Composite quadratic Lyapunov functions for constrained control systems

A Lyapunov function based on a set of quadratic functions is introduced in this paper. We call this Lyapunov function a composite quadratic function. Some important properties of this Lyapunov function are revealed. We show that this function is continuously differentiable and its level set is the convex hull of a set of ellipsoids. These results are used to study the set invariance properties of continuous-time linear systems with input and state constraints. We show that, for a system under a given saturated linear feedback, the convex hull of a set of invariant ellipsoids is also invariant. If each ellipsoid in a set can be made invariant with a bounded control of the saturating actuators, then their convex hull can also be made invariant by the same actuators. For a set of ellipsoids, each invariant under a separate saturated linear feedback, we also present a method for constructing a nonlinear continuous feedback law which makes their convex hull invariant.     

Absolute stability analysis of discrete-time systems with composite quadratic Lyapunov functions

A generalized sector bounded by piecewise linear functions was introduced in a previous paper for the purpose of reducing conservatism in absolute stability analysis of systems with nonlinearity and/or uncertainty. This paper will further enhance absolute stability analysis by using the composite quadratic Lyapunov function whose level set is the convex hull of a family of ellipsoids. The absolute stability analysis will be approached by characterizing absolutely contractively invariant (ACI) level sets of the composite quadratic Lyapunov functions. This objective will be achieved through three steps. The first step transforms the problem of absolute stability analysis into one of stability analysis for an array of saturated linear systems. The second step establishes stability conditions for linear difference inclusions and then for saturated linear systems. The third step assembles all the conditions of stability for an array of saturated linear systems into a condition of absolute stability. Based on the conditions for absolute stability, optimization problems are formulated for the estimation of the stability region. Numerical examples demonstrate that stability analysis results based on composite quadratic Lyapunov functions improve significantly on what can be achieved with quadratic Lyapunov functions.     

Global stability of relay feedback systems

For a large class of relay feedback systems (RFS) there will be limit cycle oscillations. Conditions to check existence and local stability of limit cycles for these systems are well known. Global stability conditions, however, are practically nonexistent. The paper presents conditions in the form of linear matrix inequalities (LMIs) that, when satisfied, guarantee global asymptotic stability of limit cycles induced by relays with hysteresis in feedback with linear time-invariant (LTI) stable systems. The analysis consists in finding quadratic surface Lyapunov functions for Poincare maps associated with RFS. These results are based on the discovery that a typical Poincare map induced by an LTI flow between two hyperplanes can be represented as a linear transformation analytically parametrized by a scalar function of the state. Moreover, level sets of this function are convex subsets of linear manifolds. The search for quadratic Lyapunov functions on switching surfaces is done by solving a set of LMIs. Although this analysis methodology yields only a sufficient criterion of stability, it has proved very successful in globally analyzing a large number of examples with a unique locally stable symmetric unimodal limit cycle. In fact, it is still an open problem whether there exists an example with a globally stable symmetric unimodal limit cycle that could not be successfully analyzed with this new methodology. Examples analyzed include minimum-phase systems, systems of relative degree larger than one, and of high dimension. Such results lead us to believe that globally stable limit cycles of RFS frequently have quadratic surface Lyapunov functions     

On control system design using random samples of contractive block Toeplitz matrices

Contractive lower triangular block Toeplitz (LTBT) matrices have recently been parametrized in a closed-form by a sequence of unstructured contractive matrices. A problem naturally arises between uniform samplings over contractive LTBT matrices and their matrix parameters, that is, which one is more appropriate in control system analysis and synthesis. This paper makes it clear that uniform sampling over matrix parameters results in a contractive LTBT matrix with a more rapid concentration speed towards the boundary of the uncertainty set. This is not an attractive property in control-related problems. Numerical comparisons also show that uniform sampling directly over contractive LTBT matrices outperforms that over the matrix parameter set in robustness analysis for a closed-loop system, as well as in strongly stable closed-loop system design with complexity constraints on the controller. As a sacrifice, uniform sampling over contractive LTBT matrices is more mathematically complicated and more time consuming.     

A Switching Anti-windup Design Using Multiple Lyapunov Functions

This technical note proposes a switching anti-windup design, which aims to enlarge the domain of attraction of the closed-loop system. Multiple anti-windup gains along with an index function that orchestrates the switching among these anti-windup gains are designed based on the min function of multiple quadratic Lyapunov functions. In comparison with the design of a single anti-windup gain which maximizes a contractively invariant level set of a single quadratic Lyapunov function as a way to enlarge the domain of attraction, the use of multiple Lyapunov functions and switching in the proposed design allows the union of the level sets of the multiple Lyapunov functions, each of which is not necessarily contractively invariant, to be contractively invariant and within the domain of attraction. As a result, the resulting domain of attraction is expected to be significantly larger than the one resulting from a single anti-windup gain and a single Lyapunov function. Indeed, simulation results demonstrate such a significant improvement.      

