Approximate Discrete-time Model Research Articles

Stability Analysis of Characteristic Model Based All-Coefficient Adaptive Control for a Class of Minimum-Phase Linear System

Through treating the high-order subsystem as a nonlinear uncertainty, this paper converts a high-order hybrid adaptive system to a lower-order, sampled-data, indirect adaptive control problem through a special transformation, and analyzes its stability via the framework for stabilization of sampled-data systems based on its approximate discrete-time models. Therefore, a stability analysis framework for the adaptive control based on the characteristic model is proposed, and it is helpful to analyze the stability of system which applies the lower-order adaptive robust controller to control the high-order system.

Dynamical modeling and border collision bifurcation in pulse train controlled discontinuous conduction mode buck converter

According to the charge variation of output capacitor in a switching cycle, the output voltage variation in a switching cycle can be obtained, from which an approximate discrete-time model of pulse train (PT) controlled buck converter operating in discontinuous conduction mode (DCM) is established. Based on the model, the border-collision bifurcations of the PT controlled DCM buck converter with the variations of the load resistance and the input voltage are studied. By constructing the corresponding iterative map curves, the stabilities of the fixed points, formed by the orbits of period-1, period-2, period-3 and so on, are analyzed, and the mechanisms of border-collision bifurcations of the converter under different operation states are revealed. The analysis results indicate that with the variation of parameters, the PT controlled DCM buck converter always operates in different periodic states, in which the change of the operation mode with periodic state is caused by the border-collision bifurcation and the lyapunov exponent is always less than zero. By using PSIM circuit simulation software, the time domain waveforms and phase portraits under different load resistances are obtained. Experimental results are provided to verify the correctnesses of theoretical analyses and circuit simulations and the feasibility of dynamical modelling.

Stability and Feedback Stabilization of HIV Infection Model with Two Classes of Target Cells

We study the stability and feedback stabilization of the uninfected steady state of a human immunodeficiency virus (HIV) infection model. The model is a 6‐dimensional nonlinear ODEs that describes the interaction of the HIV with two classes of target cells, CD4+ T cells and macrophages, and takes into account the Cytotoxic T Lymphocytes (CTLs) immune response. Lyapunov function is constructed to establish the global asymptotic stability of the uninfected steady state of the model. In a control system framework, the HIV infection model incorporating the effect of Highly Active AntiRetroviral Therapy (HAART) is considered as a nonlinear control system with drug dose as control input. We developed treatment schedules for HIV‐infected patients by using Model Predictive Control (MPC‐)based method. The MPC is constructed on the basis of an approximate discrete‐time model of the HIV infection model. The MPC is applied to the stabilization of the uninfected steady state of the HIV infection model. Besides model inaccuracies that HIV infection model suffers from, some disturbances/uncertainties from different sources may arise in the modelling. In this work the disturbances are modelled in the HIV infection model as additive bounded disturbances. The robustness of the MPC against small model uncertainties or disturbances is also shown.

Discretisation method and stability criteria for non-linear systems under discrete-time time delay control

Time delay control (TDC) for non-linear systems has rapidly drawn attention as a result of its unusually robust performance and yet its extraordinarily compact form. In many real applications, TDC has been implemented digitally and the time delay, λ, was set to the sampling period of the control system, which is a constant during the control process. The existing stability analysis, however, has been made based on the assumption of the continuous-time TDC and infinitesimal time delay (λ→0). The assumption not only fails to reflect the reality that the closed-loop system (CLS) is a sampled-data system, but also leads to a stability criterion in which important parameters, such as λ, are absent. In this paper, therefore, sufficient stability criteria for a non-linear system based on the premise of discrete-time TDC and λ that is equal to the sampling period are presented. To this end, we have first proposed a discretization method to derive the approximate discrete-time model of CLS. Then by using the model and the concepts of consistency and Lyapunov stability, we have derived stability criteria for the exact discrete-time model of CLS. The suggested criteria consist of the sampling period and other parameters of TDC. These criteria have been verified by simulation results.

