On the estimation of the domain of attraction for discrete-time switched and hybrid nonlinear systems

This paper addresses the estimation of the domain of attraction for discrete-time nonlinear systems where the vector field is subject to changes. First, the paper considers the case of switched systems, where the vector field is allowed to arbitrarily switch among the elements of a finite family. Second, the paper considers the case of hybrid systems, where the state space is partitioned into several regions described by polynomial inequalities, and the vector field is defined on each region independently from the other ones. In both cases, the problem consists of computing the largest sublevel set of a Lyapunov function included in the domain of attraction. An approach is proposed for solving this problem based on convex programming, which provides a guaranteed inner estimate of the sought sublevel set. The conservatism of the provided estimate can be decreased by increasing the size of the optimisation problem. Some numerical examples illustrate the proposed approach.     

Intelligent control of nonlinear systems with application to chemical reactor recycle

The dynamic surface control technique can simplify the backstepping design for the control of nonlinear systems by overcoming the problem of “explosion of complexity.” In this paper, we incorporate this design technique into a neural network-based adaptive control design framework for a class of nonlinear stochastic systems. The time delays exist in the gain of the stochastic disturbance in the systems, and the neural networks are employed to compensate for all unknown nonlinear terms depending on the delayed output. The proposed approach is able to eliminate the problem of “explosion of complexity” inherent in the existing method. It can be proven that all the signals are semi-globally uniformly ultimately bounded in probability, and the system output tracks the reference signal to a bounded compact set. A simulation example is given to verify the effectiveness of the proposed approach.     

New results on stabilization of Markovian jump systems with time delay

This paper studies the problem of stochastic stabilization for a class of Markovian jump systems with time delay. A new delay-dependent stochastic stability criterion on the stochastic stability of the system is derived based on a novel Lyapunov-Krasovskii functional (LKF) approach. The equivalence and superiority to existing results are demonstrated. Then a state feedback controller, which guarantees the stochastic stability of the closed-loop system, is designed. Illustrative examples are provided to show the reduced conservatism and effectiveness of the proposed techniques.     

A Bayesian perspective on stochastic neurocontrol.

Control design for stochastic uncertain nonlinear systems is traditionally based on minimizing the expected value of a suitably chosen loss function. Moreover, most control methods usually assume the certainty equivalence principle to simplify the problem and make it computationally tractable. We offer an improved probabilistic framework which is not constrained by these previous assumptions, and provides a more natural framework for incorporating and dealing with uncertainty. The focus of this paper is on developing this framework to obtain an optimal control law strategy using a fully probabilistic approach for information extraction from process data, which does not require detailed knowledge of system dynamics. Moreover, the proposed control method framework allows handling the problem of input-dependent noise. A basic paradigm is proposed and the resulting algorithm is discussed. The proposed probabilistic control method is for the general nonlinear class of discrete-time systems. It is demonstrated theoretically on the affine class. A nonlinear simulation example is also provided to validate theoretical development.     

Robust H ∞ control for a class of uncertain mechanical systems

In this article, the problem of H _∞ control is investigated for a class of mechanical systems with input delay and parameter uncertainties which appear in all the mass, damping and stiffness matrices. Two approaches, norm-bounded and linear fractional transformation (LFT) uncertainty formulations, are considered. By using a new Lyapunov–Krasovskii functional approach, combined with the advanced techniques for achieving delay dependence, improved robust H _∞ state-feedback controller design methods are developed. The existence condition for admissible controllers is formulated in the form of linear matrix inequalities (LMIs), and the controller design is cast into a convex optimisation problem subject to LMI constraints. If the optimisation problem is solvable, a desired controller can be readily constructed. The result for the norm-bounded uncertainty case improves the existing ones in terms of design conservatism, and that for the LFT uncertainty case represents the first attempt in this direction. An illustrative example is provided to show the effectiveness and advantage of the proposed controller design methodologies.     

