We discuss the positivity and stability of an SEIADR (susceptible-exposed-symptomatic infectious-asymptomatic infectious-dead lying bodies-recovered by immunity) Ebola-type epidemic model and study different uses of a supervisory cost function being applied to a parallel multi-control strategy. Two supervisory strategies are proposed for on-line adjustment of the control gains of antiviral treatment and vaccination action. The cost functions eventually include weighted infection and vaccination treatment penalties and have a high definition freedom for the designer. Some simulation examples are developed in order to compare the efficiency against the use of unsupervised controls.

A vaccination strategy based on the state feedback control theory is proposed. The objective is to fight against the propagation of an infectious disease within a host population. Such a propagation is modelled by means of a SISV (susceptible-infectious-susceptible-vaccinated) epidemic model with a time varying whole population and with a mortality directly associated with the disease. This model contains some free-design parameters, namely, the feedback gains of the vaccination control law. The paper analyses the positivity of such a model under the proposed vaccination strategy as well as the conditions for the existence of the equilibrium points of its normalized model. In this context, it is proved that an appropriate adjustment of the control gains avoids the existence of endemic equilibrium points in the normalized SISV model while guaranteeing the existence of a unique disease-free equilibrium point being globally asymptotically stable.

This paper investigates the error between the approximate and exact solutions of a class of nonlinear differential equations which possess unique solution on a certain interval for any admissible initial conditions. The differential equations are assumed to be approximated by well-posed truncated Taylor series up to a certain order obtained about certain, in general non-periodic, sampling points tiϵ [t0, tJ] for i = 0,1,..., J of the solution. Two simulation examples are also provided. The first one is concerned with a predefined periodic sampling law while the second one generates new sampling points according to a constant amplitude difference sampling criterion.

This paper deals with iterative learning control (ILC) systems subject to the presence of dropouts. The system is composed by a set of discrete-time multiple input-multiple output (MIMO) linear models, each one with its corresponding actuator and its sensor. The ILC law is processed in a control device sited far away of the iterative models so the control signals have to be transmitted from the controller to the actuators. Moreover, the ILC law uses the measurements of each iterative model to generate the inputs vector to be applied to its subsequent iterative model so the measurements of the iterative models have to be transmitted from the sensors to the controller. An unknown set of such transmissions is susceptible to suffer failures at any time instant. A compensation dropout technique is used to replace the data which have been lost in the transmission processes with the aim of guaranteeing the convergence of the output errors.

This paper presents the design of a state-observer for a discrete-time SEIR epidemic model. The model is obtained by discretizing a continuous-time one by using the Micken's non-standard method. This procedure provides an implicit system of difference equations. The observer is then designed in an implicit way instead of obtaining first an explicit model of the system. Moreover, some sufficient conditions to ensure the asymptotic stability of the observer are provided in terms of a matrix inequality that can be cast into a LMI. The feasibility of the matrix inequality is stated while some simulation examples show the operation and usefulness of the observer.

This paper presents a novel control architecture system which is composed of a multi-objective cost function which Pareto optimizes the programming of cutting parameters while controlling the milling process to new cutting conditions if new constraints appear. The paper combines a self-optimized module which looks for and finds Pareto optimal cutting parameters according to multi-objective purposes and, a multi-model control module which keeps the cutting forces under prescribed upper safety limits independently of programmed cutting conditions and material properties while maintaining the performance of the process. An intelligent algorithm acts as decision support-software to automatically switch to the best performance tracking controller among those available at each required time.

The objective of this paper is to present an open and modular expert rule-based system in order to automatically select cutting parameters in milling operations. The knowledge base of the system presents considerations of stability, machine drives efficiency and restrictions while adaptively controlling milling forces in suitable working points. Moreover, a novel classical cost function has been conceived and constructed to Pareto-optimise cutting parameters subjected to multi-objective purposes, namely: tool-life, surface roughness, material remove rate and stability rate parameter. Different Pareto optimal front solutions can be obtained modulating the weighting factors of the cost function. Additional rules have been added in order to manually and/or automatically modulate this cost function. Furthermore, a database which relates weighting factors, cutting conditions and cost function variables is produced for learning purposes. Chatter detection and suppression system automatically feedback to the system to take into account non-modelled disturbances. Finally, since the knowledge of the system is basically obtained from mathematical models, the possibility of combining experience and knowledge from expert engineers and operators is included. In this way, best practice from mathematical modelling and expert engineers and operators is joined in one system obtaining a full, automated system combining the best of each world. As a result, the expert rule-based system selects Pareto optimal cutting conditions for a broad range of milling processes, sorting out automatically different problems such as chatter vibrations, incorporating model reference adaptive control (MRAC) of forces. This procedure is intuitive, being executed in the same way as a human expert would do and it provides the possibility to interact with expert engineers and operators in order to take into account their experience and knowledge. Finally, the expert system is designed in modular form allowing incorporating new functionalities in rule based forms to them or just adding new modules to improve the performance of the milling system.

