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In this paper, an adaptive robust boundary controller is designed for an unstable heat equation with external disturbance flowing into the control end. The disturbance and uncertainty effect is cancelled out in the closed-loop system by using on-line approximation of disturbance upper-bound provided as an output of adaptive robust update law. The asymptotic stability of the controlled system in the presence of unknown disturbance and/or parametric uncertainties is proved utilising the Lyapunov theorem. Unlike previous researches, the control implementation does not require prior knowledge of unknown disturbance. Moreover, the controller does not require measuring the system states or estimating the system states by an observer. The adaptive robust controller can be regarded as a combination of the best qualities of the adaptive controller and the robust controller with no prior knowledge of system uncertainty, and asymptotic tracking error performance. Numerical simulations and comparisons are provided to illustrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

This paper proposes a dynamic output feedback robust model predictive control for system with polytopic model uncertainty. Earlier researches on this topic utilise iterative methods to solve the non-convex optimisation problem which are computationally demanding. In order to reduce the computational burden, we explore a new approach in this paper, where, by utilising some proper matrix transformations, a computationally more efficient but conservative convex optimisation problem is formulated which can be solved in terms of linear matrix inequalities. Furthermore, we try to reduce the conservativeness by introducing a nonsingular matrix as a degree of freedom. The recursive feasibility and the convergence of the augmented state to the equilibrium point are guaranteed. The effectiveness of the proposed approach is illustrated by two numerical examples. [ABSTRACT FROM AUTHOR]

This paper is concerned with the robust stabilsation of uncertain delayed Markovian jump systems. Given a Markovian jump system with time delay and Brownian motion simultaneously, we allow the uncertainty added in the form of additive perturbations and existing in the drift and diffusion sections at the same time. A sufficient condition on the mean square stability of system in the face of such disturbances is obtained, which is similar to small-gain theorem. A kind of partially delay-dependent controller stabilising the resulting closed-loop system is firstly designed to relate to the probability distribution of delay, whose key idea is applied to construct a delayed controller with disordering phenomenon. It is seen that the existence conditions established here could be solved easily. Based on the proposed results, some applications on robust synchronisation of uncertain delayed multi-agent systems with Markovian switching are considered. It is shown that the robust synchronisation of such an uncertain multi-agent network could be achieved by a protocol that each controller being partially delay-dependent or disordering could robustly stabilise a given single Markovian jump system. As for these cases, the proposed protocols could be obtained by solving certain algebraic Riccati equations and inequalities, which also involve weighting factors and depend on the eigenvalues of the Laplacian graph. [ABSTRACT FROM AUTHOR]

Output containment problem for high-order nonlinear time-invariant multi-agent systems in directed networks is investigated in this paper. The output is related with the observation matrix. The dimensions of observation matrix are extended so that it is non-singular. Then the containment problem is transformed into stability problem. The model of each agent is constructed by a nominal system combined with uncertainties. A robust controller, which includes a nominal controller and a robust compensator, is proposed to achieve output containment and restrain external uncertainties. The nominal controller is based on the output feedback and the nominal system constructed by the nominal controller contains desired containment properties. The robust compensator design is based on robust signal compensation technology for restraining the effects of external disturbances. A sufficient condition on the output containment is proposed and the containment errors can be made as small as desired with the expected convergence rate. Finally, numerical simulation is presented to demonstrate the effectiveness of the control method. [ABSTRACT FROM AUTHOR]

In this work, we present a novel continuous robust controller for a class of multi-input/multi-output nonlinear systems that contains unstructured uncertainties in their drift vectors and input matrices. The proposed controller compensates uncertainties in the system dynamics and achieves asymptotic tracking while requiring only the knowledge of the sign of the leading principal minors of the input gain matrix. A Lyapunov-based argument backed up with an integral inequality is applied to prove the asymptotic stability of the closed-loop system. Simulation results are presented to illustrate the viability of the proposed method. [ABSTRACT FROM AUTHOR]

This paper is concerned with passivity and robust synchronisation of switched coupled neural networks with uncertain parameters. First, the mathematical model of switched coupled neural networks with interval uncertain parameters is established, which consists ofLmodes and switches from one mode to another according to the switching rule. Second, by employing passivity theory and linear matrix inequality techniques, delay-independent and delay-dependent conditions are derived to guarantee the passivity of switched interval coupled neural networks. Moreover, based on the proposed passivity results, global synchronisation criteria can be obtained for switched coupled neural networks with or without uncertain parameters. Finally, an illustrative example is provided to demonstrate the effectiveness of the obtained results. [ABSTRACT FROM AUTHOR]