Robust extended dissipative control for sampled-data Markov jump systems

This paper investigates the problem of the sampled-data extended dissipative control for uncertain Markov jump systems. The systems considered are transformed into Markov jump systems with polytopic uncertainties and sawtooth delays by using an input delay approach. The focus is on the design of a mode-independent sampled-data controller such that the resulting closed-loop system is mean-square exponentially stable with a given decay rate and extended dissipative. A novel exponential stability criterion and an extended dissipativty condition are established by proposing a new integral inequality. The reduced conservatism of the criteria is demonstrated by two numerical examples. Furthermore, a sufficient condition for the existence of a desired mode-independent sampled-data controller is obtained by solving a convex optimisation problem. Finally, a resistance, inductance and capacitance (RLC) series circuit is employed to illustrate the effectiveness of the proposed approach.     

Combining model reference adaptive controllers and stochastic self-tuning regulators

The similarities between model reference adaptive controllers (MRAC) for minimum phase plants where the control objectives are specified by reference models and stochastic self-tuning regulators (S-STURE) for minimum phase plants where the control objectives are specified by autoregressive moving average (ARMA) stochastic models are investigated.These similarities permit the extension, for these two classes of adaptive systems, of the duality existing in the linear case with known parameters between modal control and minimum variance control. The convergence analysis of these schemes in a combined deterministic and stochastic environment allows one finally to define schemes which behave as a desired MRAC in a deterministic environment and as a desired S-STURE in a stochastic environment. The problem of removing the positive real conditions for convergence in deterministic and stochastic environment is also discussed.     

A stochastic games framework for verification and control of discrete time stochastic hybrid systems

We describe a framework for analyzing probabilistic reachability and safety problems for discrete time stochastic hybrid systems within a dynamic games setting. In particular, we consider finite horizon zero-sum stochastic games in which a control has the objective of reaching a target set while avoiding an unsafe set in the hybrid state space, and a rational adversary has the opposing objective. We derive an algorithm for computing the maximal probability of achieving the control objective, subject to the worst-case adversary behavior. From this algorithm, sufficient conditions of optimality are also derived for the synthesis of optimal control policies and worst-case disturbance strategies. These results are then specialized to the safety problem, in which the control objective is to remain within a safe set. We illustrate our modeling framework and computational approach using both a tutorial example with jump Markov dynamics and a practical application in the domain of air traffic management.     

Sequential probabilistic analytical target cascading method for hierarchical multilevel optimization under uncertainty

Probabilistic Analytical Target Cascading (PATC) is a methodology for hierarchical multilevel optimization under uncertainty. In PATC, the statisticalmoments of the stochastic interrelated responses are matched between neighbouring levels to ensure the consistency of the solution. When the interrelated response is far from normal distribution, high order moments may need to be matched in the PATC formulation, which results in great computational difficulty. To overcome this disadvantage, a sequential PATC (SPATC) approach is proposed in this paper. SPATC firstly decouples the original probabilistic design problem into deterministic optimization problem and probabilistic analysis, and then hierarchically decomposes them into subproblems. The statistical information matching between neighbouring levels in the existing PATC framework is eliminated in SPATC. All in one probabilistic analysis and hierarchical probabilistic analysis are established to calculate the probabilistic characteristic of the interrelated responses and linking variables. Three examples are used to demonstrate the effectiveness and efficiency of the proposed SPATC approach. The results show that the SPATC approach is more efficient and accurate than PATC, especially for the multilevel design problem with non-normal interrelated responses.     

CAD based shape optimization for gas turbine component design

In order to improve product characteristics, engineering design makes increasing use of Robust Design and Multidisciplinary Design Optimisation. Common to both methodologies is the need to vary the object’s shape and to assess the resulting change in performance, both executed within an automatic loop. This shape change can be realised by modifying the parameter values of a suitably parameterised Computer Aided Design (CAD) model. This paper presents the adopted methodology and the achieved results when performing optimisation of a gas turbine disk. Our approach to hierarchical modelling employing design tables is presented, with methods to ensure satisfactory geometry variation by commercial CAD systems. The conducted studies included stochastic and probabilistic design optimisation. To solve the multi-objective optimisation problem, a Pareto optimum criterion was used. The results demonstrate that CAD centric approach enables significant progress towards automating the entire process while achieving a higher quality product with the reduced susceptibility to manufacturing imperfections.     