Fuzzy Stabilization of Nonlinear Systems Under Sampled-Data Feedback: An Exact Discrete-Time Model Approach

This paper addresses stabilization problems for a nonlinear system via a sampled-data fuzzy controller. The nonlinear system is assumed to be exactly modeled in Takagi-Sugeno's form, at least locally. Unlike the conventional direct discrete-time design approach based on an approximate discrete-time model, the sampled-data fuzzy controllers are designed based on an exact discrete-time model. Sufficient design conditions are formulated in terms of linear matrix inequalities. It is shown that whenever the exact discrete-time fuzzy model is asymptotically stabilizable via the sampled-data fuzzy controller uniformly bounded in the state, then so is the original nonlinear system. A numerical example is given to illustrate the effectiveness of the proposed methodology.

HIV dynamics: Analysis and robust multirate MPC-based treatment schedules

Analysis and control of human immunodeficiency virus (HIV) infection have attracted the interests of mathematicians and control engineers during the recent years. Several mathematical models exist and adequately explain the interaction of the HIV infection and the immune system up to the stage of clinical latency, as well as viral suppression and immune system recovery after treatment therapy. However, none of these models can completely exhibit all that is observed clinically and account the full course of infection. Besides model inaccuracies that HIV models suffer from, some disturbances/uncertainties from different sources may arise in the modelling. In this paper we study the basic properties of a 6-dimensional HIV model that describes the interaction of HIV with two target cells, CD4 + T cells and macrophages. The disturbances are modelled in the HIV model as additive bounded disturbances. Highly Active AntiRetroviral Therapy (HAART) is used. The control input is defined to be dependent on the drug dose and drug efficiency. We developed treatment schedules for HIV infected patients by using robust multirate Model Predictive Control (MPC)-based method. The MPC is constructed on the basis of the approximate discrete-time model of the nominal model. We established a set of conditions, which guarantee that the multirate MPC practically stabilizes the exact discrete-time model with disturbances. The proposed method is applied to the stabilization of the uninfected steady state of the HIV model. The results of simulations show that, after initiation of HAART with a strong dosage, the viral load drops quickly and it can be kept under a suitable level with mild dosage of HAART. Moreover, the immune system is recovered with some fluctuations due to the presence of disturbances.

Sampled-data control and stability of sets for nonlinear systems

This paper is devoted to the investigation of the relation between stability properties of nonlinear closed-loop control systems and those of families of approximations with respect to a given set. Firstly, such relation is derived for continuous-time closed-loop systems and the associated sampled-data systems. Secondly, stability results are presented for parametrized families of exact and approximate discrete-time models considered with parametrized family of feedbacks. In both cases, the sampling period is allowed to vary from step to step, and the stability is investigated with respect to nonempty closed (not necessarily compact) sets. Necessary and sufficient conditions are presented on the stability of approximations ensuring stability of the approximated systems.

Stability connection between sampled-data fuzzy control systems with quantization and their approximate discrete-time model

This paper concerns the stability of a sampled-data Takagi–Sugeno (T–S) fuzzy control system with quantization, when a controller design is based on an approximate discrete-time model of the plant without quantization. The motivations come from the facts that digital devices for interfacing a plant with a controller quantize signals and an exact discrete-time model of the T–S fuzzy system is generally not amenable to synthesis process. We show that the concerned system is Lagrange stabilizable by the controller asymptotically stabilizing the approximate discrete-time model. A constructive design algorithm for the developed stability analysis is proposed in terms of linear matrix inequalities.

On Stability of Sets for Sampled-Data Nonlinear Inclusions via Their Approximate Discrete-Time Models and Summability Criteria

This paper consists of two main parts. In the first part, we provide a framework for stabilization of arbitrary (not necessarily compact) closed sets for sampled-data nonlinear differential inclusions via their approximate discrete-time models. We generalize [D. Nešić, A. R. Teel, and P. V. Kokotović, Systems Control Lett., 38 (1999), pp. 259–270, Theorem 1] in several different directions: we consider stabilization of arbitrary closed sets, plants described as sampled-data differential inclusions, and arbitrary dynamic controllers in the form of difference inclusions. Our result does not require the knowledge of a Lyapunov function for the approximate model, which is a standing assumption in [D. Nešić and A. R. Teel, IEEE Trans. Automat. Control, 49 (2004), pp. 1103–1122] and [D. Nešić, A. R. Teel, and P. V. Kokotović, Systems Control Lett., 38 (1999), pp. 259–270, Theorem 2]. We present checkable conditions that one can use to conclude semiglobal asymptotic stability, or global exponential stability (GES), of the sampled-data system via appropriate properties of its approximate discrete-time model. In the second part, we present sufficient conditions for stability of parameterized difference inclusions that involve various summability criteria on trajectories of the system to conclude global asymptotic stability (GAS) or GES, and they represent discrete-time counterparts of results given in [A. R. Teel, E. Panteley, and A. Loria, Math. Control Signals Systems, 15 (2002), pp. 177–201]. These summability criteria are not Lyapunov based, and they are tailored to be used within our above-mentioned framework for stabilization of sampled-data differential inclusions via their approximate discrete-time models. We believe that these tools will be a useful addition to the toolbox for controller design for sampled-data nonlinear systems via their approximate discrete-time models.

