipiente del control distribuido donde los distintos agentes se agrupan de forma dinámica en coaliciones en función de los enlaces de comunicación activos/inactivos en cada instante de tiempo. Gracias a ello, se reduce la carga de comunicación sin comprometer las prestaciones del sistema. En este tutorial, se analizan las principales características de estos esquemas dentro del marco de la teoría de juegos cooperativos, estando el juego definido por la función de coste a optimizar en el esquema de control, y correspondiendo los jugadores bien a los enlaces de comunicación o bien a los propios agentes. En este contexto, se estudiarán diversas herramientas de teoría de juegos cooperativos, con objeto de clasificar jugadores, imponer restricciones en los mismos, proponer vías de cálculo más eficientes, realizar particionado de sistemas, etc., examinando las características más relevantes presentadas por cada herramienta.

[EN] Coalitional control is a fairly new branch of distributed control where the agents merge dynamically into coalitions according to the enabled/disabled communication links at each time instant. Therefore, with these schemes there is a reduction of the communication burden without compromising the system performance. In this tutorial, the main features of these schemes will be introduced in the framework of cooperative game theory, being the game related to the cost function that is optimized by the control approach, and with the players corresponding to either the communication links or the agents involved. In this context, several cooperative game theory tools will be considered in order to: rank the players, impose constraints on them, provide more effcient ways of calculation, perform system partitioning, etc., hence analyzing the main features related to each tool., [ES] El control coalicional es una rama incipiente del control distribuido donde los distintos agentes se agrupan de forma dinámica en coaliciones en función de los enlaces de comunicación activos/inactivos en cada instante de tiempo. Gracias a ello, se reduce la carga de comunicación sin comprometer las prestaciones del sistema. En este tutorial, se analizan las principales características de estos esquemas dentro del marco de la teoría de juegos cooperativos, estando el juego definido por la función de coste a optimizar en el esquema de control, y correspondiendo los jugadores bien a los enlaces de comunicación o bien a los propios agentes. En este contexto, se estudiarán diversas herramientas de teoría de juegos cooperativos, con objeto de clasificar jugadores, imponer restricciones en los mismos, proponer vías de cálculo más eficientes, realizar particionado de sistemas, etc., examinando las características más relevantes presentadas por cada herramienta.

[EN] Coalitional control is a fairly new branch of distributed control where the agents merge dynamically into coalitions according to the enabled/disabled communication links at each time instant. Therefore, with these schemes there is a reduction of the communication burden without compromising the system performance. In this tutorial, the main features of these schemes will be introduced in the framework of cooperative game theory, being the game related to the cost function that is optimized by the control approach, and with the players corresponding to either the communication links or the agents involved. In this context, several cooperative game theory tools will be considered in order to: rank the players, impose constraints on them, provide more effcient ways of calculation, perform system partitioning, etc., hence analyzing the main features related to each tool., [ES] El control coalicional es una rama incipiente del control distribuido donde los distintos agentes se agrupan de forma dinámica en coaliciones en función de los enlaces de comunicación activos/inactivos en cada instante de tiempo. Gracias a ello, se reduce la carga de comunicación sin comprometer las prestaciones del sistema. En este tutorial, se analizan las principales características de estos esquemas dentro del marco de la teoría de juegos cooperativos, estando el juego definido por la función de coste a optimizar en el esquema de control, y correspondiendo los jugadores bien a los enlaces de comunicación o bien a los propios agentes. En este contexto, se estudiarán diversas herramientas de teoría de juegos cooperativos, con objeto de clasificar jugadores, imponer restricciones en los mismos, proponer vías de cálculo más eficientes, realizar particionado de sistemas, etc., examinando las características más relevantes presentadas por cada herramienta.

[EN] Coalitional control is a fairly new branch of distributed control where the agents merge dynamically into coalitions according to the enabled/disabled communication links at each time instant. Therefore, with these schemes there is a reduction of the communication burden without compromising the system performance. In this tutorial, the main features of these schemes will be introduced in the framework of cooperative game theory, being the game related to the cost function that is optimized by the control approach, and with the players corresponding to either the communication links or the agents involved. In this context, several cooperative game theory tools will be considered in order to: rank the players, impose constraints on them, provide more effcient ways of calculation, perform system partitioning, etc., hence analyzing the main features related to each tool., [ES] El control coalicional es una rama incipiente del control distribuido donde los distintos agentes se agrupan de forma dinámica en coaliciones en función de los enlaces de comunicación activos/inactivos en cada instante de tiempo. Gracias a ello, se reduce la carga de comunicación sin comprometer las prestaciones del sistema. En este tutorial, se analizan las principales características de estos esquemas dentro del marco de la teoría de juegos cooperativos, estando el juego definido por la función de coste a optimizar en el esquema de control, y correspondiendo los jugadores bien a los enlaces de comunicación o bien a los propios agentes. En este contexto, se estudiarán diversas herramientas de teoría de juegos cooperativos, con objeto de clasificar jugadores, imponer restricciones en los mismos, proponer vías de cálculo más eficientes, realizar particionado de sistemas, etc., examinando las características más relevantes presentadas por cada herramienta.