The problem of decentralised robust stabilisation is considered for a class of uncertain large-scale time-delay interconnected dynamical systems. In the paper, the upper bounds of delayed state perturbations, uncertainties, interconnection terms, and external disturbances are assumed to be completely unknown, and the delays are assumed to be any non-negative constants. For such a class of uncertain large-scale time-delay interconnected systems, a new method is presented whereby a class of adaptation-free decentralised local robust state feedback controllers can be constructed. In addition, it is also shown that the solutions of uncertain large-scale time-delay interconnected systems can be guaranteed to be uniformly ultimately bounded. Finally, as an application to the practical mechanical systems, some simulations of a numerical example are provided to demonstrate the validity of the theoretical results. [ABSTRACT FROM AUTHOR]

For a class of linear discrete-time uncertain systems, a feedback feed-forward iterative learning control (ILC) scheme is proposed, which is comprised of an iterative learning controller and two current iteration feedback controllers. The iterative learning controller is used to improve the performance along the iteration direction and the feedback controllers are used to improve the performance along the time direction. First of all, the uncertain feedback feed-forward ILC system is presented by an uncertain two-dimensional Roesser model system. Then, two robust control schemes are proposed. One can ensure that the feedback feed-forward ILC system is bounded-input bounded-output stable along time direction, and the other can ensure that the feedback feed-forward ILC system is asymptotically stable along time direction. Both schemes can guarantee the system is robust monotonically convergent along the iteration direction. Third, the robust convergent sufficient conditions are given, which contains a linear matrix inequality (LMI). Moreover, the LMI can be used to determine the gain matrix of the feedback feed-forward iterative learning controller. Finally, the simulation results are presented to demonstrate the effectiveness of the proposed schemes. [ABSTRACT FROM PUBLISHER]

Most existing works on data stream classification assume the streaming data is precise and definite. Such assumption, however, does not always hold in practice, since data uncertainty is ubiquitous in data stream applications due to imprecise measurement, missing values, privacy protection, etc. The goal of this paper is to learn accurate decision tree models from uncertain data streams for classification analysis. On the basis of very fast decision tree (VFDT) algorithms, we proposed an algorithm for constructing anuncertainVFDTtree withclassifiers at tree leaves (uVFDTc). The uVFDTc algorithm can exploit uncertain information effectively and efficiently in both the learning and the classification phases. In the learning phase, it uses Hoeffding bound theory to learn from uncertain data streams and yield fast and reasonable decision trees. In the classification phase, at tree leaves it uses uncertain naive Bayes (UNB) classifiers to improve the classification performance. Experimental results on both synthetic and real-life datasets demonstrate the strong ability of uVFDTc to classify uncertain data streams. The use of UNB at tree leaves has improved the performance of uVFDTc, especially the any-time property, the benefit of exploiting uncertain information, and the robustness against uncertainty. [ABSTRACT FROM PUBLISHER]

This paper studies the design of control systems subject to plant uncertainties and data losses in the channel connecting the plant sensor with the controller. The controller design has two main objectives. The first one is to robustify the control law against plant uncertainties. The other one is to achieve good performance by minimising the variance of the error signal. Data losses are modelled as an independent and identically distributed sequence of Bernoulli random variables. For analysis and design, this random variable is replaced by an additive noise plus gain channel model. To cope with structural uncertainties in the model of the plant, anH∞control technique is employed. The controller is synthesised in order to make the closed-loop system robust against structural uncertainties of the nominal model, while achieving optimal performance of the system in the presence of dropouts. [ABSTRACT FROM AUTHOR]

In this paper, the problem of state estimation for linear systems with bounded parametric uncertainties and quantised measurements is addressed. The quantised measurements are random variables of which the probability density functions (PDF) are closely related to the uncertain system parameters. Thus the statistical information of the quantised measurements and the deterministic bounds of the parametric uncertainties need to be incorporated in the design. Based on this, a robust estimator is proposed to minimise the worst-case expectation of the regularised residual norm over all possible parametric uncertainties. An iterative algorithm is presented to obtain the optimal solution. Simulation results are provided to show the effectiveness of the proposed estimator. [ABSTRACT FROM PUBLISHER]