Input-to-state stabilization for nonlinear dual-rate sampled-data systems via approximate discrete-time model

The problem of state feedback stabilization of nonlinear sampled-data systems is considered under the “low measurement rate” constraint. A dual-rate control scheme is proposed that utilizes a numerical integration scheme to approximately predict the current state. Given an approximate discrete-time model of a sampled nonlinear plant and given a family of controllers that stabilizes the plant model in input-to-state sense, we show that under some standard assumptions the closed loop dual-rate sampled data system is input-to-state stable in the semiglobal practical sense.

On the model-based approach to nonlinear networked control systems

The problem of model-based stabilization of a nonlinear system based on its approximate discrete-time model is addressed, under the assumption that both the feedforward and the feedback paths are subject to network-induced constraints. These constraints include irregularity of the transfer intervals, time-varying communication delays, and the possibility of packet losses. A communication protocol that copes with these constraints is proposed. A “Stability+performance recovery” result for the nonlinear model-based networked control system (NCS) is formulated and proven.Simulation results presented confirm that the proposed method improves the maximum allowable transfer interval.

HIV dynamics: Analysis and robust multirate MPC-based treatment schedules

Abstract Analysis and control of human immunodeficiency virus (HIV) infection have attracted the interests of mathematicians and control engineers during the recent years. Several mathematical models exist and adequately explain the interaction of the HIV infection and the immune system up to the stage of clinical latency, as well as viral suppression and immune system recovery after treatment therapy. However, none of these models can completely exhibit all that is observed clinically and account the full course of infection. Besides model inaccuracies that HIV models suffer from, some disturbances/uncertainties from different sources may arise in the modelling. In this paper we study the basic properties of a 6-dimensional HIV model that describes the interaction of HIV with two target cells, CD4+ T cells and macrophages. The disturbances are modelled in the HIV model as additive bounded disturbances. Highly Active AntiRetroviral Therapy (HAART) is used. The control input is defined to be dependent on the drug dose and drug efficiency. We developed treatment schedules for HIV infected patients by using robust multirate Model Predictive Control (MPC)-based method. The MPC is constructed on the basis of the approximate discrete-time model of the nominal model. We established a set of conditions, which guarantee that the multirate MPC practically stabilizes the exact discrete-time model with disturbances. The proposed method is applied to the stabilization of the uninfected steady state of the HIV model. The results of simulations show that, after initiation of HAART with a strong dosage, the viral load drops quickly and it can be kept under a suitable level with mild dosage of HAART. Moreover, the immune system is recovered with some fluctuations due to the presence of disturbances.

Theoretical justification of approximate norm minimization method for intelligent digital redesign

This paper investigates the theoretical justification of intelligent digital redesign (IDR) methods based on the approximate discrete-time models. Its main purpose is to design the digital fuzzy controller achieving the minimum norm distance between the trajectories of the approximate closed-loop systems under the analog and the digital controls. It is verified that, if there exist the digital gains such that the approximate norm distance is sufficiently small, the redesigned controller makes the trajectories of the exact closed-loop systems close, or, at least, asymptotically stabilizes the plant.

Robust [formula omitted] observer design for sampled-data Lipschitz nonlinear systems with exact and Euler approximate models

An LMI approach is proposed for the design of robust H ∞ observers for a class of Lipschitz nonlinear systems. Two type of systems are considered, Lipschitz nonlinear discrete-time systems and Lipschitz nonlinear sampled-data systems with Euler approximate discrete-time models. Observer convergence when the exact discrete-time model of the system is available is shown. Then, practical convergence of the proposed observer is proved using the Euler approximate discrete-time model. As an additional feature, maximizing the admissible Lipschitz constant, the solution of the proposed LMI optimization problem guaranties robustness against some nonlinear uncertainties. The robust H ∞ observer synthesis problem is solved for both cases. The maximum disturbance attenuation level is achieved through LMI optimization.

