Probabilistic analysis and control of systems with uncertain parameters over non-hypercube domain

Generalized polynomial chaos expansion provides a computationally efficient way of quantifying the influence of stochastic parametric uncertainty on the states and outputs of a system. In this study, a polynomial chaos-based method was proposed for an analysis and design of control systems with parametric uncertainty over a non-hypercube support domain. In the proposed method, the polynomial chaos for the hypercube domain was extended to non-hypercube domains through proper parameterization to transform the non-hypercube domains to hypercube domains. Based on the proposed polynomial chaos framework, a constrained optimization problem minimizing the mean under the maximum allowable variance was formulated for a robust controller design of dynamic systems with the parametric uncertainties of the non-hypercube domain. Several numerical examples ranging from integer to fractional order systems were considered to validate the proposed method. The proposed method provided superior control performance by avoiding the over-bounds from a hypercube assumption in a computationally efficient manner. From the simulation examples, the computation time by gPC analysis was approximately 10–100 times lower than the traditional approach.     

Characteristic polynomial assignment for plants with semialgebraic uncertainty: A robust diophantine equation approach

In this paper, we address the problem of robust characteristic polynomial assignment for LTI systems whose parameters are assumed to belong to a semialgebraic uncertainty region. The objective is to design a dynamic fixed‐order controller in order to constrain the coefficients of the closed‐loop characteristic polynomial within prescribed intervals. First, necessary conditions on the plant parameters for the existence of a robust controller are reviewed, and it is shown that such conditions are satisfied if and only if a suitable Sylvester matrix is nonsingular for all possible values of the uncertain plant parameters. The problem of checking such a robust nonsingularity condition is formulated in terms of a nonconvex optimization problem. Then, the set of all feasible robust controllers is sought through the solution to a suitable robust diophantine equation. Convex relaxation techniques based on sum‐of‐square decomposition of positive polynomials are used to efficiently solve the formulated optimization problems by means of semidefinite programming. The presented approach provides a generalization of the results previously proposed in the literature on the problem of assigning the characteristic polynomial in the presence of plant parametric uncertainty. Copyright © 2014 John Wiley & Sons, Ltd.     

Event‐triggered probabilistic robust control of linear systems with input constrains: By scenario optimization approach

This paper addresses the problem of probabilistic robust stabilization for uncertain systems subject to input saturation. A new probabilistic solution framework for robust control analysis and synthesis problems is addressed by a scenario optimization approach, in which the uncertainties are not assumed to be norm bounded. Furthermore, by expressing the saturated linear feedback law on a convex hull of a group of auxiliary linear feedback laws, we establish conditions under which the closed‐loop system is probabilistic stable. Based on these conditions, the problem of designing the state feedback gains for achieving the largest size of the domain of attraction is formulated and solved as a constrained optimization problem with linear matrix inequality constraints. The results are then illustrated by a numerical example.     

Robust multiple-model LPV approach to nonlinear process identification using mixture t distributions

In this paper, we propose a robust multiple-model linear parameter varying (LPV) approach to identification of the nonlinear process contaminated with outliers. The identification problem is formulated and solved under the EM framework. Instead of assuming that the measurement noise comes from the Gaussian distribution like conventional LPV approaches, the proposed robust algorithm formulates the LPV solution using mixture t distributions and thus naturally addresses the robust identification problem. By modulating the distribution tails through degrees of freedom, the proposed algorithm can handle various outliers. Two simulated examples and an experiment are studied to verify the effectiveness of the proposed approach.     

Robust multi‐response surface optimization: a posterior preference approach

This paper discusses the use of multi‐response surface optimization (MRSO) to select the preferred solutions from among various non‐dominated solutions (NDS). Since MSRO often involves conflicting responses, the decision‐maker's (DM) preference information should be included in the model in order to choose the preferred solutions. In some approaches this information is added to the model after the problem is solved. In contrast, this paper proposes a three‐stage method for solving the problem. In the first stage, a robust approach is used to construct a regression model. In the second phase, non‐dominated solutions are generated by the ε‐constraint approach. The robust solutions obtained in the third phase are NDS that are more likely to be Pareto solutions during consecutive iterations. A simulation study is then presented to show the effective performance of the proposed approach. Finally, a numerical example from the literature is brought in to demonstrate the efficiency and applicability of the proposed methodology.     