Componentwise ultimate bound and invariant set computation for switched linear systems

We present a novel ultimate bound and invariant set computation method for continuous-time switched linear systems with disturbances and arbitrary switching. The proposed method relies on the existence of a transformation that takes all matrices of the switched linear system into a convenient form satisfying certain properties. The method provides ultimate bounds and invariant sets in the form of polyhedral and/or mixed ellipsoidal/polyhedral sets, is completely systematic once the aforementioned transformation is obtained, and provides a new sufficient condition for practical stability. We show that the transformation required by our method can easily be found in the well-known case where the subsystem matrices generate a solvable Lie algebra, and we provide an algorithm to seek such transformation in the general case. An example comparing the bounds obtained by the proposed method with those obtained from a common quadratic Lyapunov function computed via linear matrix inequalities shows a clear advantage of the proposed method in some cases.     

On invariant sets and closed‐loop boundedness of Lure‐type nonlinear systems by LPV‐embedding

We address the problem of achieving trajectory boundedness and computing ultimate bounds and invariant sets for Lure‐type nonlinear systems with a sector‐bounded nonlinearity. Our first contribution is to compare two systematic methods to compute invariant sets for Lure systems. In the first method, a linear‐like bound is considered for the nonlinearity, and this bound is used to compute an invariant set by regarding the nonlinear system as a linear system with a nonlinear perturbation. In the second method, the sector‐bounded nonlinearity is treated as a time‐varying parameterised linear function with bounded parameter variations, and then invariant sets are computed by embedding the nonlinear system into a convex polytopic linear parameter varying (LPV) system. We show that under some conditions on the system matrices, these approaches give identical invariant sets, the LPV‐embedding method being less conservative in the general case. The second contribution of the paper is to characterise a class of Lure systems, for which an appropriately designed linear state feedback achieves bounded trajectories of the closed‐loop nonlinear system and allows for the computation of an invariant set via a simple, closed‐form expression. The third contribution is to show that, for disturbances that are ‘aligned’ with the control input, arbitrarily small ultimate bounds on the system states can be achieved by assigning the eigenvalues of the linear part of the system with ‘large enough’ negative real part. We illustrate the results via examples of a pendulum system, a Josephson junction circuit and the well‐known Chua circuit. Copyright © 2015 John Wiley & Sons, Ltd.     

Stability analysis of bilinear systems under aperiodic sampled-data control

This note considers the problem of local stability of bilinear systems with aperiodic sampled-data linear state feedback control. The sampling intervals are time-varying and upper bounded. It is shown that the feasibility of some linear matrix inequalities (LMIs), implies the local asymptotic stability of the sampled-data system in an ellipsoidal region containing the equilibrium. The method is based on the analysis of contractive invariant sets, and it is inspired by the dissipativity theory. The results are illustrated by means of numerical examples.     

Piecewise-affine Lyapunov functions for discrete-time linear systems with saturating controls

This paper is concerned with piecewise-affine (PWA) functions as Lyapunov function candidates for stability analysis of time-invariant discrete-time linear systems with saturating closed-loop control inputs. Using a PWA model of saturating closed-loop system, new necessary and sufficient conditions for a PWA function be a Lyapunov function are presented. Based on linear programming formulation of these conditions, an effective algorithm is proposed for construction of such Lyapunov functions for estimation of the region of local asymptotic stability. Compared to piecewise-linear functions, like Minkowski functions, PWA functions are more adequate to capture the dynamical effects of saturation nonlinearities, giving strictly less conservative results. The complexity of the proposed approach is polynomial in state dimension and exponential in saturating control dimension, being hence appropriate for problems with large state dimension but with few saturating inputs.     

Gain-scheduled control of time-varying delay systems with input constraint

This paper investigates gain-scheduled control design for linear systems with time-varying state delays subject to actuator saturation and external disturbance. Assuming the disturbance is peak bounded, a sufficient delay-dependent condition is established to guarantee that a family of level sets, corresponding to a novel parameter-dependent Lyapunov–Krasovskii functional, are nested and invariant to the closed-loop system. The invariant sets are then used to obtain nested reachable sets (ellipsoids) to bound the closed-loop states. A family of continuous controllers are designed based on these nested ellipsoids. The controller with the best performance is selected, each time, based on the closed-loop state vector, while complying with the saturation bound, and the resulting closed-loop system is locally input-to-state stable. All conditions are represented in the form of linear matrix inequalities (LMIs) by the linear spline method. Finally, the benefit of the control method is illustrated by two examples.     

时变不确定系统鲁棒控制器设计的新方法

基于包含两个二次项的分段Lyapunov函数,研究了线性时变不确定系统的鲁棒控制器设计问题.所考虑的系统由两个矩阵的凸组合构成,通过引入一个附加矩阵,推导出鲁棒控制器存在的充分条件.该控制器的状态反馈增益的求解问题可以转化为一组带有两个比例参数的线性矩阵不等式的凸优化问题.最后的数值示例说明了该设计方法的可行性.     