Gaze-steering is often used to broaden the viewing range of an active vision system. Gaze-steering procedures are usually based on estimated parameters such as image position, image velocity, depth and camera calibration parameters. However, there may be uncertainties in these estimated parameters because of measurement noise and estimation errors. In this case, robust gaze-steering cannot be guaranteed. To compensate for such problems, this paper proposes a gaze-steering method based on a linear matrix inequality (LMI). In this method, we first propose a proportional derivative (PD) control scheme on the unit sphere that does not use depth parameters. This proposed PD control scheme can avoid uncertainties in the estimated depth and camera calibration parameters, as well as inconveniences in their estimation process, including the use of auxiliary feature points and highly non-linear computation. Furthermore, the control gain of the proposed PD control scheme on the unit sphere is designed using LMI such that the designed control is robust in the presence of uncertainties in the other estimated parameters, such as image position and velocity. Simulation results demonstrate that the proposed method provides a better compensation for uncertainties in the estimated parameters than the contemporary linear method and steers the gaze of the camera more steadily over time than the contemporary non-linear method. [ABSTRACT FROM PUBLISHER]

In this paper, we investigate the mixedH2/H∞robust model predictive control (RMPC) for polytopic uncertain systems, which refers to the infinite horizon optimal guaranteed cost control (OGCC). To fully use the capability of actuators, we adopt a saturating feedback control law as the control strategy of RMPC. As the saturating feedback control law can be effectively represented by the convex hull of a group of auxiliary linear feedback laws, the auxiliary feedback laws allow us to design the actual feedback control law without consideration of the input constraints directly to achieve the improved performance. Moreover, we suggest the relative weights on the actual and auxiliary feedback laws to the RMPC, which in turn improves the closed-loop system performance. Furthermore, an off-line design of the proposed RMPC is also developed to make it more practical. Numerical studies demonstrate the effectiveness of the proposed algorithm. [ABSTRACT FROM PUBLISHER]

This article is focused on reliable fuzzyH∞controller design for a class of Takagi–Sugeno (T–S) fuzzy systems with state delay, actuator failures, disturbance input and norm bounded uncertainties. In the design, theH∞performance of the closed-loop system is optimised during normal operation (without failures) while the system satisfies a prescribedH∞performance level in the case of actuator failures. Two methods are presented in this study. In the first method, delay-dependent conditions are derived based on a single Lyapunov–Krasovskii function. This method improves delay-independent results existing in the literature. Next, to further reduce the conservatism, we use a parameter-dependent Lyapunov–Krasovskii function. The new sufficient conditions for the existence of the suboptimal robust reliable controller are shown in terms of linear matrix inequalities (LMIs), which can be solved by using LMI optimisation techniques. A simulation example shows the effectiveness of the proposed methods. [ABSTRACT FROM AUTHOR]

This article provides a framework for robust performance analysis of linear time-invariant uncertain multivariable systems obtained by classical system identification methods. The performance measures are in terms ofH2orH∞norm of a closed-loop transfer matrix. An upper bound for the performance analysis criterion is computed via an LMI-based optimisation problem. The LFT description is used as a tool for uncertainty modelling. The proposed performance conditions are derived based on using the parameter-dependent Lyapunov functions and are deduced via a parametrisation for the set of multipliers corresponding to the ellipsoidal uncertainty set delivered by system identification procedure. The effectiveness of the proposed analysis approach is demonstrated by a numerical example. [ABSTRACT FROM AUTHOR]

This work is concerned with the robust resilient control problem for uncertain networked control systems (NCSs) with variable sampling intervals, variant-induced delays and possible data dropouts, which is seldom considered in current literature. It is mainly based on the continuous time-varying-delay system approach. Followed by the nominal case, delay-dependent resilient robust stabilising conditions for the closed-loop NCS against controller gain variations are derived by employing a novel Lyapunov–Krasovskii functional which makes good use of the information of both lower and upper bounds on the varying input delay, and the upper bound on the variable sampling interval as well. A feasible solution of the obtained criterion formulated as linear matrix inequalities can be gotten. A tighter bounding technique is presented for acquiring the time derivative of the functional so as to utilise many more useful elements, meanwhile neither slack variable nor correlated augmented item is introduced to reduce overall computational burden. Two examples are given to show the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

This article studies that the robust consensus problem for high-order multi-agent systems with time-delay, which are subjected to external disturbances and communication uncertainties. An approach based on the weighting matrix is introduced to meet the requirement that the peak value of controlled output is often required into a certain range. Then, a dynamic model of high-order multi-agent systems is established. Using a feedback controller and interactions from the neighbours including uncertainties, a linear control protocol is proposed for the systems with networks under fixed or switching topologies. Sufficient conditions in terms of linear matrix inequalities are derived to make all the agents reach robust consensus with the prescribedL2 − L∞performance. A numerical simulation is provided to demonstrate the effectiveness of the proposed protocol. [ABSTRACT FROM AUTHOR]