[EN] Coalitional control is a fairly new branch of distributed control where the agents merge dynamically into coalitions according to the enabled/disabled communication links at each time instant. Therefore, with these schemes there is a reduction of the communication burden without compromising the system performance. In this tutorial, the main features of these schemes will be introduced in the framework of cooperative game theory, being the game related to the cost function that is optimized by the control approach, and with the players corresponding to either the communication links or the agents involved. In this context, several cooperative game theory tools will be considered in order to: rank the players, impose constraints on them, provide more effcient ways of calculation, perform system partitioning, etc., hence analyzing the main features related to each tool., [ES] El control coalicional es una rama incipiente del control distribuido donde los distintos agentes se agrupan de forma dinámica en coaliciones en función de los enlaces de comunicación activos/inactivos en cada instante de tiempo. Gracias a ello, se reduce la carga de comunicación sin comprometer las prestaciones del sistema. En este tutorial, se analizan las principales características de estos esquemas dentro del marco de la teoría de juegos cooperativos, estando el juego definido por la función de coste a optimizar en el esquema de control, y correspondiendo los jugadores bien a los enlaces de comunicación o bien a los propios agentes. En este contexto, se estudiarán diversas herramientas de teoría de juegos cooperativos, con objeto de clasificar jugadores, imponer restricciones en los mismos, proponer vías de cálculo más eficientes, realizar particionado de sistemas, etc., examinando las características más relevantes presentadas por cada herramienta.

[EN] Coalitional control is a fairly new branch of distributed control where the agents merge dynamically into coalitions according to the enabled/disabled communication links at each time instant. Therefore, with these schemes there is a reduction of the communication burden without compromising the system performance. In this tutorial, the main features of these schemes will be introduced in the framework of cooperative game theory, being the game related to the cost function that is optimized by the control approach, and with the players corresponding to either the communication links or the agents involved. In this context, several cooperative game theory tools will be considered in order to: rank the players, impose constraints on them, provide more effcient ways of calculation, perform system partitioning, etc., hence analyzing the main features related to each tool., [ES] El control coalicional es una rama incipiente del control distribuido donde los distintos agentes se agrupan de forma dinámica en coaliciones en función de los enlaces de comunicación activos/inactivos en cada instante de tiempo. Gracias a ello, se reduce la carga de comunicación sin comprometer las prestaciones del sistema. En este tutorial, se analizan las principales características de estos esquemas dentro del marco de la teoría de juegos cooperativos, estando el juego definido por la función de coste a optimizar en el esquema de control, y correspondiendo los jugadores bien a los enlaces de comunicación o bien a los propios agentes. En este contexto, se estudiarán diversas herramientas de teoría de juegos cooperativos, con objeto de clasificar jugadores, imponer restricciones en los mismos, proponer vías de cálculo más eficientes, realizar particionado de sistemas, etc., examinando las características más relevantes presentadas por cada herramienta.

This article addresses the multiagent systems consensus control problem via a learning observer-based performance tuning control policy. Specifically, a novel learning observer is presented to reconstruct the compound nonlinear terms and system states simultaneously. Based on the learning observer’s reconstructed information, a novel performance tuning control policy is proposed to deal with the internal nonlinear terms and external disturbances acting on the system while providing prescribed consensus performance. The proposed learning observer can guarantee the uniformly ultimately bounded estimation while saving computing resources, which is beneficial to the multiagent system. The proposed control policy, combined with observer and performance tuning, ensures the robustness to nonideal perturbations and the high accuracy control performance simultaneously. Mathematical simulations verify the effectiveness of the control algorithm. IEEE

This article addresses the multiagent systems consensus control problem via a learning observer-based performance tuning control policy. Specifically, a novel learning observer is presented to reconstruct the compound nonlinear terms and system states simultaneously. Based on the learning observer’s reconstructed information, a novel performance tuning control policy is proposed to deal with the internal nonlinear terms and external disturbances acting on the system while providing prescribed consensus performance. The proposed learning observer can guarantee the uniformly ultimately bounded estimation while saving computing resources, which is beneficial to the multiagent system. The proposed control policy, combined with observer and performance tuning, ensures the robustness to nonideal perturbations and the high accuracy control performance simultaneously. Mathematical simulations verify the effectiveness of the control algorithm. IEEE

