Nonlinear Sampled-data Systems—A lifting framework

The problem of feedback linearization (FL) of continuous and discrete nonlinear MIMO systems is considered. The idea of FL method consists in converting the original nonlinear system into a linear one by means of feedback. Then, the methods of control theory for linear systems are used for system design. A widespread approach to FL design is based on the method of normal form, that uses a nonsingular transformation of system state variables z = T(x). In order to obtain a normal form of the nonlinear system in the neighbor of a some point, it is necessary to determine a special function - the system virtual output, for which a relative degree (in the case of single input) or a vector relative degree (in the case of multiple input) is determined. Applicability of the normal form method for FL is provided by the conditions of controllability and involutivity for the considered nonlinear system, which are not always true. Moreover, when developing a linearizing control law, the main difficulty lies in the transition from transformed variables z to state variables x of the original system. In this paper, we propose another approach, based on representing the original nonlinear system into a state-dependent coefficient form and applying the canonical similarity transformation z = T(x)x, that allow getting the system to canonical form, that considerably simplifies the FL problem. Such similarity transformation allow accomplishing linearization of system without determining of the virtual system output. Another advantage of the proposed method is that the technique of the transition from the transformed variables z to the state variables x of the original system is simpler. The results are illustrated by examples for continuous and discrete nonlinear systems.

Linear stabilization of the programmed motions of non-linear controlled dynamical systems under parametric perturbations

In this paper, we provide a novel defence strategy for a nonlinear sampled-data control system under zero-dynamics attacks. In a sampled data structure, sampling zeros induced by discretization make a system vulnerable to deception attacks. we analyze the dissipativity in the zero-dynamics part of the system equipped with a multi-rate setup and find conditions on sampling rates to neutralize the attacker's target plan. We show that, under some mild conditions, using multi-rate sampling in the nonlinear sampled-data system not only preserves the dissipativity property of the intrinsic zero-dynamics but also stabilizes the extrinsic zero-dynamics induced by the sample and hold process, and as a result attenuate the effects of attacks on the system stability. Finally, a numerical example is used to illustrate the effectiveness of the proposed approach.

We introduce a controller design methodology for nonlinear systems via complementary Linear Parameter Varying (LPV) controller and observer structures. The recently developed method is able to control physiological systems even with complex nonlinearities — without using Linear Matrix Inequalities (LMI) or other techniques requiring iterations. The developed method is based on the classical state feedback theorems, matrix similarity theorems and supplementary controller and observer structure which efficiently uses the mathematical properties of the parameter space of the LPV system. The main benefits of the proposed method is that the controller design does not require mathematical tools needing iteration thus high computational capacity. We used a nonlinear compartmental model in order to demonstrate the application of the method. The results showed that the developed complementary LPV controller and observer structures perform well on both the LPV systems and the original nonlinear system as well.

This paper addresses the diagonal stability problem of a class of discrete-time nonlinear positive switched systems with delays. Different from previous methods, we first use homogeneous polynomial Lyapunov function method to present the diagonal stability condition of the corresponding linear system of the original nonlinear system, and then use the comparison method to establish its relationship with the nonlinear system, so as to obtain the final result. Finally, a numerical example is provided to illustrate the theoretical results obtained in this paper.

The article shows the possibility of using the Poincare-Steklov filter to ensure the stability ofharware in the loop (HIL) simulation of nonlinear systems.HIL simulation involves splitting theinitial system into parts, with one part being modeled numerically on a computer, and the secondpart is represented by a real physical object. The parts of the system exchange data with eachother through a hardware-software interface, which can be implemented in different ways andshould ensure stability, as well as convergence of the results of HIL simulation to the results ofmodeling the original system. The variants of constructing software and hardware interfaces ITM,TLM, TFA, PCD, DIM, GCS and the Poincare-Steklov filter are described in the relevant literaturesources. The article shows how the original nonlinear system was divided into parts using thePoincare-Steklov filter, which, accordingly, led to the splitting into parts of the system of equationsdescribing the behavior of the original system. Next, it was shown how the values of the stabilizingparameters of the Poincare-Steklov filter were calculated and the systems of equations of the systemdivided into parts were corrected in accordance with the obtained values. At the next stage,the article presents the results of numerical modeling of the initial and partitioned system inMATLAB. When modeling in parts, the parts of the system exchanged data with each other at eachstep of the simulation only once with a delay of h. This method of numerical modeling of a systemdivided into parts is as close as possible to the processes occurring during semi-natural modelingof systems. A comparison of the obtained simulation results of the initial and the system dividedinto parts allowed us to conclude that the Poincare-Steklov filter, with the correct choice of thevalues of the stabilizing parameters, allows for the stability and convergence of the results of seminaturalmodeling of both linear and nonlinear systems