Choosing an appropriate control scheme to alleviate nonlinearities and uncertainties is not a trivial task, especially when models are not easily available and practical evaluation provides the only means for actual performance assessment. Various factors can contribute to these nonlinearities and uncertainties, such as friction and stiction. Thus, this article investigates four different control schemes, namely PID, adaptive, conventional sliding mode control (SMC) and integral sliding mode control (ISMC) which are implemented in the Bristol Elumotion Robot Hand (BERUL) to analyse and overcome the aforementioned problems. The hand has five fingers with 16 joints and all fingers are underactuated. The implementation of the proposed control schemes are challenging since the BERUL fingers have significant friction, stiction and unknown parameters. The fingers are light in weight and fragile. Comparative performance characteristics have shown that the ISMC is the most suitable candidate to provide good experimental trajectory following and positioning control for underactuated BERUL fingers. [ABSTRACT FROM AUTHOR]

Concepts of robustness are sometimes employed when decisions under uncertainty are made without probabilistic information. We present a theorem that establishes necessary and sufficient conditions for non-probabilistic robustness to be equivalent to the probability of satisfying the specified outcome requirements. When this holds, probability is enhanced (or maximised) by enhancing (or maximising) robustness. Two further theorems establish important special cases. These theorems have implications for success or survival under uncertainty. Applications to foraging and finance are discussed. [ABSTRACT FROM AUTHOR]

In this article, by using the fractional order PIλcontroller, we propose a simple and effective method to compute the robust stability region for the fractional order linear time-invariant plant with interval type uncertainties in both fractional orders and relevant coefficients. The presented method is based on decomposing the fractional order interval plant into several vertex plants using the lower and upper bounds of the fractional orders and relevant coefficients and then constructing the characteristic quasi-polynomial of each vertex plant, in which the value set of vertex characteristic quasi-polynomial in the complex plane is a polygon. The D-decomposition method is used to characterise the stability boundaries of each vertex characteristic quasi-polynomial in the space of controller parameters, which can obtain the stability region by varying λ orders in the range (0, 2). These regions of each vertex plant are computed by using three stability boundaries: real root boundary (RRB), complex root boundary (CRB) and infinite root boundary (IRB). The method gives the explicit formulae corresponding to these boundaries in terms of fractional order PIλcontroller parameters. Thus, the robust stability region for fractional order interval plant can be obtained by intersecting stability region of each vertex plant. The robustness of stability region is tested by the value set approach and zero exclusion principle. Our presented technique does not require sweeping over the parameters and also does not need linear programming to solve a set of inequalities. It also offers several advantages over existing results obtained in this direction. The method in this article is useful for analysing and designing the fractional order PIλcontroller for the fractional order interval plant. An example is given to illustrate this method. [ABSTRACT FROM PUBLISHER]

In this article, a control system design methodology for neutrally stable, uncertain, single-input single-output plants under input amplitude saturation is presented. Based on Horowitz's original three degree of freedom design and extensions developed afterwards, this approach concentrates on neutrally stable, higher type, uncertain plants. A three degree of freedom non-interfering loop structure is used for the synthesis, along with the structure of the additional, independent loop transmission around the saturating element proposed for designing the third degree of freedom H(s). Robust stability and performance are established. The circle criterion, the describing function and non-overshooting conditions are utilised to obtain design constraints. Finally, all these design constraints are expressed in frequency domain bounds and synthesis follows from loop shaping methods such as quantitative feedback theory. [ABSTRACT FROM AUTHOR]

The problem of robust stabilisation for uncertain discrete singular systems with time-varying delay is discussed in this article. First, a new delay-distribution-dependent Lyapunov function is established. Then, sufficient conditions are proposed for guaranteeing that the uncertain discrete singular system with time-varying delay has the properties of regularity, causality and stability. Finally, a suitable robust state feedback controller is designed by linear matrix inequality. Numerical examples illustrate that the results derived from the proposed method are less conservative than the existing ones. [ABSTRACT FROM AUTHOR]

This article presents a method to design a robust controller for a class of nonlinear mechanical systems. The parameters of the system are assumed uncertain in the sense that they are known only within intervals of known lower and upper limits. The method involves using Kharitonov stability theory of interval polynomials to design a robust stabilising controller for the considered class of systems. To formulate the interval parameter problem in joint space, the dynamic equation, which is derived in Cartesian space for parallel kinematic machines, is transformed to joint space using the Jacobian matrix and its time derivative. The nonlinear joint space model is linearised yielding an interval linear model and a robust controller with interval parameter gains is then found. A simulation study on a dynamic model of Stewart platform based parallel kinematics machine demonstrates the design procedure and shows its effectiveness. [ABSTRACT FROM AUTHOR]

