Disturbance tolerance and H ∞ control of port-controlled hamiltonian systems in the presence of actuator saturation

This paper investigates the disturbance tolerance and H _∞ control of multi-input Port-Controlled Hamiltonian (PCH) systems in the presence of actuator saturation which may be not openloop stable. A simple condition is derived under which trajectories starting from the origin will remain inside an ellipsoid. The disturbance tolerance ability of the closed-loop system under a given feedback control law is measured by the size of this ellipsoid. Based on the above mentioned condition, the problem of disturbance tolerance can be expressed in the form of the linear matrix inequalities (LMIs) optimization problem with constraints. In addition, an H _∞ control approach is presented to attenuate the disturbances, and disturbance rejection ability in terms of L ₂ gain is also determined by the solution of an LMI optimization problem. Study of an illustrative example with simulations shows the effectiveness of the methods proposed.     

Robust H ∞ filter design for affine fuzzy systems

This paper investigates the problem of robust H _∞ filtering for nonlinear systems, which are described by affine fuzzy parts with norm-bounded uncertainties. The system outputs have been chosen as premise variables, which can guarantee the plant and the filter always switch to the same region. By using a piecewise Lyapunov function and adding slack matrix variables, a fuzzy-basis-dependent H _∞ filter design method is obtained in the formulation of linear matrix inequalities (LMIs), which can be efficiently solved numerically. Compared with the existing results, the proposed method needs less LMI constraints and leads to less conservatism. Finally, numerical examples illustrate the effectiveness of the new results.     

Output-feedback control for switched linear systems subject to actuator saturation

This article is devoted to the output-feedback ?_∞ control problem for switched linear systems subject to actuator saturation. We consider both continuous- and discrete-time switched systems. Using the minimal switching rule, nonlinear output feedbacks expressed in the form of quasi-linear parameter varying system are designed to satisfy a pre-specified disturbance attenuation level defined by the regional ?₂ (?₂)-gains over a class of energy-bounded disturbances. The conditions are expressed in bilinear matrix inequalities and can be solved by line search coupled with linear matrix inequalities optimisation. A spherical inverted pendulum example is used to illustrate the effectiveness of the proposed approach.     

On the R-order of coupled sequences II

As in the preceding paper [3], the question whether some of the sequences {x _{n, i} } coupled by a system (3) of inequalities converge at least with a certain R-order τ is reduced to the nonnegative solvability of the system (4) of linear inequalities. Further, the optimal R-order τ implied by (3) is characterized as the spectral radius of a certain matrix composed of exponents appearing in (3).     

New delay range–dependent stability criteria for interval time‐varying delay systems via Wirtinger‐based inequalities

Stability conditions for time‐delay systems using the Lyapunov‐based methodologies are generically expressed in terms of linear matrix inequalities. However, due to assuming restrictive conditions in deriving the linear matrix inequalities, the established stability conditions can be strictly conservative. This paper attempts to relax this problem for linear systems with interval time‐varying delays. A double‐integral inequality is derived inspired by Wirtinger‐based single‐integral inequality. Using the advanced integral inequalities, the reciprocally convex combination techniques and necessary slack variables, together with extracting a condition for the positive definiteness of the Lyapunov functional, novel stability criteria, have been established for the system. The effectiveness of the criteria is evaluated via 2 numerical examples. The results indicate that more complex stability criteria not only improve the stability region but also bring computational expenses.     

Tracing Lineage of Array Data

Arrays are a common and important class of data in many applications. Arrays can model data such as digital images, digital video, scientific and experimental data, matrices, and finite element grids. Although array manipulations are diverse and domain-specific, they often exhibit structural regularities. This paper describes an algorithm called sub-pushdown to trace data lineage in such array computations. Lineage tracing is a type of data-flow analysis that relates parts of a result array to those parts of the argument (base) arrays that have bearings on the result array parts. Sub-pushdown can be used to trace data lineage in array-manipulating computations expressed in the Array Manipulation Language (AML) that was introduced previously. Sub-pushdown has several useful features. First, the lineage computation is expressed as an AML query. Second, it is not necessary to evaluate the AML lineage query to compute the array data lineage. Third, sub-pushdown never gives false-negative answers. Sub-pushdown has been implemented as part of the ArrayDB prototype array database system that we have built.     

Minimax filtering: γ 0-optimal observers and generalized H ∞-optimal filters

We study minimax observation and filtering problems. We introduce the notions of a γ ₀-optimal observer and a generalized H _∞-optimal filter of full and reduced orders whose existence conditions and construction methods are expressed in terms of linear matrix inequalities.     