The problem of H∞ filtering design is studied for non-linear sampled-data systems using the Takagi–Sugeno (T–S) fuzzy model approach. The sampled-data filtering is to estimate the states of a continuous-time system using only sampled measurements at discrete instants of time. Traditionally, the sufficient conditions for the existence of such an H∞ filter are characterised in terms of the solution of a differential Hamilton–Jacobi inequality with jumps, which is equivalent to solving the partial differential inequality with jumps. In general, there is no analytical solution for this non-linear partial differential inequality with jumps. First, in this study, the T–S fuzzy model is proposed to represent a class of non-linear sampled-data systems. Next, by using the T–S fuzzy model, the H∞ fuzzy filtering design problem for non-linear sampled-data system is characterised in terms of a linear matrix inequality (LMI) problem. Hence, the H∞ fuzzy filter of non-linear sampled-data systems can be given via solving LMIs instead of solving a differential Hamilton–Jacobi inequality with jumps. To illustrate the results, a numerical example is included.

An identification method of nonlinear dynamic system is studied in this paper. First, suppose that the original nonlinear dynamic system is described by Hammerstein model. Then, an intermediate model is generated. Next, Parameters of the intermediate model are obtained using a Bacterial Chemotaxis Optimization (BCO) approach. Finally, through the relationships of the parameters of intermediate model and those of Hammerstein model, we derive the parameters of the system. Consequently, the original nonlinear dynamic system is identified. The feasibility and efficiency of the presented algorithm are demonstrated using numerical simulations.

This paper discusses nonnegativity and positivity concepts and related properties for the state and output trajectory solutions of dynamic linear time-invariant systems described by functional differential equations subject to point time delays. The various nonnegativities and positivities are introduced hierarchically from the weakest one to the strongest one while separating the corresponding properties when applied to the state space or to the output space as well as for the zero-initial state or zero-input responses. The formulation is first developed by defining cones for the input, state and output spaces of the dynamic system, and then extended, in particular, to cones being the three first orthants each being of the corresponding dimension of the input, state, and output spaces.

Integral versions of input-to-state stability for dual-rate nonlinear sampled-data systems

This paper presents a general framework based on lifting technique for sampled-data systems with input time delays. By analyzing the properties of operator-valued matrices of lifted systems with input time delays, an extended lifting technique is obtained. It is then shown that, with the proposed lifting technique, the complex behavior of the system can be illustrated by two simple lifted systems, which construct the extended lifted system. The extended lifted system has the same induced norm as that of the original system with an input time delay, since the proposed lifting technique is an isometric isomorphism. Through applying the proposed lifting technique to sampled-data systems with input time delays, the time-invariant discrete-time system with infinite-dimensional input and output spaces is obtained. The equivalent discrete-time system, which is derived from the extended lifted system, can satisfy the problem of H2 sampled-data control systems with input time delays. Simulation results are given to show that the proposed method can guarantee a more stable system response than the conventional H2 sampled-data controller for the sampled-data systems with the various input time delays.

The paper studies an extension to nonlinear systems of a recently proposed approach to the definition of modal participation factors. A definition is given for local mode-in-state participation factors for smooth nonlinear autonomous systems. While the definition is general, the resulting measures depend on the assumed uncertainty law governing the system initial condition, as in the linear case. The work follows Hashlamoun et al. (IEEE Trans Autom Control 54(7):1439–1449 2009) in taking a mathematical expectation (or set-theoretic average) of a modal contribution measure over an uncertain set of system initial state. Poincaré linearization is used to replace the nonlinear system with a locally equivalent linear system. It is found that under a symmetry assumption on the distribution of the initial state, the tractable calculation and analytical formula for mode-in-state participation factors found for the linear case persists to the nonlinear setting. This paper is dedicated to the memory of Professor Ali H. Nayfeh.