This paper introduces a nonlinear observer to reconstruct faults in a class of nonlinear descriptor systems affected by disturbances, in both the state and output equations. The fault enters the system through nonlinear functions in both its state and output equations. The original system is first transformed such that design freedom in its structure is easier to exploit, and then a nonlinear observer is designed based on the transformed system to reconstruct the fault. Linear matrix inequalities are used to determine the gains of the observer such that the effect of the disturbances on the fault reconstruction is bounded. Finally, three simulation examples are carried out to verify the effectiveness of the scheme.

This paper studies the asymptotic behavior of switched linear systems, beyond classical stability. We focus on systems having a low-dimensional asymptotic behavior, that is, systems whose trajectories converge to a common time-varying low-dimensional subspace. We introduce the concept of path-complete $p$-dominance for switched linear systems, which generalizes the approach of quadratic Lyapunov theory by replacing the contracting ellipsoids by families of quadratic cones whose contraction properties are dictated by an automaton. We show that path-complete $p$-dominant switched linear systems are exactly the ones that have a $p$-dimensional asymptotic behavior. Then, we describe an algorithm for the computation of the cones involved in the property of $p$-dominance. This allows us to provide an algorithmic framework for the analysis of switched linear systems with a low-dimensional asymptotic behavior.

In this article, the power fluctuation problem caused by adding renewable energy sources to microgrids (MGs) is addressed. These power fluctuations which can cause several adverse effects, including system instability, load shedding, and eventually blackouts are attenuated by designing a load frequency controller (LFC) to provide a constant and uniform frequency in different operation cases of MGs. This article designs a dynamic output feedback controller (DOFC) for LFC in MGs while considering the contraction observer for attack mitigation. We suggest a new formulation of the sufficient conditions for the DOFC design problem in the form of bilinear matrix inequalities (BMIs) and try to minimize the frequency deviation subject to load, solar, and wind energy changes. The proposed formulation effectively changes the problem to a new BMI model which can easily be divided into two problems, one is a convex optimization problem and the remained part is a quadratic matrix inequality problem which is linearized around the feasible solution of the linear matrix inequality part by a Taylor-like expansion. This leads to a more exact and less conservative solution as compared to the previous methods suggested in the literature. To illustrate the merits of the developed approach, numerical simulations on a sample ac–MG are carried out. Comparing norms of the frequency variations in the closed-loop system with multiple prominent methods in previous works, including ${{\bm{H}}_\infty },{\bm{\ \mu }}$, fuzzy type-1, and intelligent-PI, shows that our proposed method reduces the two and infinity norms of frequency fluctuations by 84% and 80%, respectively compared to the traditional ${{\bm{H}}_\infty }$ method which is the best among the analyzed approaches. This further verifies the efficacy of the designed DOFC in our article., QC 20211221

Hybrid models of genetic regulatory networks allow for a simpler analysis with respect to fully detailed quantitative models, still maintaining the main dynamical features of interest. In this paper we consider a piecewise affine model of a genetic regulatory network, in which the parameters describing the production function are affected by polytopic uncertainties. In the first part of the paper, after recalling how the problem of finding a Lyapunov function is solved in the nominal case, we present the considered polytopic uncertain system and then, after describing how to deal with sliding mode solutions, we prove a result of existence of a parameter dependent Lyapunov function subject to the solution of a feasibility linear matrix inequalities problem. In the second part of the paper, based on the previously described Lyapunov function, we are able to determine a set of domains where the system is guaranteed to converge, with the exception of a zero measure set of times, independently from the uncertainty realization. Finally a three nodes network example shows the validity of the results., QC 20211206

This paper addresses a zonotopic fault detection (FD) observer for discrete-time descriptor systems subject to additive actuator faults. The considered descriptor systems are also perturbed by unknown-but-bounded uncertainties including state disturbances and measurement noise. Under a set-based framework, the effects of uncertainties and faults are separated into two state zonotopes. Based on this decomposition, the FD observer gain is designed to be robust against uncertainties and meanwhile sensitive to faults based on a (Formula presented.) index in a finite-frequency domain. Then, two linear matrix inequality (LMI) conditions are obtained to design an observer that achieves robustness and fault sensitivity at the same time. The generalised KYP lemma is applied to address the fault sensitivity condition. The optimal FD observer gain can be obtained via an offline design procedure. Finally, the proposed fault detection method is applied to a chemical mixing system and the effectiveness is shown.