Formalization and implementation of floating-point arithmetics

The paper is intended to show that floating-point arithmetic can be implemented in a way which leads to reasonable mathematical structures as described in chapters 5 and 6. It turns out for instance that all the rules of the minus-operator of the real numbers can be saved and that with respect to ≦ and ≧ inequalities can be manipulated as if they were real inequalities. These structures also occur in other fields of mathematics. They allow among others many theoretical considerations with floating-point arithmetics. Theorem 5.1 is the main result for the implementation. It reduces the structures to special properties of the rounding function. In chapter 3 these properties are derived as necessary conditions for an algebraic and order homomorphism between the real numbers and a floating-point system. Chapter 4 gives the algorithms for an implementation of floating-point arithmetics for all roudings of the set {?Δ□_μ,μ=0(1)b} (for definition see chapter 2) using short and long accumulators. It is an essential result that the whole implementation can be separated into fiveindependent steps as indicated in figure 2 and its context which means that an exchange of the rounding does not influence any other part of the algorithm. Although theorem 5.1 calls for monotone and antisymmetric roundings a computer is most flexibly equipped with the monotone downwardly directed rounding Δ since all roundings of the considered set can easily be generated by it and furthermore then also the directed roundings are available.     

H ∞ model reduction for discrete time-delay systems: delay-independent and dependent approaches

This paper investigates the problem of H _∞ model reduction for linear discrete-time state-delay systems. For a given stable system, our attention is focused on the construction of reduced-order models, which guarantee the corresponding error system to be asymptotically stable and have a prescribed H _∞ error performance. Both delay-independent and dependent approaches are developed, with sufficient conditions obtained for the existence of admissible reduced-order solutions. Since these obtained conditions are not expressed as strict linear matrix inequalities (LMIs), the cone complementary linearization method is exploited to cast them into sequential minimization problems subject to LMI constraints, which can be readily solved in standard numerical software. In addition, the development of reduced-order models with special structures, such as delay-free models and zeroth-order models, is also addressed. The approximation methods presented in this paper can be further extended to cope with systems with uncertain parameters. Two numerical examples have been provided to show the effectiveness of the proposed theories.     

Integer programming with 2-variable equations and 1-variable inequalities

We present an efficient algorithm to find an optimal integer solution of a given system of 2-variable equalities and 1-variable inequalities with respect to a given linear objective function. Our algorithm has worst-case running time in O(N²) where N is the number of bits in the input.     

Separation of circulating tokens

Self-stabilizing distributed control is often modeled by token abstractions. A system with a single token may implement mutual exclusion; a system with multiple tokens may ensure that immediate neighbors do not simultaneously enjoy a privilege. In models of process control, tokens may represent physical objects whose movement is controlled. The problem studied in this paper is to ensure that a synchronous system with m circulating tokens has at least d distance between tokens. This problem is first considered in a ring where d is given whilst m and the ring size n are unknown. The protocol solving this problem can be uniform, with all processes running the same program, or it can be non-uniform, with some processes acting only as token relays. The protocol for this first problem is simple, and can be expressed with a Petri net formalism. A second problem is to maximize d when m is given, and n is unknown. For the second problem, this paper presents a non-uniform protocol with a single corrective process.     

Solving linear differential equations as a minimum norm least squares problem with error-bounds

Linear ordinary/partial differential equations (DEs) with linear boundary conditions (BCs) are posed as an error minimization problem. This problem has a linear objective function and a system of linear algebraic (constraint) equations and inequalities derived using both the forward and the backward Taylor series expansion. The DEs along with the BCs are approximated as linear equations/inequalities in terms of the dependent variables and their derivatives so that the total error due to discretization and truncation is minimized. The total error along with the rounding errors render the equations and inequalities inconsistent to an extent or, equivalently, near-consistent, in general. The degree of consistency will be reasonably high provided the errors are not dominant. When this happens and when the equations/inequalities are compatible with the DEs, the minimum value of the total discretization and truncation errors is taken as zero. This is because of the fact that these errors could be negative as well as positive with equal probability due to the use of both the backward and forward series. The inequalities are written as equations since the minimum value of the error (implying error-bound and written/expressed in terms of a nonnegative quantity) in each equation will be zero. The minimum norm least-squares solution (that always exists) of the resulting over-determined system will provide the required solution whenever the system has a reasonably high degree of consistency. A lower error-bound and an upper error-bound of the solution are also included to logically justify the quality/validity of the solution.     