Probabilistic reachability and safety for controlled discrete time stochastic hybrid systems

In this work, probabilistic reachability over a finite horizon is investigated for a class of discrete time stochastic hybrid systems with control inputs. A suitable embedding of the reachability problem in a stochastic control framework reveals that it is amenable to two complementary interpretations, leading to dual algorithms for reachability computations. In particular, the set of initial conditions providing a certain probabilistic guarantee that the system will keep evolving within a desired ‘safe’ region of the state space is characterized in terms of a value function, and ‘maximally safe’ Markov policies are determined via dynamic programming. These results are of interest not only for safety analysis and design, but also for solving those regulation and stabilization problems that can be reinterpreted as safety problems. The temperature regulation problem presented in the paper as a case study is one such case.     

Improved dynamic output feedback RMPC for linear uncertain systems with input constraints

In this work, we propose a dynamic output feedback robust model predictive control (RMPC) design method for linear uncertain systems with input constraints. In order to handle the input constraints, the control signals are permitted to saturate, which can fully utilize the capability of actuators and thus can reduce the conservatism. For the unavailable states, an ellipsoidal set is used to obtain an estimation, and it is updated at every time instant. A modified RMPC design requirement is used to ensure the recursive feasibility of the optimization problem. Then, the design method is formulated in terms of a convex optimization problem with linear matrix inequality constraints. The proposed output feedback RMPC design method is expected to further reduce the conservativeness. The improvements of the proposed algorithm over the other existing techniques is demonstrated by an example. Copyright © 2015 John Wiley & Sons, Ltd.     

Centroid-based memetic algorithm – adaptive Lamarckian and Baldwinian learning

The application of specific learning schemes in memetic algorithms (MAs) can have significant impact on their performances. One main issue revolves around two different learning schemes, specifically, Lamarckian and Baldwinian. It has been shown that the two learning schemes are better suited for different types of problems and some previous studies have attempted to combine both learning schemes as a means to develop a single optimisation framework capable of solving more classes of problems. However, most of the past approaches are often implemented heuristically and have not investigated the effect of different learning scheme on noisy design optimisation. In this article, we introduce a simple probabilistic approach to address this issue. In particular, we investigate a centroid-based approach that combines the two learning schemes within an MA framework (centroid-based MS; CBMA) through the effective allocation of resources (in terms of local search cost) that are based on information obtained during the optimisation process itself. A scheme that applies the right learning scheme (Lamarckian or Baldwinian) at the right time (during search) would lead to higher search performance. We conducted an empirical study to test this hypothesis using two different types of benchmark problems. The first problem set consists of simple benchmark problems whereby the problem landscape is static and gradient information can be obtained accurately. These problems are known to favour Lamarckian learning while Baldwinian learning is known to exhibit slower convergence. The second problem set consists of noisy versions of the first problem set whereby the problem landscape is dynamic as a result of the random noise perturbation injected into the design vector. These problems are known to favour learning processes that re-sample search points such as Baldwinian learning. Our experiments show that CBMA manages to adaptively allocate resources productively according to problem in most of the cases.     

A modified model reference adaptive control scheme for improvedtransient performance

The author proposes a modified model reference adaptive control (MRAC) scheme aimed at improving the transient performance of adaptive systems while maintaining the ideal asymptotic properties possessed by standard MRAC. In the modified scheme, the estimation error, which is generated by the identification scheme, is used directly as a control signal to counteract errors resulting from the certainty equivalence design. It is shown that the modified scheme provides essentially the same stability and robustness properties as the standard adaptive MRAC approach but with the transient performance substantially improved. Simulation results are presented to illustrate the effectiveness of the proposed scheme     

Probabilistic‐constrained filtering for a class of nonlinear systems with improved static event‐triggered communication

This paper is concerned with the probabilistic‐constrained filtering problem for a class of time‐varying systems with stochastic nonlinearities and state constraints. An improved static event‐triggering scheme is used to reduce unnecessary signal transmissions on the communication channel, where a time‐varying triggering parameter is designed according to engineering practice. The aim of the problem addressed is to design a time‐varying filter such that (1) the prescribed probabilistic constraints on the estimation error are satisfied (ie, the probability for the estimation error to be confined to the given ellipsoidal set is larger than a prescribed value) and (2) the ellipsoid is minimized at each time instant in the sense of the matrix norm. First, the probabilistic constraints are handled by means of the multidimensional Chebyshev bounds. By using recursive matrix inequalities, stochastic analysis is conducted to establish sufficient conditions for the existence of the desired probabilistic‐constrained filter. Then, a recursive optimization algorithm is proposed to design the filter gain matrices. Finally, a simulation example is proposed to demonstrate the effectiveness and applicability of the proposed method.     

