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This paper presents a new method to calculate the inversion of the controlled linear/nonlinear dynamic plants which are described by input–output differential equation models. This new U-model-based inverter (U-inverter), cancels both system dynamics and nonlinearities, can be used directly to facilitate the control system design, which the design of U-model-based control (U-control) systems is selected for demonstration in this study. The most important advantage of this U-inverter is that it does not require system state variables, only uses the input/output measurements from the controlled plants. A nonlinear disturbance observer is introduced into the U-control design framework to increase its robustness. The analysis explains the properties of this developed the disturbance observer-based U-control (DOBUC) method and its design procedures. Finally, a wind energy conversion system is simulated to illustrate the design of this DOBUC and the corresponding performance.

This article presents a state-space model with time-delay to map the relationship between known input-output data for discrete systems. For the given input-output data, a model identification algorithm combining parameter estimation and state estimation is proposed in line with the causality constraints. Consequently, this article proposes a least squares parameter estimation algorithm, and analyzes its convergence for the studied systems to prove that the parameter estimation errors converge to zero under the persistent excitation conditions. In control system design, the U-model based control is introduced to provide a unilateral platform to improve the design efficiency and generality. A simulation portfolio from modeling to control is provided with computational experiments to validate the derived results.

This paper focuses on presenting a new identification algorithm to estimate the parameters and state variables for two-input two-output dynamic systems with time delay based on canonical state space models. First, the related input-output equation is determined and transformed into an identification oriented model, which does not involve in the unmeasurable states, and then a residual based least squares identification algorithm is presented for the estimations. After the parameters being estimated, the system states are subsequently estimated by using the estimated parameters. Through theoretical analysis, the convergence of the algorithm is derived to provide assurance for applicability. Finally, a selected simulation example is given for a meaningful case study to show the effectiveness of the proposed algorithm.

This study presents, based on bias compensation, an integrated parameter and state estimation algorithm for a class of time-delay non-linear systems which are described by canonical observable state-space model. In technical development, the state-space system model is transformed into an input-output representation/realisation by eliminating the state variables, which is accordingly used as a feasible identification model. With such an input-output structure, directly data measurable to accommodate the estimation bias, an augmented least-squares algorithm (by adding the bias correction terms into the estimates) is proposed for estimating the parameters and states interactively. Regarding the estimator properties, the proposed algorithm is proved unbiased. The simulation results show that the proposed algorithm has good performance in estimating the parameters of state-space systems.

Amine absorption method which is used for removing CO 2 from coal-fired power plant flue gas has the high energy consumption and degradation rate, strong corrosion. In view of these defects and flue gas having low CO 2 partial pressure, a new organic amine absorbent whose saturated absorption capacity can reach to 47.447.4L CO2 /L amine has been developed. Compared with MEA solution, its saturated absorption capacity has been increased by 29.1%, and the regeneration rate increased by more than 80%. The CO 2 capture process flow has been analyzed, and ‘absorption heat pump +MVR heat pump’ double heat pump coupled low energy consumption CO 2 capture system has been developed. Compared with traditional MEA technology, this system can save 38.32% energy, and its water saving rate can reach to 63%.