EP-based adaptive tracker with observer and fault estimator for nonlinear time-varying sampled-data systems against actuator failures

A NOVEL approach to analyze the robustness of a flight control system (FCS) with respect to parametric uncertainties is presented, which specifically applies to gliding vehicles in the terminal phases of reentry flight. Robustness analyses are particularly challenging for these systems. Their reference trajectories are appreciably time-varying and encompass a broad variety of flight regimes. Furthermore, significant uncertainties on some critical design parameters affect the vehicle model, most notably those related to the aerodynamic behavior [1]. Current practice in FCS robustness analysis for this kind of application mainly relies on the theory of linear time-invariant (LTI) systems. In this approach, the original nonlinear system is linearized around a limited number of representative time-varying trajectories, including the nominal one. Then thewell-known frozentime approach [2] is applied, yielding multiple LTI models. In this way, classical stability margins [3] or more sophisticated LTI-based robustness criteria, such as analysis [4] and D-stability analyses [5], can be evaluated. Recently, a Lyapunov-based criterion coupled to interval analysis techniques [6] has been proposed for establishing robustness of a FCS. This approach does not resort to linearization of the system dynamics, but still requires the introduction of fictitious equilibrium points obtained by a frozen-time approach. Even if the flight experience demonstrated that frozen-time approaches are indeed operative, they are widely recognized as inefficient [7]. In fact, because the nominal trajectory may not be an equilibrium trajectory for the system in offnominal conditions, frozen-time analyses can provide only indicative, and often heavily conservative, results. To overcome such problems, further investigations are usually performed to identify a limited set of worst-case combinations of uncertain parameters to be used for FCS design refinement. In this case, nonlinear simulations in specific offnominal conditions, selected using sensitivity analysis and designer’s experience, represent the current practice. Optimization-based worst-case search has also been proposed [8], which may disclose the mutual effects of multiple uncertainties, but to a limited extent. In fact, the complexity of reentry dynamics under multiple uncertainties implies that actual worst cases relevant for FCS design refinement are difficult to identify. In any case, worst-case analysis can select only a limited number of test cases, hiding possible further causes of requirement violations, thus driving wrong refinement strategies that would not solve (or even worsen) FCS robustness problems. Monte Carlo (MC) analysis is, in practice, the only tool that is capable of investigating the combined effect of all uncertainties with a reasonable effort. However, being only a verification tool, when unsatisfactory robustness is discovered at this stage, the identification of its causes can require considerable postprocessing effort [9]. This yields one of the major limitations of this approach: that is, the limited support to the FCS design refinement when a requirement violation occurs due to poor robustness. As a result, in these cases, one is forced to iterate the design with scarce additional information. The present paper contributes toward advancing the current practice used in robustness analysis for FCS design refinement by introducing a method that takes into account nonlinear effects of multiple uncertainties over the whole trajectory, to be used before robustness is finally assessed withMC analysis. Themethod delivers feedback on the causes of requirement violation and adopts robustness criteria directly linked to the original mission or system requirements, such as those employed in MC analyses. The first objective is achieved estimating the region of requirement compliance in the space of the uncertain parameters. In this way, the approach provides an exhaustive coverage of the uncertainty’s effects on the FCS robustness. To translate mission requirements into robustness criteria over the whole trajectory, rather than at isolated points as in frozen-time approaches, we make use of the practical stability concept [10], which, to the authors’ knowledge, has never been applied to robustness analyses of atmospheric reentry vehicles.

In this paper, the problem of adaptive fuzzy decentralized optimal control is investigated for a class of nonlinear large-scale systems in strict-feedback form. The considered nonlinear large-scale systems contain the unknown nonlinear functions and unmeasured states. By utilizing the fuzzy logic systems to approximate the unknown nonlinear functions and cost functions, a fuzzy state observer is established to estimate the unmeasured states. The control design is divided into two phases. First, by using the state observer and the backstepping design technique, a feedforward decentralized controller with parameters adaptive laws is designed, by which the original controlled strict-feedback nonlinear large-scale system is transformed into an equivalent affine nonlinear large-scale system. Second, by using adaptive dynamic programming theory, a feedback decentralized optimal controller is developed for the equivalent affine nonlinear system. The whole adaptive fuzzy decentralized optimal control scheme consists of a feedforward decentralized controller and a feedback decentralized optimal controller. It is shown that the proposed adaptive fuzzy decentralized optimal control approach can guarantee that all the signals in the closed-loop system are bounded, and the tracking errors converge to a small neighborhood of zero. In addition, the proposed control approach can guarantee that the cost functions are minimized. Simulation results are given to demonstrate the effectiveness of the proposed control approach.