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In networked control systems, selecting a subset of input and output nodes is a crucial step in designing a stabilizing controller. Most existing approaches to input and output selection focus on nominal systems with known parameters. For systems with uncertainties and time delays, current selection methods are based on exploiting the convexity after relaxing the original problem, which is inherently discrete, to continuous optimization forms, and hence lack optimality guarantees. This paper studies the problem of identifying the minimum-size sets of input and output nodes to guarantee stability of a linear system with uncertainties and time delays. We derive sufficient conditions to guarantee the existence of a stabilizing controller for an uncertain linear system, based on a subset of system modes lying within the controllability and observability subspaces induced by the selected inputs and outputs. We then formulate the problems of selecting minimum-size sets of input and output nodes to satisfy the derived conditions, and prove that they are equivalent to discrete optimization problems with bounded submodularity ratios. We develop polynomial-time selection algorithms with provable guarantees on the minimum number of inputs and outputs required. Our approach is applicable to various types of uncertainties, including additive uncertainty, multiplicative uncertainty, uncertain output delay, and structured uncertainty. In a numerical study, we test our approach for the wide-area damping control in power systems to ensure small signal stability. Our results are validated on the IEEE 39-bus test power system. [ABSTRACT FROM AUTHOR]

The scenario approach is a general methodology for data-driven optimization that has attracted a great deal of attention in the past few years. It prescribes that one collects a record of previous cases (scenarios) from the same setup in which optimization is being conducted and makes a decision that attains optimality for the seen cases. Scenario optimization is by now very well understood forconvexproblems, where a theory exists that rigorously certifies the generalization properties of the solution, that is, the ability of the solution to perform well in connection to new situations. This theory supports the scenario methodology and justifies its use. This paper considersnonconvexproblems. While other contributions in the nonconvex setup already exist, we here take a major departure from previous approaches. We suggest that the generalization level is evaluated only after the solution is found and its complexity in terms of the length of a support subsample (a notion precisely introduced in this paper) is assessed. As a consequence, the generalization level is stochastic and adjusted case by case to the available scenarios. This fact is key to obtain tight results. The approach adopted in this paper applies not only to optimization, but also to generic decision problems where the solution is obtained according to a rule that is not necessarily the optimization of a cost function. Accordingly, in our presentation we adopt a general stance of which optimization is just seen as a particular case. [ABSTRACT FROM AUTHOR]

This paper studies the projected saddle-point dynamics associated to a convex–concave function, which we term saddle function. The dynamics consists of gradient descent of the saddle function in variables corresponding to convexity and (projected) gradient ascent in variables corresponding to concavity. We examine the role that the local and/or global nature of the convexity–concavity properties of the saddle function plays in guaranteeing convergence and robustness of the dynamics. Under the assumption that the saddle function is twice continuously differentiable, we provide a novel characterization of the omega-limit set of the trajectories of this dynamics in terms of the diagonal blocks of the Hessian. Using this characterization, we establish global asymptotic convergence of the dynamics under local strong convexity–concavity of the saddle function. When strong convexity–concavity holds globally, we establish three results. First, we identify a Lyapunov function (that decreases strictly along the trajectory) for the projected saddle-point dynamics when the saddle function corresponds to the Lagrangian of a general constrained convex optimization problem. Second, for the particular case when the saddle function is the Lagrangian of an equality-constrained optimization problem, we show input-to-state stability (ISS) of the saddle-point dynamics by providing an ISS Lyapunov function. Third, we use the latter result to design an opportunistic state-triggered implementation of the dynamics. Various examples illustrate our results. [ABSTRACT FROM AUTHOR]

Control laws for selective transfer of information encoded in excitations of a quantum network, based on shaping the energy landscape using time-invariant, spatially-varying bias fields, can be successfully designed by using numerical optimization. Such control laws, already departing from classicality by replacing closed-loop asymptotic stability with alternative notions of localization, have the intriguing property that for all practical purposes they achieve the upper bound on the fidelity, yet the (logarithmic) sensitivity of the fidelity to such structured perturbation as spin coupling errors and bias field leakages is nearly vanishing. Here, these differential sensitivity results are extended to large structured variations using $\mu$ -design tools to reveal a crossover region in the space of controllers where objectives usually thought to be conflicting are actually concordant. [ABSTRACT FROM PUBLISHER]