International audience; For more than half a century, considerable efforts have been made in thefield of system control and monitoring. These actions are, obviously, crucialin the presence of technological risks directly impacting human health andthe environment. Techniques to be developed aim, in general, to betterunderstand, at each moment, the state of a system. The estimation phaseis, of course, insufficient, and should be completed by state analysis toevaluate its normal or abnormal character. In the latter case, the analysis isfurther refined to accurately localize where the anomaly is to be found, tospecify which part of the system, sensor, or actuator, is faulty. In order tojudge the importance of the anomaly, its magnitude should be estimated.The ultimate phase of diagnosis seeks to specify the cause of the anomaly.In some cases, although this remains marginal because of great difficultyat the moment, the future evolution estimation of the anomaly is made.All these steps can contribute to considering how to react to anomalies inorder to reduce their effects by means of appropriate control laws.This book does not attempt to address all these problems, but it canbe a good introduction to some of the techniques designed to estimatethe system states in different situations. In particular, the design of anobserver to reconstruct the system states from partial measurements, howto use a state observer to detect and locate anomalies, and how to adjust acontrol law to counter the effect of anomalies on the behavior of a systemwill be discussed. In addition, a number of difficulties resulting fromrealistic physical constraints are considered: the presence of uncertaintiesand delays in system models; the influence of unmeasured exogenousinputs on system dynamics; the nonlinear behavior of systems; switchingsystems, or systems with several operating modes and interconnectedsystems. This diversity of objectives, systems, and constraints is coveredin complementary chapters, and addresses a large part of the problem ofobserver state estimation and its applications.This book is a timely and comprehensive reference guide for graduatestudents, researchers, engineers, and practitioners in the areas of controltheory. The content has been written for investigators acting in the fieldsof electrical, mechanical, aerospace, or mechatronics engineering. Withcontributions by eminent scientists in the field of control theory andsystems engineering from22 countries, this book covers the latest advancesin observer-based control, from new design approaches to control engineeringapplications. Readers will find the fundamentals and applicationsrelated to this topical issue. The book contains examples that make it idealfor advanced courses, as well as for researchers starting to work in the field,or engineers wishing to enter the field quickly and efficiently.The authors of the various chapters have tried to clearly present thetheoretical concepts underlying the proposed solutions, and to illustratethem with pedagogical examples of modest dimensions; but allowing usto clearly see the implementation of these solutions and to assess theirrelevance through numerical data. In some cases, short Matlab programscomplete the formulation. The book is structured in 13 chapters, and theorganization is given as follows.Chapter 1 is dedicated to the class of descriptor systems. After somereminders on proportional and proportional-integral observers, theauthors propose a new dynamical observer called “general observerstructure,” and its extension to the case of systems with disturbing input.The stability analysis of the observer is proved via a Lyapunov methodand solved via a set of linear matrix inequalities (LMIs). Several academicexamples illustrate the performance of the proposed structure.Chapter 2 proposes an observer design technique for nonlinear interconnectedsystems with uncertain variable parameters. The observer hasadaptive parameters that are adjusted from a stability study of the reconstructionerror. The two examples that are given, coupled reverse pendulumsand a quarter vehicle system, illustrate the implementation.The case of a linear switching system is discussed in Chapter 3, incorporatingtwo difficulties: the presence of unknown inputs, and a lackof knowledge of the switching law. The observer is then designed toestimate the continuous and discrete states of the system. The proposedtechnique is applied to a modulation/demodulation procedure in a securecommunication system with chaotic behavior.Another interesting situation is the subject of Chapter 4: state estimationfor linear systems with unknown inputs and delays affecting their statesand inputs. A first method proposes the design of a delay-dependentunknown input observer (UIO), whereas a second one suggests the designof a delay-independent UIO. The numerical example of the quadruple-tankbenchmark is used to illustrate the efficiency of the two proposedmethodsfor the case study.Chapter 5 presents the basics, progress, and outlook for the observerbasedcontrol design problem in dynamical systems. After reviewing theroots and needs of the problem, the authors have provided completeanalytical results pertaining to dynamic modeling, control design, andcomputer simulation of several distinct approaches. The authors have alsoinvestigated issues regarding robust stability and robust performance ofcontrol design for different system configurations.In Chapter 6, the authors have developed new sufficient LMI conditionsfor the problem of stabilization of discrete-time uncertain switched linearsystems under arbitrary switching rules. Different scenarios of the use ofFinsler’s lemma are proposed to reduce the conservatism of existing resultsin the literature. Numerical examples and simulation results are presentedto demonstrate the effectiveness of the proposed methods.Model predictive control (MPC) based on state observers for nonlinearmultivariable systems is the subject of Chapter 7. To overcome classical limits,the authors developed an adaptive MPC-based observer for nonlinearmultivariable systems. The implementation of the proposed approach toa three-tank benchmark system is performed, with a comparison betweenlinear and nonlinear predictive controllers.The authors of Chapter 8 propose a new decentralized observer-basedcontroller design method for nonlinear discrete-time interconnected systemswith nonlinear interconnections. An enhanced linear matrix inequalitydesign condition is provided to guarantee asymptotic stability forsystems with both known and unknown interconnection bounds. Twonumerical examples illustrate the effectiveness of the design approach.Chapter 9 presents results of the polytopic model (PM) approach tocopewith the modeling, stability analysis, state feedback control, state, andunknown input estimation, and finally, fault-tolerant control of nonlinearsystems. The backbone of all presented results is the capacity of the PMstructure to represent nonlinearities in a selected operating range of thesystem. It is proposed to design a fault-tolerant controller fed with thesimultaneous state and unknown input estimates. The benefits of the activefault-tolerant controller based on the PM approach are illustrated in anexample consisting of the stabilization of the lateral dynamics of a vehicle.Chapter 10 studies the application of high-order sliding mode observersfor the estimation of faults and their later compensation in linear systems.The study is restricted to the systems with strongly observable faults. Themain idea is to exploit the finite-time convergence of the high-order slidingmode-based observers to estimate the states, and also the dynamic effects,of the faults, showing that these are powerful tools not only to estimatestates, but also unknown signals. The methodology is illustrated with thedesign of a fault-tolerant control of the roll autopilot for a missile mode.The authors of Chapter 11 consider the problem of simultaneous stateand fault estimation of linear descriptor and nonlinear descriptor discretetimestochastic systems with arbitrary unknown disturbances. The studyis based on input filtering and the use of a robust two-stage Kalman filter.Chapter 12 investigates the problem of the simultaneous estimation ofdiscrete state, continuous state, and faults of a class of switched linearsystems with measurement noise. Anew algebraic approach is developedin order to estimate, in real time, and with a negligible delay, the switchingtimes, and to reconstruct the discrete state. The proposed strategy isillustrated by a system with three operating modes whose dynamics areaffected by two faults.Chapter 13 introduces an appropriate model associating paradigmsfrom control theory and computer science to deal with the system subjectto both physical attacks and sensor/actuator attacks via the connectednetwork. Inspired by a combination of the classical fault-tolerant controlapproach and the event-triggered control, an observer-based, attacktolerantcontrol solution is proposed. The control design is applied to alaboratory benchmark including a three-tank system subject to physicalattacks.Finally, on behalf of all the editors, I would like to express my gratefulnessto all the authors of the book for their valuable contributions, andall reviewers for their helpful and professional efforts to provide valuablecomments and feedback.