The control of plasma in nuclear fusion has been revealed as a promising application of Control Engineering, with increasing interest in the control community during last years. In this paper it is developed a control-oriented linear model for the control of plasma current. For this purpose, it is provided a summary of the background necessary to deal with control problems in tokamak-based nuclear fusion reactors as it is the case of the future ITER tokamak. Besides, it is also given a review of the most used simulators and plasma models, with the aim of providing an adequate background for control engineers to derive their own control-oriented model or to choose the appropriate existing one. Finally, the proposed plasma model performance is proven in a current drive profile trajectory tracking problem using a modified anti-windup PID-based control scheme.

This paper is devoted to discuss the role of the minimum residence time in the stabilization of dynamic discrete switched systems. It is found that it can be obviated if the sampling period is sufficiently large even without imposing the requirement in the continuous-time case that the various switched parameterizations (referred to often as “ configurations”) possess a common Lyapunov function. However, it is claimed that the same role related to needing a sufficiently large minimum residence time at each stable parameterization in the continuous-time case is now played by the need of a sufficiently large minimum stabilizing sampling period. This requirement is removed if the various parameterizations possess a common Lyapunov function.

In this paper an optimization procedure has been developed in order to select mill cutter and cutting parameters. For the purpose a cost function, which compares system performance, is contrived. An example is presented to illustrate the method.

A multiestimation scheme for adaptive discrete-time control is presented. The different models of the scheme to be estimated are obtained from a set of different discretizations of a continuous-time unknown transfer function under a fractional order hold of correcting gain (β-FROH) which leads to distinct associate discrete-time models for the given continuous-time plant. The objective is to design a scheme being able to find the most appropriate value for the gain β to enhance the identification and tracking performances. The scheme chooses online the value of β with the best reference tracking performance by implementing eventually switches through time among a prefixed set of values characterizing the discrete models of the plant in a parallel multiestimation scheme. The switching rule is subjected to a minimum residence time in order to guarantee the closed loop stability. Simulations for two different practical cases, a controlled DC motor and a resonant circuit, are presented.

This paper introduces a novel model-reference discrete-time control scheme applied to milling processes. The novelty of the scheme relies on the use of a fractional order hold (FROH) instead of a traditional ZOH used in the manufacturing literature to obtain a discrete-time model of the continuous system. The additional degree of freedom introduced by the FROH through its correcting gain allows the designer to improve the transient response of the closed-loop system by an adequate choice of its value. Simulation examples showing the influence of the correcting gain in the closed-loop response are presented and compared. The control scheme is applied in this paper to known piecewise-constant plants being subsequently extended to unknown ones through adaptive control.

In this paper, a novel discrete-time model-reference based control of the tool-work-piece interaction force in a milling process has been designed. The novelty of the scheme relies on the use of a fractional order hold (FROH) instead of a ZOH to obtain the discrete model of the plant. The additional degree of freedom provided by the FROH allows improve the closed-loop behavior of the time-varying unknown system by an adequate selection of its gain, as simulation results have pointed out.

This paper presents a multi-estimation adaptive control strategy for stabilizing a potentially noninversely stable, linear and time-invariant plant. Such a strategy works with several discretization models of the plant. Each one of them is obtained by means of a fractional-order hold (FROH) and a multirate input in order to place its zeros into the stability region. A supervisor activates one of such models and maintains it in operation during at least a minimum residence time for stability purposes. That estimation model parameterizes a discrete-time adaptive controller for asymptotically matching a stable reference model at sampling instants

This paper focuses on the suboptimal regulation of multivariable discrete-time bilinear systems consisting of interconnected bilinear subsystems with respect to a linear quadratic optimal regulation criterion which involves the use of state weighting terms only. Three transformations are used in order to treat the scheme over an equivalent linear system. This leads to the appearance of quadratic weighting terms related to both transformed input and state variables. In this way, a Ricatti matrix-sequence allowing the synthesis of a standard feedback control law is obtained.