This paper presents a general framework for the analysis and design of a class of model-free, robust, and efficient sampled-data-based algorithms for extremization and learning in continuous-time nonlinear systems that generate response maps with an optimal operational set. In particular, we consider plants described by differential inclusions, interconnected in a sampled-data setting with a robust learning algorithm characterized by a constrained difference inclusion. In contrast to standard sampled-data-based approaches, where the learning dynamics are updated after a fixed sufficiently long sampling time has passed, we design a robust dynamic event-based mechanism that triggers the control action as soon as the rate of change of the output of the plant is sufficiently small. By using this event-based update rule, a significant improvement in the convergence time of the closed-loop system can be achieved. Using the framework of set-valued hybrid dynamical systems, we establish for the closed-loop system the existence of a uniformly asymptotically stable compact set, which, by an appropriate tuning of the control parameters, can be made arbitrarily close to the optimal operational set. Our results generalize existing results for periodic sampled-data extremum seeking, and can be used to solve model-free multivariable smooth/nonsmooth constrained optimization problems, as well as learning problems in game theoretical scenarios. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel robust model predictive controller for tracking periodic signals for linear systems subject to bounded additive uncertainties based on nominal predictions and constraint tightening. The proposed controller joins optimal periodic trajectory planning and robust control for tracking in a single optimization problem and guarantees that the perturbed closed-loop system converges asymptotically to a neighborhood of an optimal reachable periodic trajectory while robustly satisfying the constraints. In addition, the closed-loop system maintains recursive feasibility even in the presence of sudden changes in the target reference. [ABSTRACT FROM AUTHOR]

This paper addresses the design of output controllers of chosen order in a given set for ensuring a number of desired properties, such as robust D-stability and robust performance. It is shown that a sufficient condition for determining such controllers can be obtained by solving convex optimization problems with linear matrix inequality constraints through the introduction of a Lyapunov function depending polynomially on both uncertainty and controller. The proposed condition is also necessary for sufficiently large degrees of the polynomials involved under some assumptions. [ABSTRACT FROM AUTHOR]

A new design of linear quadratic control (LQ) is proposed for batch processes with partial actuator faults and unknown disturbance. The autoregressive model built through input-output process data is transformed into a new non-minimal state space (NMSS) model in which the process state variables and tracking error are combined, then a new LQ control scheme is derived based on minimax optimization. Unlike LQ based on traditional NMSS model (TNMSSLQ), the process state variables and tracking error can both be regulated in the proposed method. Monotonic situations of optimal performance cost are further derived to yield sufficient conditions for both nominal and robust system stability. Finally, an injection velocity control example is illustrated to show the effectiveness. [ABSTRACT FROM AUTHOR]

This technical note focuses on a class of switched nonlinear systems with unstable modes and switching uncertainties. Both prescribed switching instants and switching sequence of the nominal switching signal would change. Two novel indexes named time changing ratios and mode changing ratios are proposed, based on which several conditions that fully utilize the trade-off among stable and unstable modes are established to achieve the robust stability of the switched system. An example of multi-agent systems illustrates the efficiency of the new results. [ABSTRACT FROM PUBLISHER]

In this paper, we consider the l\infty gain characterizations of linear switched systems (LSS) and present various relevant results on their exact computation and optimization. Depending on the role of the switching sequence, we study two broad cases: first, when the switching sequence attempts to maximize, and second, when it attempts to minimize the l\infty gain. The first, named as worst-case throughout the paper, can be related to robustness of the system to uncontrolled switching; the second relates to situations when the switching can be part to the overall decision making. Although, in general, the exact computation of l\infty gains is difficult, we provide specific classes, the input-output switching systems, for which it is shown that linear programming can be used to obtain the worst-case l\infty gain. This is a sufficiently rich class of systems as any stable LSS can be approximated by one. Certain applications to robust control design are provided where we show that a switched compensation independently of the plant has no advantage over a linear time invariant (LTI) compensation, and further, if the plant is strictly causal, even a switched compensation which has a matched switching with the plant does not provide a better performance over an LTI compensation. Also, we present a new necessary and sufficient condition to check the stability of LSS in form of a model matching problem. On the other hand, if one is interested in minimizing the l\infty gain over the switching sequences, we show that, for finite impulse response (FIR) switching systems the minimizing switching sequence can be chosen to be periodic. For input-only or output-only switching an exact, readily computable, characterization of the minimal l\infty gain is provided, and it is shown that the minimizing switching sequence is constant, which, as also shown, is not true for input-output switching. [ABSTRACT FROM AUTHOR]