Effective digital implementation of fuzzy control systems based on approximate discrete-time models

This paper addresses an effective digital implementation of fuzzy control systems via an intelligent digital redesign (IDR) approach. The purpose of IDR is to effectively convert an existing continuous-time fuzzy controller to an equivalent sampled-data fuzzy controller in the sense of the state-matching. The authors show that, under reasonable assumptions, the IDR based on the exact discrete-time models can be reduced to the IDR based on the approximate discrete-time models. The state-matching error between the closed-loop trajectories is carefully analyzed using the integral quadratic functional approach. The estimation of the state-matching error is presented using the linear matrix inequality (LMI) techniques. The problem of designing the sampled-data fuzzy controller to minimize the estimation as well as to guarantee the stability is formulated and solved as the convex optimization problem with LMI constraints. It is also shown that the resulting sampled-data fuzzy controller recovers the performance of the continuous-time fuzzy controller as the sampling period approaches zero. A numerical example is used to demonstrate the effectiveness of the proposed design technique.

STABILIZING OUTPUT FEEDBACK RECEDING HORIZON CONTROL OF SAMPLED-DATA NONLINEAR SYSTEMS VIA THEIR APPROXIMATE DISCRETE-TIME MODELS

This paper is devoted to the stabilization problem of sampled-data nonlinear systems by receding horizon control. The receding horizon control method requires the availability of the full state measurements. In some applications, not all state are directly measurable. Therefore, an output feedback can be used which consists of a stabilizing state feedback receding horizon control and an observer for estimating the unknown states. Due to the unavailability of the exact discrete-time model of nonlinear plants, both the receding horizon control and the observer are designed via an approximate discrete-time model of the plant. We investigate under what conditions this design achieve practical stability for the exact discrete-time model.

Multirate sampling and input-to-state stable receding horizon control for nonlinear systems

In this paper, a robust receding horizon control for multirate sampled-data nonlinear systems with bounded disturbances is presented. The proposed receding horizon control is based on the solution of Bolza-type optimal control problems for the approximate discrete-time model of the nominal system. “Low measurement rate” is assumed. It is shown that the multistep receding horizon controller that stabilizes the nominal approximate discrete-time model also practically input-to-state stabilizes the exact discrete-time system with disturbances.

Stabilization of sampled-data nonlinear systems via backstepping on their Euler approximate model

Two integrator backstepping designs are presented for digitally controlled continuous-time plants in special form. The controller designs are based on the Euler approximate discrete-time model of the plant and the obtained control algorithms are novel. The two control laws yield, respectively, semiglobal-practical stabilization and global asymptotic stabilization of the Euler model. Both designs achieve semiglobal-practical stabilization (in the sampling period that is regarded as a design parameter) of the closed-loop sampled-data system. A simulation example illustrates that the obtained controllers may sometimes be superior to backstepping controllers based on the continuous-time plant model that are implemented digitally.

Construction of discrete-time models for port-controlled Hamiltonian systems with applications

The issues of constructing a discrete-time model for Hamiltonian systems are in general different from those for dissipative systems. We propose an algorithm for constructing an approximate discrete-time model, which guarantees Hamiltonian conservation. We show that the algorithm also preserves, in a weaker sense, the losslessness property of a class of port-controlled Hamiltonian systems. An application of the algorithm to port-controlled Hamiltonian systems with quadratic Hamiltonian is presented, and we use this to solve the stabilization problem for this class of systems based on the approximate discrete-time model constructed using the proposed algorithm. We illustrate the usefulness of the algorithm in designing a discrete-time controller to stabilize the angular velocity of the dynamics of a rigid body.

Input-to-state stability for discrete-time time-varying systems with applications to robust stabilization of systems in power form

Input-to-state stability (ISS) of a parameterized family of discrete-time time-varying nonlinear systems is investigated. A converse Lyapunov theorem for such systems is developed. We consider parameterized families of discrete-time systems and concentrate on a semiglobal practical type of stability that naturally arises when an approximate discrete-time model is used to design a controller for a sampled-data system. An application of our main result to time-varying periodic systems is presented, and this is used to solve a robust stabilization problem, namely to design a control law for systems in power form yielding semiglobal practical ISS (SP-ISS).