An improved method for synthesising the constrained robust model predictive controller is proposed in this study. It constructs a continuum of terminal constraint sets off-line, and achieves robust stability with a variable control horizon on-line from the very beginning and a time-varying terminal constraint set, by solving the min–max optimisation problem, which can be formulated as a linear matrix inequality problem. This algorithm not only dramatically reduces the on-line computation burden, but also guarantees the control performance by reserving at least one free control move in the whole process. Simulation results for the three-tank system with uncertain dynamic behaviour on flux coefficients are given. [ABSTRACT FROM AUTHOR]

This article is concerned with the problem of robust H ∞ output feedback control for a kind of networked control systems with time-varying network-induced delays. Instead of using boundaries of time delays to represent all time delays, the occurrence probability of each time delay is considered in H∞ stability analysis and stabilisation. The problem addressed is the design of an output feedback controller such that, for all admissible uncertainties, the resulting closed-loop system is stochastically stable for the zero disturbance input and also simultaneously achieves a prescribed H∞ performance level. It is shown that less conservativeness is obtained. A set of linear matrix inequalities is given to solve the corresponding controller design problem. An example is provided to show the effectiveness and applicability of the proposed method. [ABSTRACT FROM AUTHOR]

This article mainly studies the decoupling controller design for non-minimum phase plants of different pole numbers on RHP within uncertainties. The normalised coprime factorisation is considered to achieve the robustness requirements. The pole-zero cancellations on RHP should be averted for the sake of robustness. For convenience, the H∞ sub-optimal controller is utilised to meet the robust criterion of the plant. Some necessary state space formulae are also provided to facilitate the synthesis of the decoupling controller. The configuration of the two-parameter compensation is employed. The Bezout identity makes the feedforward controller easy to determine. A brief algorithm is presented. In addition, the proposed synthesis is illustrated with a numerical example. The robust bounds of the feedback controller can be assessed for both the additive uncertainty and the coprime factor uncertainties. The result shows that the compensated system is decoupled and is guaranteed to be internally stable within the specified robust bound although the pole number varies on RHP. [ABSTRACT FROM AUTHOR]

This article presents a robust adaptive control scheme for a discrete-time plant that is subjected to both coprime factor perturbations and unknown exogenous disturbances. With the proposed control scheme, all the variables in the closed-loop system are bounded in the presence of the perturbations and disturbances, and an a priori computable upper bound on the size of the nonparametric dynamical uncertainty, for which stability is ensured, is provided. Moreover, one can guarantee a priori bound on the asymptotic performance of the overall adaptive system which is arbitrarily close to that of the corresponding nonadaptive control system. In addition, it is shown that the ℓ1 optimal robust controller design is continuous as a map from the plant to the optimal closed-loop solution. Furthermore, if the set of plants is compact, then the ℓ1 optimal robust controller design is uniformly continuous on the set of plants. These properties are necessary for analysing the interplay between identification and control in the overall adaptive system. [ABSTRACT FROM AUTHOR]

This article presents the integral sliding mode technique applied to identify disturbances and robustify the optimal linear quadratic Gaussian controller for linear uncertain stochastic systems, which is compared to the conventional sliding mode approach. The obtained identifier/controller provides a method for estimating uncertainty values and ensures robustness of the system against matched uncertainties, starting from the initial time instant. Numerical simulations illustrating the obtained results are given for the inverted pendulum. [ABSTRACT FROM AUTHOR]

Fault diagnosis is analysed here as a decision between alternative hypotheses, based on uncertain evidence. W e consider severe lack of information, and perceive the uncertainty as an information gap between what is known, and what needs to be known for a perfect decision. This uncertainty is quantified with info-gap models of uncertainty, which require less information than probabilistic models. Previous work with convex set-models is extended to linear info-gap models which are not necessarily convex, as well as to more general info-gap models with arbitrary expansion properties. We define a decision algorithm based on info-gap models and prove three theorems, one establishing the connection with the earlier work on convex models, the other two showing that the algorithm is maximally robust for linear info-gap models as well as for general infogap models of uncertainty. An illustrative example is presented which shows how these results can be used for optimizing the design of a model-based fault diagnosis algorithm. [ABSTRACT FROM AUTHOR]