A stabilized finite element method for stochastic incompressible Navier–Stokes equations

The major emphasis of this work is the development of a stabilized finite element method for solving incompressible Navier–Stokes equations with stochastic input data. The polynomial chaos expansion is used to represent stochastic processes in the variational problem, resulting in a set of deterministic variational problems to be solved for each Wiener polynomial chaos. To obtain the chaos coefficients in the corresponding deterministic incompressible Navier–Stokes equations, we combine the modified method of characteristics with the finite element discretization. The obtained Stokes problem is solved using a robust conjugate-gradient algorithm. This algorithm avoids projection procedures and any special correction for the pressure. These numerical techniques associate the geometrical flexibility of the finite element method with the ability offered by the modified method of characteristics to solve convection-dominated problems using time steps larger than its Eulerian counterpart. Numerical results are shown for the benchmark problems of driven cavity flow and backward-facing step flow. We also present numerical results for a problem of stochastic natural convection. It is found that the proposed stabilized finite element method offers a robust and accurate approach for solving the stochastic incompressible Navier–Stokes equations, even when high Reynolds and Rayleigh numbers are used in the simulations.     

Robust Control of Positive Fractional‐Order Interconnected Systems with Heterogeneous Delays

The problem of robust decentralized control of positive fractional‐order interconnected systems with heterogeneous time‐varying delays is studied in this paper. Necessary and sufficient conditions are first derived for internal positiveness of the system. By exploiting the monotonicity induced from positivity of the system, robust stability conditions subject to uncertain system parameters are derived. The derived stability conditions are then utilized to address the controller synthesis problem. The design conditions for obtaining controller gains of stabilizing decentralized controllers are formulated using linear programming, which can be effectively solved by various convex optimization algorithms. Finally, the effectiveness of the obtained results is validated by two numerical examples.     

AN ITERATIVE LMI APPROACH TO RFDF FOR LINEAR SYSTEM WITH TIME‐VARYING DELAYS

This paper deals with robust fault detection filter (RFDF) problem for a class of linear uncertain systems with time‐varying delays and model uncertainties. The RFDF design problem is formulated as an optimization problem by using L₂‐induced norm to represent the robustness of residual to unknown inputs and modelling errors, and the sensitivity to faults. A sufficient condition to the solvability of formulated problem is established in terms of certain matrix inequalities, which can be solved with the aid of an iterative linear matrix inequality (ILMI) algorithm. Finally, a numerical example is given to illustrate the effectiveness of the proposed method.     

General and Robust Error Estimation and Reconstruction for Monte Carlo Rendering

Adaptive filtering techniques have proven successful in handling non‐uniform noise in Monte‐Carlo rendering approaches. A recent trend is to choose an optimal filter per pixel from a selection of non spatially‐varying filters. Nonetheless, the best filter choice is difficult to predict in the absence of a reference rendering. Our approach relies on the observation that the reconstruction error is locally smooth for a given filter. Hence, we propose to construct a dense error prediction from a small set of sparse but robust estimates. The filter selection is then formulated as a non‐local optimization problem, which we solve via graph cuts, to avoid visual artifacts due to inconsistent filter choices. Our approach does not impose any restrictions on the used filters, outperforms previous state‐of‐the‐art techniques and provides an extensible framework for future reconstruction techniques.     

An improved sum‐of‐squares based approach to fuzzy tracking control design of nonlinear systems

In this paper, using a more general Lyapunov function, less conservative sum‐of‐squares (SOS) stability conditions for polynomial‐fuzzy‐model‐based tracking control systems are derived. In tracking control problems the objective is to drive the system states of a nonlinear plant to follow the system states of a given reference model. A state feedback polynomial fuzzy controller is employed to achieve this goal. The tracking control design is formulated as an SOS optimization problem. Here, unlike previous SOS‐based tracking control approaches, a full‐state‐dependent Lyapunov matrix is used, which reduces the conservatism of the stability criteria. Furthermore, the SOS conditions are derived to guarantee the system stability subject to a given H_∞ performance. The proposed method is applied to the pitch‐axis autopilot design problem of a high‐agile tail‐controlled pursuit and another numerical example to demonstrate the effectiveness and benefits of the proposed method.     

