Input Delay Estimation for Input-Affine Dynamical Systems Based on Taylor Expansion

In this brief, we propose a novel method based on the Taylor expansion for the estimation of input delay for a class of input-affine dynamical systems. The proposed method guarantees the asymptotic convergence of the estimation error to zero. An application to the input delay estimation of a continuous stirred tank reactor system shows the effectiveness of the proposed method

Robust stabilization of 2×22 \times 2 first-order hyperbolic PDEs with uncertain input delay

A backstepping-based compensator design is developed for a system of $2\times2$ first-order linear hyperbolic partial differential equations (PDE) in the presence of an uncertain long input delay at boundary. We introduce a transport PDE to represent the delayed input, which leads to three coupled first-order hyperbolic PDEs. A novel backstepping transformation, composed of two Volterra transformations and an affine Volterra transformation, is introduced for the predictive control design. The resulting kernel equations from the affine Volterra transformation are two coupled first-order PDEs and each with two boundary conditions, which brings challenges to the well-posedness analysis. We solve the challenge by using the method of characteristics and the successive approximation. To analyze the sensitivity of the closed-loop system to uncertain input delay, we introduce a neutral system which captures the control effect resulted from the delay uncertainty. It is proved that the proposed control is robust to small delay variations. Numerical examples illustrate the performance of the proposed compensator

Prediction-Based Control of Linear Systems by Compensating Input-Dependent Input Delay of Integral-Type

International audienceThis study addresses the problem of delay compensation via a predictor-based output feedback for a class of linear systems subject to input delay which itself depends on the input. The equation defining the delay is implicit and involves past values of the input through an integral relation, the kernel of which is a polynomial function of the input. This modeling represents systems where transport phenomena take place at the inlet of a system involving a nonlinearity, which frequently occurs in the processing industry. The conditions of asymptotic stabilization require the magnitude of the feedback gain to comply with the initial conditions. Arguments for the proof of this novel result include general Halanay inequalities for delay differential equations and take advantage of recent advances in backstepping techniques for uncertain or varying delay systems

Implicit integral equations for modeling systems with a transport delay

International audienceIn this chapter, we present a particular class of transport delay systems (e.g. systems involving transportation of material), in which the delay is definedthrough an implicit integral equation. To illustrate the practical interest of this class, experimental use of such models is presented for two different examples of physical systems, both from the field of automotive gasoline engines (specifically, exhaust gas recirculation and exhaust catalyst thermal dynamics). We also discuss related control challenges, together with some solution

Adaptive Finite-time Fuzzy Control of Nonlinear Active Suspension Systems With Input Delay

This paper presents a new adaptive fuzzy control scheme for active suspension systems subject to control input time delay and unknown nonlinear dynamics. First, a predictor based compensation scheme is constructed to address the effect of input delay in the closed-loop system. Then, a fuzzy logic system (FLS) is employed as the function approximator to address the unknown nonlinearities. Finally, to enhance the transient suspension response, a novel parameter estimation error based finite-time (FT) adaptive algorithm is developed to online update the unknown FLS weights, which differs from traditional estimation methods, e.g. gradient algorithm with e-modification or σ-modification. In this framework, both the suspension and estimation errors can achieve convergence in finite-time. A Lyapunov-Krasovskii functional is constructed to prove the closed-loop system stability. Comparative simulation results based on a dynamic simulator built in a professional vehicle simulation software, Carsim, are provided to demonstrate the validity of the proposed control approach, and show its effectiveness to operate active suspension systems safely and reliably in various road conditions

Compensation of Time-Varying Delay in Networked Control System over Wi-Fi Network

In this study, we design a state predictor-based output feedback controller that compensates for unavoidable time-varying network delays in networked control systems (NCSs) over Wi-Fi networks. We model time-varying network delays as timevarying input delays of NCSs over Wi-Fi networks. The designed controller consists of a linear quadratic regulator (LQR), a full-order observer, and a time-varying stepahead state predictor. The state predictor plays a key role in compensating for the time-varying input delay by providing the LQR with an estimation of future states ahead by the current network delay time. The time-varying network delays are acquired in real time by measuring the time differences between sent and received control data packets. We verify the stability and compensation performance of the designed controller by performing extensive experiments for an NCS in which a rotary inverted pendulum is controlled over Wi-Fi networks

Cooperative intersection control for autonomous vehicles

Self-driving cars crossing road intersection

Novel Strategies to design Controllers and State Predictors based on Disturbance Observers