We study a distributed allocation process where, at each time, every player: i) proposes a new bid based on the average utilities produced up to that time, ii) adjusts such allocations based on the inputs received from its neighbors, and iii) generates and allocates new utilities. The average allocations evolve according to a doubly (over time and space) averaging algorithm. We study conditions under which the average allocations reach consensus to any point within a predefined target set even in the presence of adversarial disturbances. Motivations arise in the context of coalitional games with transferable utilities (TU) where the target set is any set of allocations that makes the grand coalition stable. [ABSTRACT FROM AUTHOR]

This paper presents a robust, distributed algorithm to solve general linear programs. The algorithm design builds on the characterization of the solutions of the linear program as saddle points of a modified Lagrangian function. We show that the resulting continuous-time saddle-point algorithm is provably correct but, in general, not distributed because of a global parameter associated with the nonsmooth exact penalty function employed to encode the inequality constraints of the linear program. This motivates the design of a discontinuous saddle-point dynamics that, while enjoying the same convergence guarantees, is fully distributed and scalable with the dimension of the solution vector. We also characterize the robustness against disturbances and link failures of the proposed dynamics. Specifically, we show that it is integral-input-to-state stable but not input-to-state stable. The latter fact is a consequence of a more general result, that we also establish, which states that no algorithmic solution for linear programming is input-to-state stable when uncertainty in the problem data affects the dynamics as a disturbance. Our results allow us to establish the resilience of the proposed distributed dynamics to disturbances of finite variation and recurrently disconnected communication among the agents. Simulations in an optimal control application illustrate the results. [ABSTRACT FROM PUBLISHER]

In this paper, we present a unified framework for solving a general class of problems arising in the context of set-membership estimation/identification theory. More precisely, the paper aims at providing an original approach for the computation of optimal conditional and robust projection estimates in a nonlinear estimation setting, where the operator relating the data and the parameter to be estimated is assumed to be a generic multivariate polynomial function, and the uncertainties affecting the data are assumed to belong to semialgebraic sets. By noticing that the computation of both the conditional and the robust projection optimal estimators requires the solution to min-max optimization problems that share the same structure, we propose a unified two-stage approach based on semidefinite-relaxation techniques for solving such estimation problems. The key idea of the proposed procedure is to recognize that the optimal functional of the inner optimization problems can be approximated to any desired precision by a multivariate polynomial function by suitably exploiting recently proposed results in the field of parametric optimization. Two simulation examples are reported to show the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

Set membership identification seeks to obtain models amenable to be used in a robust control framework. While under suitable assumptions this problem is convex, existing methods lead to high order models. As we show in this note, this difficulty can be avoided by recasting the problem as a structured nuclear norm minimization. To solve this problem, we propose a computationally efficient first order algorithm that requires performing only a combination of thresholding and eigenvalue decomposition steps. Finally, since the optimization is carried out only over sequences compatible with the output responses of stable systems, the identified model is guaranteed to be stable. [ABSTRACT FROM PUBLISHER]

In this paper, we propose a stochastic search algorithm for solving general optimization problems with little structure. The algorithm iteratively finds high quality solutions by randomly sampling candidate solutions from a parameterized distribution model over the solution space. The basic idea is to convert the original (possibly non-differentiable) problem into a differentiable optimization problem on the parameter space of the parameterized sampling distribution, and then use a direct gradient search method to find improved sampling distributions. Thus, the algorithm combines the robustness feature of stochastic search from considering a population of candidate solutions with the relative fast convergence speed of classical gradient methods by exploiting local differentiable structures. We analyze the convergence and converge rate properties of the proposed algorithm, and carry out numerical study to illustrate its performance. [ABSTRACT FROM AUTHOR]