The problem of controlling a tandem of robotic manipulators composing a teleoperation system with force reflection is addressed in this paper. The final objective of this paper is twofold: 1) to design a robust control law capable of ensuring closed-loop stability for robots with uncertainties and 2) to use the so-obtained control law to improve the tracking of each robot to its corresponding reference model in comparison with previously existing controllers when the slave is interacting with the obstacle. In this way, a multiestimation-based adaptive controller is proposed. Thus, the master robot is able to follow more accurately the constrained motion defined by the slave when interacting with an obstacle than when a single-estimation-based controller is used, improving the transparency property of the teleoperation scheme. The closed-loop stability is guaranteed if a minimum residence time, which might be updated online when unknown, between different controller parameterizations is respected. Furthermore, the analysis of the teleoperation and stability capabilities of the overall scheme is carried out. Finally, some simulation examples showing the working of the multiestimation scheme complete this paper.

This paper discusses the selection of tools in milling operations.To carry out this research, it has been developed an expert system hinged on numerical methods.The knowledge base is given by limitations in process variables, which let us to define the allowable cutting parameter space.The mentioned process variables are, instabilities due to tool-work-piece interaction, knowing as chatter vibration, and the power available in the spindle motor.Then, a tool cost model is contrived.It is used to choose the suitable cutting tool, among a known set of candidate available cutters, and to obtain the appropriate cutting parameters, which are the expert system outputs.An example is presented to illustrate the method.

A multiestimation-based adaptive controller is designed for robotic manipulators. The identification subsystem is composed of a bank of estimation algorithms running in parallel incorporating each one a relative adaptation dead-zone in order to guarantee adequate estimation properties under the influence of bounded perturbations and unmodelled dynamics. Moreover, a supervisory index is proposed with the aim of evaluating the identification performance of each identification algorithm. Then, a higher order level supervision algorithm decides in real time the estimator which will parameterize the adaptive controller according to the values of the above supervisory indexes. There exists a minimum residence time between switches in such a way that the closed-loop system stability is guaranteed. It is verified through simulation examples that multiestimation based schemes can improve the transient response of adaptive systems through appropriate switching between the various estimation algorithms running in parallel. The closed-loop system is robustly stable under the influence of uncertainties due to a poor modelling of the robotic manipulator while the switching process is subject to a minimum residence time. Some simulation examples show the usefulness of the proposed scheme.

This paper develops a representation of multimodel based controllers using artificial intelligence techniques. These techniques will be neural networks and genetic algorithms. Thus, the interpretation of multimodel controllers in an artificial intelligence frame will allow the application of each specific technique to the design of improved multimodel based controllers. The obtained artificial intelligence-based multimodel controllers are compared with classical single model based ones and with standard multimodel controllers. It is shown through simulation examples that a transient response improvement can be achieved using the proposed artificial intelligence based techniques. Furthermore, a method for synthesizing multimodel based neural network controllers from already designed single model based ones is presented, extending the applicability of this kind of techniques to a more general type of controllers including the case of nonlinear plants. Also, some applications of genetic algorithms to multimodel controller design are proposed. In this way, a method to select a controller with improved robustness properties inspired from the genetic mutation operator is presented. The so obtained scheme has revealed to be adequate to use in noisy environments.

This paper deals with the robust stability of model-following control schemes for linear plants under parametric uncertainties and/or unmodeled dynamics and external disturbances. Apart from the nominal controller, a correcting one is incorporated to improve the disturbance rejection in certain ranges of frequencies. A Bode-type design is proposed to improve the reliability of the proposed method.

In this paper, we present an expert scheme designed to obtain discrete transfer functions for LTI systems under real sampling of finite duration rather than an instantaneous ideal one. For this purpose, the expert network handles two different identification methods to derive parametric discrete models techniques of reduced mathematical complexity from measured input-output data series. One of the methods is based on a typically used least-squares minimization, while the other one is based on Leverrier's algorithm; that is, using a data series of the impulse response of the system to identify a parametric discrete model. These techniques are of particular practical interest when the continuous-time system is unknown or when dealing with discrete-time systems whose analytical expression become very complex due, for instance, to the use of finite duration real sampling. The expert network improves the discretization process implementing a biestimation mechanism that switches to the model that provides a better performance at each considered estimation instant for different values of the hold order.