Fault detection filter design for stochastic time-delay systems with sensor faults

This article considers the fault detection (FD) problem for a class of Itô-type stochastic time-delay systems subject to external disturbances and sensor faults. The main objective is to design a fault detection filter (FDF) such that it has prescribed levels of disturbance attenuation and fault sensitivity. Sufficient conditions for guaranteeing these levels are formulated in terms of linear matrix inequalities (LMIs), and the corresponding fault detection filter design is cast into a convex optimisation problem which can be efficiently handled by using standard numerical algorithms. In order to reduce the conservatism of filter design with mixed objectives, multi-Lyapunov functions approach is used via Projection Lemma. In addition, it is shown that our results not only include some previous conditions characterising H _∞ performance and H _? performance defined for linear time-invariant (LTI) systems as special cases but also improve these conditions. Finally, two examples are employed to illustrate the effectiveness of the proposed design scheme.     

An efficient framework for optimal robust stochastic system design using stochastic simulation

The knowledge about a planned system in engineering design applications is never complete. Often, a probabilistic quantification of the uncertainty arising from this missing information is warranted in order to efficiently incorporate our partial knowledge about the system and its environment into their respective models. This leads to a robust stochastic design framework where probabilistic models of excitation uncertainties and system modeling uncertainties can be introduced; the design objective is then typically related to the expected value of a system performance measure, such as reliability or expected life-cycle cost. For complex system models, this expected value can rarely be evaluated analytically and so it is often calculated using stochastic simulation techniques, which involve an estimation error and significant computational cost. An efficient framework, consisting of two stages, is presented here for the optimization in such robust stochastic design problems. The first stage implements a novel approach, called stochastic subset optimization (SSO), for iteratively identifying a subset of the original design space that has high plausibility of containing the optimal design variables. The second stage adopts some other stochastic optimization algorithm to pinpoint the optimal design variables within that subset. The focus is primarily on the theory and implementation issues for SSO but also on topics related to the combination of the two different stages for overall enhanced efficiency. An illustrative example is presented that shows the efficiency of the proposed methodology; it considers the optimization of the reliability of a base-isolated structure considering future near-fault ground motions.     

H ∞ output tracking control for delta operator systems with insensitivity to controller coefficient variations

The problem of designing insensitive H _∞ output tracking controller for discrete-time systems is studied in a unified framework by using the delta operator approach. A type of coefficient sensitivity performance measure is first developed tomeasure the sensitivity of the transfer function with respect to controller coefficient variations, and the corresponding controller design problem is naturally reformulated as a mixed-objective H _∞ control problem. It is worth mentioning that a novel bounded real lemma (BRL), i.e. Theorem 1 in this article, for delta operator systems is obtained, which is adapted to deal with the multi-objective optimisation problem or polytopic uncertain systems in a potentially less conservative framework. Then, based on the novel lemma, an linear matrix inequality (LMI)-based design method is proposed for achieving the output tracking purpose. Finally, a numerical example based on the linearised model of F-18 aircraft is given to illustrate the effectiveness of the proposed method.     

Control of discrete‐time periodic linear systems with input saturation via multi‐step periodic invariant sets

This paper studies local control of discrete‐time periodic linear systems subject to input saturation by using the multi‐step periodic invariant set approach. A multi‐step periodic invariant set refers to a set from which all trajectories will enter a periodic invariant set after finite steps, remain there forever, and eventually converge to the origin as time approaches infinity. The problems of (robust) estimation of the domain of attraction, (robust) local stabilization (with bounded uncertainties), and disturbance rejection are considered. Compared with the conventional periodic invariant set approach, which has been used in the literature for local stability analysis and stabilization of discrete‐time periodic linear systems subject to input saturation, this new invariant set approach is capable of significantly reducing the conservatism by introducing additional auxiliary variables in the set invariance conditions. Moreover, the new approach allows to design (robust) stabilizing periodic controller, in the presence of norm bounded uncertainties, whose period is the same as the open‐loop system and is different from the existing periodic enhancement approach by which the period of the controller is multiple times of the period of the open‐loop system. Several numerical examples are worked out to show the effectiveness of the proposed approach. Copyright © 2013 John Wiley & Sons, Ltd.