[ES] Los sistemas de ingeniería o físicos suelen ser inciertos. Su incertidumbre se manifiesta cuando el sistema muestra comportamientos que son relativamente diferentes a los que su modelo predice; estando principalmente causada por: errores de modelado; dinámicas desconocidas; cambios en las propiedades del sistema; interacciones aleatorias con otros sistemas; o cambios en las condiciones de operación. Durante los últimos 40 años, se ha demostrado reiteradamente que las incertidumbres de los sistemas pueden tener efectos muy negativos sobre el comportamiento de un controlador si éstas no se consideran adecuadamente sus formulaciones matemáticas. Por esta razón, una parte importante de la investigación actual está centrada en este tema; buscando las formas mas adecuadas para representar matemáticamente las incertidumbres de los sistemas, así como buscando nuevas herramientas matemáticas que permitan hacer uso de ésta representación de la incertidumbre con el objetivo de diseñar algoritmos de control robustos. En esta tesis se presentan nuevas aportaciones en esta línea. Concretamente, se desarrollan nuevas metodologías para diseñar controladores (DOBCs) y predictores (DOBPs) para sistemas dinámicos inciertos basados en observadores de perturbaciones. La principal aportación es demostrar que los DOBCs se pueden sintetizar desde un enfoque de control óptimo; siendo su principal criterio de diseño el de aproximar la -irrealizable- señal de control óptima que minimiza un índice de coste cuadrático sujeto a un modelo dinámico lineal (LTI). Este nuevo enfoque de diseño es indistintamente válido para modelos SISO/MIMO con múltiples o únicas perturbaciones. Además permite un ajuste del controlador muy intuitivo gracias a las matrices de ponderación del coste. De forma similar; los DOBPs se construyen con el objetivo de aproximar la solución temporal un sistema dinámico perturbado. Con el objetivo de contextualizar la aportación, el documento también incluye un breve resumen de los principales métodos de control robusto y el impacto que han tenido en la revolución tecnológica del siglo XXI; algunas discusiones sobre la utilidad de los modelos LTI perturbados para representar sistemas dinámicos inciertos; y algunas relaciones, comparaciones y simulaciones numéricas de los métodos propuestos con otras técnicas de control.[CA] Els sistemes d'enginyeria o físics solen ser incerts. La seua incertesa es manifesta quan el sistema mostra comportaments que són relativament diferents als que el seu model prediu; sent principalment causada per: errors de modelatge; dinàmiques desconegudes; canvis en les propietats del sistema; interaccions aleatòries amb altres sistemes; o canvis en les condicions d'operació. Durant els últims 40 anys, s'ha demostrat reiteradament que les incerteses dels sistemes poden tindre efectes molt negatius sobre el comportament d'un controlador si aquestes no es consideren adequadament les seues formulacions matemàtiques. Per aquesta raó, una part important de la investigació actual està centrada en aquest tema; buscant les formes mes adequades per a representar matemàticament les incerteses dels sistemes, així com buscant noves tècniques matemàtiques que permeten fer ús d'aquesta representació de la incertesa amb l'objectiu de dissenyar algorismes de control robustos. En aquesta tesi es presenten noves aportacions en aquesta línia. Concretament, es desenvolupen noves metodologies per a dissenyar controladors (DOBCs) i predictors (DOBPs) per a sistemes dinàmics incerts basats en observadors de pertorbacions. La principal aportació és demostrar que els DOBCs es poden sintetitzar des d'un punt de vista de control òptim; sent el seu principal criteri de disseny el d'aproximar la -irrealitzable- senyal de control òptima que minimitza un índex de cost quadràtic restringit a un model dinàmic lineal (LTI). Aquest nou plantejament és indistintament vàlid per a models SISO/MIMO amb múltiples o úniques pertorbacions. A més permet un ajust del controlador molt intuïtiu gràcies a les matrius de ponderació del cost. De manera similar; els DOBPs es construeixen amb l'objectiu d'aproximar la solució temporal un sistema dinàmic pertorbat. Amb l'objectiu de contextualitzar l'aportació, el document també inclou un breu resum dels principals mètodes de control robust i l'impacte que han tingut en la revolució tecnològica del segle XXI; algunes discussions sobre la utilitat dels models LTI pertorbats per a representar sistemes dinàmics incerts; i algunes relacions, comparacions i simulacions numèriques dels mètodes proposats amb altres tècniques de control.[EN] Engineering or physical systems are used to be uncertain. Its uncertainty is manifested whenever the system shows behaviors that are relatively different than the ones predicted by its model; being mostly caused by: modeling errors; unknown dynamics; changes in the system properties; random interactions with other systems; or changes in the operating conditions. Through the last 40 years, it has been persistently proved that the system uncertainties could have very negative effects in the performance of a feedback regulator if they are not properly considered in the mathematical formulations of the employed algorithms. Thus, an important part of the recent research is focused on this topic; searching for the most appropriate ways to mathematically represent the system uncertainties and looking for new mathematical-tools that permit to make use of such uncertainty-representation in order to design robust control algorithms. In this thesis, new contributions in this line are provided. Concretely, novel methodologies to design Disturbance Observer-Based Controllers (DOBCs) and Predictors (DOBPs) for uncertain dynamic systems are developed. The main contribution is to show that the DOBCs can be constructed from an optimality-based approach, with the main objective of approximating the -unrealizable- optimal control signal that minimizes a quadratic-cost performance index subject to a LTI disturbed model constraint. This novel robust control design is indistinctly valid for SISO/MIMO models with single/multiple matched/mismatched disturbances; offering also a highly intuitive and versatile tuning through the weighting matrices. Similarly, the DOBPs are synthesized in order to approximate the time-domain solution of LTI disturbed models. For the sake of completeness, the document also includes a brief review of the main robust control methods and the impact that they have had on the technological revolution of the 21st century; some discussions about the usefulness of the LTI disturbed models for representing uncertain dynamic systems; and different relationships, comparisons and numerical simulations, of the proposed methods with other control approaches.Castillo Frasquet, A. (2021). Novel Strategies to design Controllers and State Predictors based on Disturbance Observers [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/165034TESI