© The Institution of Engineering and Technology 2018. In dual-rate rational systems, some output data are missing (unmeasurable) to make the traditional recursive least squares (RLS) parameter estimation algorithms invalid. In order to overcome this difficulty, this study develops a bias compensation RLS algorithm for estimating the missing outputs and then the model parameters. The algorithm based on auxiliary model and particle filter has four steps: (i) to establish an auxiliary model to estimate unmeasurable outputs, (ii) to compensate bias induced by correlated noise, (iii) to add a filter to improve estimation accuracy of the unmeasurable outputs and (iv) to obtain an unbiased parameter estimation. Three examples are selected for simulation demonstrations to give further guarantees on the usefulness of the proposed algorithms. The comparative studies show that the bias compensation RLS is more effective for such systems with dual-rate input and output data.

In this study, U-model based control system design methodology is expanded into control of nonminimum-phase (NMP) dynamic systems. With U-model framework, a desired minimum-phase (MP) plant is established, which forms as a generic prototype. To achieve the same output response as the desired MP plant, the NMP zeros of plant are removed (NB not cancelled) by feedforward controllers, using the linear polynomial provided by U-model approach. While the NMP zeros are removed, the rest of the design procedure gets back to the standard U-model control routines. The study presents the details of analytical development and computational experiment demonstrations. In addition, the study provides benchmark examples for users with their ad hoc control of NMP systems.

This paper shows how a self-tuning PID controller to control the temperature for the continuously stirred tank reactor (CSTR) and behaviour in a desired manner. The steps are modelling, simulation and analysis of difference of controller parameter in order to identify the ideal of the controller parameters. In addition, this project will on basis of a practical example tries to use a modelling of CSTR for control input and the temperature output will have a feedback loop to pass though the PID controller, and the PID controller will auto adjust the heating temperature. All the process will be built in Simulink.