Parameter-dependent robust H ∞ filter design for uncertain discrete-time systems with quantized measurements

This paper deals with the problem of parameter-dependent robust H _∞ filter design for uncertain discrete-time systems with output quantization. The uncertain parameters are supposed to reside in a polytope. The system outputs are quantized by a memoryless logarithmic quantizer before being transmitted to a filter. Attention is focused on the design of a robust H _∞ filter to mitigate quantization effects and ensure a prescribed H _∞ noise attenuation level. Via introducing some slack variables and using the parameter-dependent Lyapunov function, sufficient conditions for the existence of a robust H _∞ filter are expressed in terms of linear matrix inequalities (LMIs). Finally, a numerical example is provided to demonstrate the effectiveness of the proposed approach.     

A linear matrix inequality approach to H∞ control

The continuous- and discrete-time H_∞ control problems are solved via elementary manipulations on linear matrix inequalities (LMI). Two interesting new features emerge through this approach: solvability conditions valid for both regular and singular problems, and an LMI-based parametrization of all H_∞-suboptimal controllers, including reduced-order controllers. The solvability conditions involve Riccati inequalities rather than the usual indefinite Riccati equations. Alternatively, these conditions can be expressed as a system of three LMIs. Efficient convex optimization techniques are available to solve this system. Moreover, its solutions parametrize the set of H_∞ controllers and bear important connections with the controller order and the closed-loop Lyapunov functions. Thanks to such connections, the LMI-based characterization of H_∞ controllers opens new perspectives for the refinement of H_∞ design. Applications to cancellation-free design and controller order reduction are discussed and illustrated by examples.     

An LMI approach to minimum sensitivity analysis with application to fault detection

This paper systematically studies the minimum input sensitivity analysis problem. The lowest level of sensitivity of system outputs to system inputs is defined as an H_- index. A full characterization of the H_- index is given, first, in terms of matrix equalities and inequalities, and then in terms of linear matrix inequalities (LMIs), as a dual of the Bounded Real Lemma. A related problem of input observability is also studied, with new necessary and sufficient conditions given, which are necessary for a fault detection system to have a nonzero worst-case fault sensitivity. The above results are applied to the problem of fault detection filter analysis, with numerical examples given to show the effectiveness of the proposed approaches.     

Estimation of the dimension of chaotic dynamical systems using neural networks and robust location estimate

In this paper, we propose a new algorithm for the estimation of the dimension of chaotic dynamical systems using neural networks and robust location estimate.The basic idea is that a member of a time series can be optimally expressed as a deterministic function of the d past series values, where d is the dimension of the system. Moreover the neural networks’ learning ability is improved rapidly when the appropriate amount of information is provided to a neural structure which is as complex as needed.To estimate the dimension of a dynamical system, neural networks are trained to learn the component of the attractor expressed by a reconstructed vector in a suitable phase space whose embedding dimension m, has been estimated using the method of mutual information.     

The Determination of Stability Boundaries and Control Energy for a Finite Pulse Width Sampled-Data System†

Stability inequalities relating five system parameters have been derived using state apace analysis for n finite pulse sampled-data system. Further, these inequalities have been utilized to compute absolute stability boundaries in n parameter space and in particular their variation with pulse width and sampling period is described.

An expression for the control energy as a function of the state variables has been derived and computer results are presented which show the variation of the asymptotic value of control energy per interval with pulse width. It is further shown how points of inflection in the control energy per interval pulse width curve, may be utilized to minimize the sensitivity of control energy per interval to pulse width timing errors.     

Jackson-type inequalities for h-p clouds and error estimates

The interest in meshfree methods for solving boundary-value problems has grown rapidly in recent years. A meshless method that has attracted much interest in the community of computational mechanics is the h-p clouds method. For this kind of applications it is fundamental to analyze the orders of approximation. In this paper we prove Jackson-type inequalities for h-p cloud functions. These inequalities set up a general framework for the theoretical analysis of high order error estimates of the h-p clouds method, with the same remarkable features of finite element theory.     

Internal positivity preserved model reduction

This article studies model reduction of continuous-time stable positive linear systems under the Hankel norm, H _∞ norm and H ₂ norm performance. The reduced-order systems preserve the stability as well as the positivity of the original systems. This is achieved by developing new necessary and sufficient conditions of the model reduction performances in which the Lyapunov matrices are decoupled with the system matrices. In this way, the positivity constraints in the reduced-order model can be imposed in a natural way. As the model reduction performances are expressed in linear matrix inequalities with equality constraints, the desired reduced-order positive models can be obtained by using the cone complementarity linearisation iterative algorithm. A numerical example is presented to illustrate the effectiveness of the given methods.