On the computation of convex robust control invariant sets for nonlinear systems

In this paper we provide a method to compute robust control invariant sets for nonlinear discrete-time systems. A simple criterion to evaluate if a convex set in state space is a robust control invariant set for a nonlinear uncertain system is presented. The criterion is employed to design an algorithm for computing a polytopic robust control invariant set. The method is based on the properties of DC functions, i.e. functions which can be expressed as the difference of two convex functions. Since the elements of a wide class of nonlinear functions have DC representation or, at least, admit an arbitrarily close approximation, the method is quite general. The algorithm requires relatively low computational resources.     

Computation of Feasible and Invariant Sets for Interpolation-based MPC

The terminal invariant set plays a key role in the stabilizing MPC (Model Predictive Control) formulation. When control gains of the terminal local control laws and corresponding feasible and invariant sets are given, the existing interpolation methods unite them to enlarge the stabilizable region and enhance performance. In this paper, when an invariant set is given, an algorithm is proposed to find another invariant set such that their convex hull is maximized and also invariant. Numerical examples show that the set of the stabilizable initial state of the MPC is enlarged by the terminal constraint set computed by an interpolation-based approach.

     

基于分段连续Lyapunov函数的采样系统鲁棒保性能控制

针对不确定采样控制系统的鲁棒保性能控制问题,首先将采样系统描述为跳变线性系统,基于矩阵凸组合思想构造了分段连续Lyapunov函数,进而在线性矩阵不等式框架内给出了不确定采样系统鲁棒稳定的条件.针对范数有界参数不确定采样系统,提出了鲁棒保性能控制器设计的在线算法,在每个采样周期内通过求解一组线性矩阵不等式的可行解来构造出状态反馈增益矩阵.最后的仿真算例验证了所提设计方法的有效性.     

Maximizing the set of recurrent states of an MDP subject to convex constraints

This paper focuses on the design of time-homogeneous fully observed Markov decision processes (MDPs), with finite state and action spaces. The main objective is to obtain policies that generate the maximal set of recurrent states, subject to convex constraints on the set of invariant probability mass functions. We propose a design method that relies on a finitely parametrized convex program inspired on principles of entropy maximization. A numerical example is provided to illustrate these ideas.     

The Ellipsoidal Invariant Set of Fractional Order Systems Subject to Actuator Saturation: The Convex Combination Form

The domain of attraction of a class of fractional order systems subject to saturating actuators is investigated in this paper. We show the domain of attraction is the convex hull of a set of ellipsoids. In this paper, the Lyapunov direct approach and fractional order inequality are applied to estimating the domain of attraction for fractional order systems subject to actuator saturation. We demonstrate that the convex hull of ellipsoids can be made invariant for saturating actuators if each ellipsoid with a bounded control of the saturating actuators is invariant. The estimation on the contractively invariant ellipsoid and construction of the continuous feedback law are derived in terms of linear matrix inequalities (LMIs). Two numerical examples illustrate the effectiveness of the developed method.      

Blob Metamorphosis based on Minkowski Sums

This paper addresses the metamorphosis of soft objects built from skeletons. We propose a new approach that may be split into three steps. The first step consists in an original splitting of the initial and the final shapes with a view to creating a bijective graph of correspondence. In the second step, we assume that the skeletons are convex polygonal shapes, and thus take advantage of the properties of Minkowski sums to characterize the skeletons of intermediate shapes. Eventually, we characterize the intermediate distance and field functions; we describe a set of interpolation methods and propose to use a restricted class of parametrized distance and field functions so as to preserve coherence and speed-up computations. We show that we can easily extend those results to achieve a Bézier like metamorphosis where control points are replaced by control soft objects; in this scope, we have adapted existing accelerated techniques that build a Bézier transformation from a set of convex polyhedra to any kind of convex polygonal shapes. Eventually, we point out that matching all components of the initial and the final shapes generates amorphous intermediate shapes based on an overwhelming number of intermediate sub-components. Thus, we propose heuristics with a view to preserving coherence during the transformation and accelerating computations. We have implemented and tested our techniques in an experimental ray-tracer.     

Enlarging the domain of attraction of MPC controllers

This paper presents a method for enlarging the domain of attraction of nonlinear model predictive control (MPC). The usual way of guaranteeing stability of nonlinear MPC is to add a terminal constraint and a terminal cost to the optimization problem such that the terminal region is a positively invariant set for the system and the terminal cost is an associated Lyapunov function. The domain of attraction of the controller depends on the size of the terminal region and the control horizon. By increasing the control horizon, the domain of attraction is enlarged but at the expense of a greater computational burden, while increasing the terminal region produces an enlargement without an extra cost.In this paper, the MPC formulation with terminal cost and constraint is modified, replacing the terminal constraint by a contractive terminal constraint. This constraint is given by a sequence of sets computed off-line that is based on the positively invariant set. Each set of this sequence does not need to be an invariant set and can be computed by a procedure which provides an inner approximation to the one-step set. This property allows us to use one-step approximations with a trade off between accuracy and computational burden for the computation of the sequence. This strategy guarantees closed loop-stability ensuring the enlargement of the domain of attraction and the local optimality of the controller. Moreover, this idea can be directly translated to robust MPC.