In this paper, an adaptive sliding mode control is proposed for position tracking of rigid robotic manipulators with system parametric uncertainties. This control scheme can drive the position tracking error reach the designed sliding mode and then converge to zero asymptotically while avoiding the chattering phenomenon. Compared with the robust full-order sliding mode control technique, the proposed scheme can overcome the system uncertainties without requiring prior knowledge of their bounds. Theoretical analysis and simulation results validate the effectiveness of the proposed scheme.

In this study, a basic idea of U-state feedback control system design method is proposed based on U-model principle. Compared with U-model based nonlinear polynomial systems, the U-state space design is to determine the desired state variable equation x d (t), therefore to find the controller output u(t−1). The desired state equation can be designed/specified and transformed into the U-state space expression easily. The controller output can be obtained from resolving the equation. The desired state variables are updated by the corresponding state variables x(t). Assume that the state variables are measurable or obtained by a proper observer in this study. In case studies, two nonlinear discrete time state space models are selected to test the proposed approach. Computational experiments, that is, simulation studies, are used to demonstrate the procedure effectiveness.

This paper adopts a Hidden Markov Model as a basis for predicting the probabilities in location of soccer robot's trajectories, develops the corresponding algorithms, and then demonstrates the simplicity of the procedure with simulations. The purpose of the initial presentation is to establish a proper platform for the future comprehensive studies of using Hidden Markov Models to assemble critical observations with uncertainties or random measurement errors in stochastic system modelling and control.

The proposed paper looks into the modelling and control of a depyrogenation tunnel. Depyrogenation tunnels are used in pharmaceutical filling lines for removing pyrogens attached to containers (sterilization of glass containers), as well as, for drying purposes. Due to the nonlinear, multi inputs multi outputs nature of the system, a multiple inputs, multiple outputs (MIMO) state-space based modelling and control has been adopted. In this initial research stage and in order to validate both the model and the control scheme, computational experiments based on simulation have been carried out. This will ultimately help setup a proper platform for further developments. The initial results, as shown across the paper, are very promising.

In the first stage studies, this paper proposes a new piecewise linear discrete time dynamic model for describing global urine flow rate profiles against time sequence. The establishment of model has been guided by physician meaningful principles and measured data. To obtain the model parameters from measured data sequence, a number of existing approaches have been tailored/integrated including least squares algorithm for model time constant and gain estimation, Butterworth filter with specified cut off frequencies, for reducing noise effect induced by abdominal and bladder squeezing, then to facilitate counting the number of peaks. A few of real case studies are selected to demonstrate the effectiveness and efficiency of the proposed procedure.

International audience; Abstract: In this paper, sliding mode observers are designed in order to estimate actuator and sensor faults for linear fractional-order systems. The system is represented by a commensurate fractional-order state space model, in the presence of unknown sensor and actuator faults. The convergence of the designed observers is established. A numerical example is given to illustrate the effectiveness of the proposed approach.

This contribution deals with an adaptive sliding mode control for a hybrid actuator consisting of a piezo, a mechanicacal and a hydraulic part that can be used for camless engine motor applications. The control structure comprises a feedforward controller and a sliding mode controller. The general approach of this actuator is to use the advantages of both systems, the high precision of the piezoelectric actuator and the force of the hydraulic part. In fact, piezoelectric actuators (PEAs) are commonly used for precise positioning, despite PEAs present nonlinearities, such as hysteresis, saturations, and creep. A sliding mode control is proposed and for deriving the structure of such a controller a Lyapunov approach is used. An adaptive self-tuning algorithm is realised. The conceived sliding mode control takes the hydraulic actuator in a resonance operating point which corresponds to the rotational speed of the engine. When the engine speed changes, the sliding mode controller adapts its parameter in away that the resonance frequency of the controlled hydraulic part of the actuator changes and corresponds to the working frequency of the engine. The resulting controller is therefor totally self-tuning and robust with respect to the model parameter variation. Asymptotic tracking is shown using Lyapunov approach. Moreover, the proposed technique avoids a switching function for the calculation of the equivalent signal of the sliding mode controller. In this way the chattering problem is completely avoided. Simulations with real data of a camless engine are presented.