This paper considers the synchronous distributed receding horizon control (RHC) for a general problem of the nonholonomic multi-vehicle systems, i.e., the simultaneous forward/backward tracking, regulation and formation with the collision avoidance. First, for each vehicle, a positively invariant terminal-state region and an auxiliary controller are developed. When every vehicle lies in its terminal-state region, all the distributed control targets are achieved by the auxiliary controller. Second, the compatibility constraint, restricting the norm of the uncertain deviation between the assumed and actual predictive trajectories of each vehicle, is given, which respects both the collision avoidance and convergence guarantee. Thirdly, a robust collision avoidance constraint tolerating for the uncertain deviation is designed. By these designs, an overall control algorithm is proposed, by applying which all the control targets are achieved. Two illustrative examples are provided to show the advantage and effectiveness of the proposed approach. [ABSTRACT FROM PUBLISHER]

This technical note proposes a method for low order H-infinity synthesis where the constraint on the order of the controller is formulated as a rational equation. The resulting nonconvex optimization problem is then solved by applying a partially augmented Lagrangian method. The proposed method is evaluated together with two well-known methods from the literature. The results indicate that the proposed method has comparable performance and speed. [ABSTRACT FROM AUTHOR]

Following a polynomial approach, many robust fixed-order controller design problems can be formulated as optimization problems whose set of feasible solutions is modeled by parametrized polynomial matrix inequalities (PMIs). These feasibility sets are typically nonconvex. Given a parametrized PMI set, we provide a hierarchy of linear matrix inequality (LMI) problems whose optimal solutions generate inner approximations modeled by a single polynomial superlevel set. Those inner approximations converge in a well-defined analytic sense to the nonconvex original feasible set, with asymptotically vanishing conservatism. One may also impose the hierarchy of inner approximations to be nested or convex. In the latter case, they do not converge any more to the feasible set, but they can be used in a convex optimization framework at the price of some conservatism. Finally, we show that the specific geometry of nonconvex polynomial stability regions can be exploited to improve convergence of the hierarchy of inner approximations. [ABSTRACT FROM AUTHOR]

In this paper, we propose a new tractable framework for dealing with linear dynamical systems affected by uncertainty, applicable to multistage robust optimization and stochastic programming. We introduce a hierarchy of near-optimal polynomial disturbance-feedback control policies, and show how these can be computed by solving a single semidefinite programming problem. The approach yields a hierarchy parameterized by a single variable (the degree of the polynomial policies), which controls the trade-off between the optimality gap and the computational requirements. We evaluate our framework in the context of three classical applications—two in inventory management, and one in robust regulation of an active suspension system—in which very strong numerical performance is exhibited, at relatively modest computational expense. [ABSTRACT FROM AUTHOR]

We consider robust feedback control of time-varying, linear discrete-time systems operating over a finite horizon. For such systems, we consider the problem of designing robust causal controllers that minimize the expected value of a convex quadratic cost function, subject to mixed linear state and input constraints. Determination of an optimal control policy for such problems is generally computationally intractable, but suboptimal policies can be computed by restricting the class of admissible policies to be affine on the observation. By using a suitable re-parameterization and robust optimization techniques, these approximations can be solved efficiently as convex optimization problems. We investigate the loss of optimality due to the use of such affine policies. Using duality arguments and by imposing an affine structure on the dual variables, we provide an efficient method to estimate a lower bound on the value of the optimal cost function for any causal policy, by solving a cone program whose size is a polynomial function of the problem data. This lower bound can then be used to quantify the loss of optimality incurred by the affine policy. [ABSTRACT FROM AUTHOR]