A Comprehensive Review of the State-of-the-Art of Secondary Control Strategies for Microgrids

The proliferation of distributed energy resources in distribution systems has given rise to a new concept known as Microgrids (MGs). The effective control of MGs is a crucial aspect that needs to be prioritized before undertaking any implementation procedure. This article provides a comprehensive overview of hierarchical control methods that ensure efficient and robust control for MGs. Specifically, it focuses on the secondary controller approaches (centralized, distributed, and decentralized control) and examines their primary strengths and weaknesses. The techniques are thoroughly discussed, deliberated, and compared to facilitate a better understanding. According to functionality, the hierarchical-based control scheme is allocated into three levels: primary, secondary, and tertiary. For secondary control level, the MG communication structures permit the usage of various control methods that provided the significance of the secondary controller for consistent and reliable MG performance and the deficiency of an inclusive recommendation for scholars. Also, it gives a review of the literature on present important matters related to MG secondary control approaches in relation to the challenges of communication systems. The problem of the secondary level control is deliberated with an emphasis on challenges like delays. Further, at the secondary layer, the distributed control techniques for reducing communication system utilization and then reducing communication system delays are conferred. Furthermore, the benefits and limitations of various control structures, such as centralized, decentralized, and distributed are also discusses in this study. Later a comparative analysis of entire control approaches, the best methods of control according to the author's perspective are also discussed

Adaptive neural control of a class of uncertain state and input-delayed systems with input magnitude and rate constraints

This article aims at proposing an adaptive neural control strategy for a class of nonlinear time-delay systems with input delays and unknown control directions. Different from previous researches that investigated delays and constraints separately, the novelty of this article lies in that it simultaneously considers delays (state and input delays) and input constraints (magnitude and rate constraints) for a class of uncertain nonlinear systems. In this article, the uncertain states and input delays are handled by integrating a constructed auxiliary system that functions as an observer with neural networks (NNs), with which the adverse effects caused by the uncertain states and input delays can be approximated and compensated. By involving smooth hyperbolic tangent functions in the designed auxiliary system, the problem of magnitude and rate constraints of the control input is fully addressed. Then, the backstepping technique runs through the entire control designing process, which allows the designed adaptive neural control strategy to handle the input constraints and delays at the same time. Furthermore, Nussbaum functions are employed to resolve the problem of unknown control directions. Due to the introduction of an input-driven filter, only the output of the system is required to be measured as the control feedback, which promotes the applicability of the designed controller. Under the proposed control scheme, semiglobal, uniform, and ultimate boundedness of all signals of the closed-loop system is realized with uncertain control directions, input and state delays, and guaranteed magnitude and rate constraints of control inputs. Finally, simulation results are illustrated to verify the effectiveness of the presented control method