Analysis and Design of Robust Controllers Using the Interval Diophantine Equation

In this paper the design of pole placement feedback controllers for interval plants is addressed within an Interval Analysis framework. Three main topics connected by the concept of interval Diophantine equation are treated: a relatively straightforward investigation about robust coprimeness of interval polynomials, a reliable computing approach for the design of pole placement controllers in the presence of inaccuracies of interval type, and the design of robust controllers with regional pole placement specifications. After explicitly characterizing a convex subset of robust controllers, the problem of designing non-fragile controllers is formulated as a design centering problem, which is then solved by a global optimization algorithm. Numerical examples illustrate the main characteristics of the approach proposed.This paper has been partially supported by grants from Conselho Nacional de Desenvolvimento Científico e Tecnológico–Brazil.     

Robust PID controller design for processes with stochastic parametric uncertainties

The stability and performance of a system can be inferred from the evolution of statistical characteristics of the system's states. Wiener's polynomial chaos can provide an efficient framework for the statistical analysis of dynamical systems, computationally far superior to Monte Carlo simulations. This work proposes a new method of robust PID controller design based on polynomial chaos for processes with stochastic parametric uncertainties. The proposed method can greatly reduce computation time and can also efficiently handle both nominal and robust performance against stochastic uncertainties by solving a simple optimization problem. Simulation comparison with other methods demonstrated the effectiveness of the proposed design method.     

Identification of high-dimension polynomial chaos expansions with random coefficients for non-Gaussian tensor-valued random fields using partial and limited experimental data

This paper is devoted to the identification of high-dimension polynomial chaos expansions with random coefficients for non-Gaussian tensor-valued random fields using partial and limited experimental data. The experimental data sets correspond to partial experimental data made up of an observation vector which is the response of a stochastic boundary value problem depending on the tensor-valued random field which has to be identified. So an inverse stochastic problem has to be solved to carry out the identification of the random field. A complete methodology is proposed to solve this challenging problem and consists in introducing a family of prior probability models, in identifying an optimal prior model in the constructed family using the experimental data, in constructing a statistical reduced order optimal prior model, in constructing the polynomial chaos expansion with deterministic vector-valued coefficients of the reduced order optimal prior model and finally, in constructing the probability distribution of random coefficients of the polynomial chaos expansion and in identifying the parameters using experimental data. An application is presented for which several millions of random coefficients are identified solving an inverse stochastic problem.     

A computational inverse method for identification of non-Gaussian random fields using the Bayesian approach in very high dimension

This paper is devoted to the identification of Bayesian posteriors for the random coefficients of the high-dimension polynomial chaos expansions of non-Gaussian tensor-valued random fields using partial and limited experimental data. The experimental data sets correspond to an observation vector which is the response of a stochastic boundary value problem depending on the tensor-valued random field which has to be identified. So an inverse stochastic problem must be solved to perform the identification of the random field. A complete methodology is proposed to solve this very challenging problem in high dimension, which consists in using the first four steps introduced in a previous paper, followed by the identification of the posterior model. The steps of the methodology are the following: (1) introduction of a family of prior algebraic stochastic model (PASM), (2) identification of an optimal PASM in the constructed family using the partial experimental data, (3) construction of a statistical reduced-order optimal PASM, (4) construction, in high dimension, of the polynomial chaos expansion with deterministic vector-valued coefficients of the reduced-order optimal PASM, (5) substitution of these deterministic vector-valued coefficients by random vector-valued coefficients in order to extend the capability of the polynomial chaos expansion to represent the experimental data and for which the joint probability distribution must be identified, (6) construction of the prior probability model of these random vector-valued coefficients and finally, (7) identification of the posterior probability model of these random vector-valued coefficients using partial and limited experimental data, through the stochastic boundary value problem. Two methods are proposed to carry out the identification of the posterior model. The first one is based on the use of the classical Bayesian method. The second one is a new approach derived from the Bayesian method, which is more efficient in high dimension. An application is presented for which several millions of random coefficients are identified.     

Robust aerodynamic shape design based on an adaptive stochastic optimization framework

Optimization techniques combined with uncertainty quantification are computationally expensive for robust aerodynamic optimization due to expensive CFD costs. Surrogate model technology can be used to improve the efficiency of robust optimization. In this paper, non-intrusive polynomial chaos method and Kriging model are used to construct a surrogate model that associate stochastic aerodynamic statistics with airfoil shapes. Then, global search algorithm is used to optimize the model to obtain optimal airfoil fast. However, optimization results always depend on the approximation accuracy of the surrogate model. Actually, it is difficult to achieve a high accuracy of the model in the whole design space. Therefore, we introduce the idea of adaptive strategy to robust aerodynamic optimization and propose an adaptive stochastic optimization framework. The surrogate model is updated adaptively by increasing training airfoils according to historical optimization results to guarantee the accuracy near the optimal design point, which can greatly reduce the number of training airfoils. The proposed method is applied to a robust aerodynamic shape optimization for drag minimization considering uncertainty of Mach number in transonic region. It can be concluded that the proposed method can obtain better optimal results more efficiently than the traditional robust optimization method and global surrogate model method.     