[EN] This paper develops an innovative approach for designing non-parallel distributed fuzzy controllers for continuous-time non-linear systems under persistent perturbations. Non-linear systems are represented using Takagi-Sugeno fuzzy models. These non-PDC controllers guarantee bounded input bounded output stabilisation in closed-loop throughout the computation of generalised inescapable ellipsoids. These controllers are computed with linear matrix inequalities using fuzzy Lyapunov functions and integral delayed Lyapunov functions. LMI conditions developed in this paper provide non-PDC controllers with a minimum *-norm (upper bound of the 1-norm) for the T-S fuzzy system under persistent perturbations. The results presented in this paper can be classified into two categories: local methods based on fuzzy Lyapunov functions with guaranteed bounds on the first derivatives of membership functions and global methods based on integral-delayed Lyapunov functions which are independent of the first derivatives of membership functions. The benefits of the proposed results are shown through some illustrative examples.

In this paper a coalitional control and observation scheme is presented in which the coalitions are changed online by enabling and disabling communication links. Transitions between coalitions are made to best balance overall system performance and communication costs. Linear Matrix Inequalities are used to design the controller and observer, guaranteeing stability of the switching system. Simulation results for vehicle platoon control are presented to illustrate the proposed method.

[EN] This paper develops an innovative approach for designing non-parallel distributed fuzzy controllers for continuous-time non-linear systems under persistent perturbations. Non-linear systems are represented using Takagi-Sugeno fuzzy models. These non-PDC controllers guarantee bounded input bounded output stabilisation in closed-loop throughout the computation of generalised inescapable ellipsoids. These controllers are computed with linear matrix inequalities using fuzzy Lyapunov functions and integral delayed Lyapunov functions. LMI conditions developed in this paper provide non-PDC controllers with a minimum *-norm (upper bound of the 1-norm) for the T-S fuzzy system under persistent perturbations. The results presented in this paper can be classified into two categories: local methods based on fuzzy Lyapunov functions with guaranteed bounds on the first derivatives of membership functions and global methods based on integral-delayed Lyapunov functions which are independent of the first derivatives of membership functions. The benefits of the proposed results are shown through some illustrative examples.

In this paper a coalitional control and observation scheme is presented in which the coalitions are changed online by enabling and disabling communication links. Transitions between coalitions are made to best balance overall system performance and communication costs. Linear Matrix Inequalities are used to design the controller and observer, guaranteeing stability of the switching system. Simulation results for vehicle platoon control are presented to illustrate the proposed method.

In this paper a coalitional control and observation scheme is presented in which the coalitions are changed online by enabling and disabling communication links. Transitions between coalitions are made to best balance overall system performance and communication costs. Linear Matrix Inequalities are used to design the controller and observer, guaranteeing stability of the switching system. Simulation results for vehicle platoon control are presented to illustrate the proposed method., Team Riccardo Ferrari

In this paper a coalitional control and observation scheme is presented in which the coalitions are changed online by enabling and disabling communication links. Transitions between coalitions are made to best balance overall system performance and communication costs. Linear Matrix Inequalities are used to design the controller and observer, guaranteeing stability of the switching system. Simulation results for vehicle platoon control are presented to illustrate the proposed method.

In this letter, we study conditions for the existence of stabilizing solutions of two classes of modified discrete algebraic Riccati equations (MAREs) emerging in stochastic control problems. In order to do so, we first rewrite each MARE in terms of a standard ARE subject to specific constraints, which allows us to connect their solution with a set of linear-control constrained problems. With this result we also determine, for each MARE, a linear matrix inequality problem whose feasibility is guaranteed if and only if a stabilizing solution of the original MARE exists., QC20191205

This paper addresses a zonotopic fault detection (FD) observer for discrete-time descriptor systems subject to additive actuator faults. The considered descriptor systems are also perturbed by unknown-but-bounded uncertainties including state disturbances and measurement noise. Under a set-based framework, the effects of uncertainties and faults are separated into two state zonotopes. Based on this decomposition, the FD observer gain is designed to be robust against uncertainties and meanwhile sensitive to faults based on a H- index in a finite-frequency domain. Then, two linear matrix inequality (LMI) conditions are obtained to design an observer that achieves robustness and fault sensitivity at the same time. The generalised KYP lemma is applied to address the fault sensitivity condition. The optimal FD observer gain can be obtained via an offline design procedure. Finally, the proposed fault detection method is applied to a chemical mixing system and the effectiveness is shown., Peer Reviewed, Postprint (author's final draft)