A pole-placement based adaptive controller synthesized from a multiestimation scheme is designed for linear plants. A higher level switching structure between the various estimation schemes is used to supervise the reparametrization of the adaptive controller in real time. The basic usefulness of the proposed scheme is to improve the transient response so that the closed-loop stability is guaranteed. The higher level switching structure is on-line supervised by a closed-loop performance based algorithm. The usefulness of the proposed algorithm is to on-line tune the free design parameter which appears in the switching structure so that the closed-loop behaviour, compared with the constant parameter case one, is improved when the design parameter is bad initialized, while the closed-loop performance is similar when the initial value is right.

The global uniform exponential stability independent of delay (g.u.e.s.i.d.) is investigated for a wide class of time-delay systems that may involve both point and distributed delays on finite intervals as well as infinitely distributed Volterra integro-differential dynamics. The stability problem is considered as a robust stability one with respect to an auxiliary system which may be defined very freely. The proposed method allows a very important generalisation related to the usual problem statement in the literature when the auxiliary system is defined by deleting the whole delayed dynamics. Conditions are established that ensure that the Laplace operator characterising the system has a bounded inverse on the closed complex right-half plane. The analysis is slightly modified for investigating uniform stability dependent of delay.

The asymptotic stability of time-delayed systems subject to multiple bounded point delays has received important attention in past years (see, for instance, Bourles, H. 1994. International Journal of Control , 59(2): 529-541; De la Sen, M. 2000. Electronics Letters , 36(4): 373-374; Xu, B. 2000. Journal of Mathematical Analysis and Applications , 282: 484-494). It is basically proved that the local f -stability in the delays (i.e., all the eigenvalues have prefixed strictly negative real parts located in Re s h - f < 0) may be tested for a set of admissible delays including possible zero delays either through a set of Lyapunov matrix inequalities or, equivalently, by checking that an identical number of matrices related to the delayed dynamics are all stability matrices. The result may be easily extended to check the k -asymptotic stability, independent of the delays; that is, for all the delays having any values, the eigenvalues are stable and located in Re s h k M 0 - (De la Sen, 2000; Xu, 2000). This number is 2 r for a set of r distinct point delays and includes all possible cases of alternate signs for summations for all the matrices of delayed dynamics (Xu, 2000).

An adaptive control scheme for mechanical manipulators is proposed. The control loop essentially consists of a network for learning the robot's inverse dynamics and on-line generating the control signal. Some simulation results are provided to evaluate the design. A supervisor is used to improve the system's performances during the adaptation transients. The supervisor exerts two supervisory actions. The first one consists basically in updating the free-design adaptive controller parameters so that the value of a quadratic loss function is maintained sufficiently small. Such a function involves past tracking errors and their predictions both on appropriate time horizons of low performances during the adaptation transients. The supervisor exerts two supervisory actions. The second supervisory action consists basically of a on-line adjustment of the sampling period within an interval centered in a nominal value of the sampling period. The sampling period is selected so that the transient of the tracking error is improved according to the simple intuitive rule of sampling faster as the tracking error changes faster.

The role of the decoupling zeros of linear systems being subsystems of cascade-parallel interconnected structures are discussed related to the overall interconnected system. The analysis is described in a simple way using as a basis the inspection of the conditions for fullness or defectiveness of the ranks of the system matrices of interest of the interconnected system and the subsystems.

This paper deals with the derivation of simple asymptotically stabilizing control laws for linear time-invariant hybrid systems, namely those that consist, in general, of coupled continuous-time and digital and or discretetime ones. The design philosophy consists of two steps. First, the synthesis of an asymptoticallystabilizing control law for the hybrid system at sampling instants is performed through the derivation of an auxiliary extended discrete system. Such a system contains both the digital substate and the discretization at sampling instants of the continuous-time substate. Subsequently, an incremental control law, which acts in between sampling instants, is synthesized to ensure the maintenance of global asymptotic stability during the intersample interval. The synthesis of the incremental control is based on small-gain type arguments related to the incremental controller. The related stability analysis is performed with the natural extension of the description on the intersample intervals of the state trajectory of the auxiliary extended system. Special emphasis is given to the use of multirate sampling as a tool for the synthesis of globally asymptotically stabilizing controls. Multirate control is an important tool to accommodate sampling of different signals in the loop at their natural sampling rates and also for noise-filtering purposes see, for instance Berg et al., 1986a; De la Sen, 1986a, 1996a, 1996b . In particular, faster input input and output sampling is shown to relax the conditions for asymptotic stabilization at sampling instants through state output feedback.