Identification of Wiener–Hammerstein benchmark model via rank minimization

In this paper, a Wiener–Hammerstein system identification problem is formulated as a semidefinite programming (SDP) problem which provides a sub-optimal solution for a rank minimization problem. In the proposed identification method, the first linear dynamic system, the static nonlinear function, and the second linear dynamic system are parameterized as an FIR model, a polynomial function, and a rational transfer function respectively. Subsequently the optimization problem is formulated by using the over-parameterization technique and an iterative approach is proposed to update two unmeasurable intermediate signals. For the modeling of static nonlinearity, the monotonically non-deceasing condition was applied to limit the number of possible selections for intermediate signals. At each step of iteration, the over-parametrized parameters are estimated and then system parameters are separated by using a singular value decomposition (SVD). The proposed method is applied to the benchmark problem and the estimation result shows the effectiveness of the proposed algorithm.     

The lexicographic α‐robust knapsack problem

The knapsack problem is a classical combinatorial optimization problem used to model many industrial situations. The robust version of this problem was studied in the literature using a max–min or min–max regret criterion. In this paper, we show the drawbacks of such criteria and propose a new robustness approach, called lexicographic α‐robustness. We show that the complexity of the lexicographic α‐robust problem does not increase compared with the max–min version and present a pseudo‐polynomial algorithm in the case of a bounded number of scenarios.     

A convex optimization approach to robust iterative learning control for linear systems with time-varying parametric uncertainties

In this paper, we present a new robust iterative learning control (ILC) design for a class of linear systems in the presence of time-varying parametric uncertainties and additive input/output disturbances. The system model is described by the Markov matrix as an affine function of parametric uncertainties. The robust ILC design is formulated as a min–max problem using a quadratic performance criterion subject to constraints of the control input update. Then, we propose a novel methodology to find a suboptimal solution of the min–max optimization problem. First, we derive an upper bound of the worst-case performance. As a result, the min–max problem is relaxed to become a minimization problem in the form of a quadratic program. Next, the robust ILC design is cast into a convex optimization over linear matrix inequalities (LMIs) which can be easily solved using off-the-shelf optimization solvers. The convergences of the control input and the error are proved. Finally, the robust ILC algorithm is applied to a physical model of a flexible link. The simulation results reveal the effectiveness of the proposed algorithm.     

Robust fault detection with missing measurements

This paper investigates the problem of robust fault detection for uncertain systems with missing measurements. The parameter uncertainty is assumed to be of polytopic type, and the measurement missing phenomenon, which appears typically in a network environment, is modelled by a stochastic variable satisfying the Bernoulli random binary distribution. The focus is on the design of a robust fault detection filter, or a residual generation system, which is stochastically stable and satisfies a prescribed disturbance attenuation level. This problem is solved in the parameter-dependent framework, which is much less conservative than the quadratic approach. Both full-order and reduced-order designs are considered, and formulated via linear matrix inequality (LMI) based convex optimization problems, which can be efficiently solved via standard numerical software. A continuous-stirred tank reactor (CSTR) system is utilized to illustrate the design procedures.     

Affinely adjustable robust optimization for a multi-period inventory problem with capital constraints and demand uncertainties

This study focuses on a multi-period inventory problem with capital constraints and demand uncertainties. The multi-period inventory problem is formulated as an optimization model with a joint chance constraint (JCC) requiring the purchase cost for each period not to exceed the available capital with a probability guarantee. To hedge against demand uncertainties, an affinely adjustable robust optimization approach is used to convert the developed model into a robust counterpart. By approximating the JCC under a budgeted uncertainty set to which the demands belong, the robust multi-period inventory model with the JCC is transformed into a linear programming model, which can be solved efficiently. Numerical studies are reported to illustrate the robustness, practicality, and effectiveness of the proposed model and the solution approach. The numerical results show that the proposed model and solution approach outperform the sample average approximation approach. Numerical studies are used further to analyze the impact of the budget coefficient and the upper bound parameter on the inventory costs and the realized capital constraint satisfaction rate. The proposed model and solution approach are further extended to the multi-product case.