The problem of robust and guaranteed stabilization is addressed for switched affine systems using sampled state feedback controllers. Based on the existence of a control Lyapunov function for a relaxed system, we propose three sampled-data controls. Global attracting sets, computed by solving a sequence of optimization problems, guarantee practical and global asymptotic stabilization for the whole system trajectories. In addition, robust margins with respect to parameters uncertainties and non uniform sampling are provided using input-to-state stability. Finally, a buck-boost converter is considered to illustrate the effectiveness of the proposed approaches. [ABSTRACT FROM AUTHOR]

Robustness is a major problem in Kalman filtering and smoothing that can be solved using heavy tailed distributions; e.g., \ell 1-Laplace. This paper describes an algorithm for finding the maximum a posteriori (MAP) estimate of the Kalman smoother for a nonlinear model with Gaussian process noise and \ell 1-Laplace observation noise. The algorithm uses the convex composite extension of the Gauss–Newton method. This yields convex programming subproblems to which an interior point path-following method is applied. The number of arithmetic operations required by the algorithm grows linearly with the number of time points because the algorithm preserves the underlying block tridiagonal structure of the Kalman smoother problem. Excellent fits are obtained with and without outliers, even though the outliers are simulated from distributions that are not \ell 1-Laplace. It is also tested on actual data with a nonlinear measurement model for an underwater tracking experiment. The \ell 1-Laplace smoother is able to construct a smoothed fit, without data removal, from data with very large outliers. [ABSTRACT FROM AUTHOR]

Nonlinear model predictive control (NMPC) strategies based on linearization about predicted system trajectories enable the online NMPC optimization to be performed by a sequence of convex optimization problems. The approach relies on bounds on linearization errors in order to ensure constraint satisfaction and convergence of the performance index, both during the optimization at each sampling instant and along closed loop system trajectories. This technical note proposes bounds based on robust tubes constructed around predicted trajectories. To ensure local optimality, the bounds are non-conservative for the case of zero linearization error, which requires the tube cross sections to vary along predicted trajectories. The feasibility, stability and convergence properties of the algorithm are established without the need for predictions to satisfy local optimality criteria. The strategy is illustrated by numerical examples. [ABSTRACT FROM AUTHOR]

In this technical note an easy-to-use \cal H\infty /LTR control solution with mixed-sensitivity properties is presented. This theory can be viewed as an expansion of LQG control via \cal H2 optimization point of view. The proposed methodology allows the control designer to bound the reference tracking frequency peak in the loop shaping at a lower value than the usual LQG/LTR setup, reducing the system overshoot. This feature is obtained by applying the LTR to the \cal H\infty state-feedback control. [ABSTRACT FROM PUBLISHER]

In this paper, we study a class of robust design problems with polynomial dependence on the uncertainty. One of the main motivations for considering these problems comes from robust controller design, where one often encounters systems that depend polynomially on the uncertain parameters. This paper can be seen as integrated in the emerging area of probabilistic robustness, where a probabilistic relaxation of the original robust problem is adopted, thus requiring the satisfaction of the constraints not for all possible values of the uncertainty, but for most of them. Different from the randomized approach for tackling probabilistic relaxations, which is only guaranteed to provide soft bounds on the probability of satisfaction, we present a deterministic approach based on the novel concept of kinship function introduced in this paper. This allows the development of an original framework, which leads to easily computable deterministic convex relaxations of the probabilistic problem. In particular, optimal polynomial kinship functions are introduced, which can be computed a priori and once for all and provide the “best convex bound” on the probability of constraint violation. More importantly, it is proven that the solution of the relaxed problem converges to that of the original robust optimization problem as the degree of the optimal polynomial kinship function increases. Furthermore, by relying on quadrature formulas for computation of integrals of polynomials, it is shown that the computational complexity of the proposed approach is polynomial in the number of uncertain parameters. Finally, unlike other deterministic approaches to robust polynomial optimization, the number of variables in the ensuing optimization problem is not increased by the proposed approximation. An important feature of this approach is that a significant amount of the computational burden is shifted to a one-time offline computation whose results can be stored and provided to the end-user. [ABSTRACT FROM PUBLISHER]

The first part of this note identifies and analyzes two weak points of the commented paper refid="ref1"/. i) Example 1 reports an incorrect result for which we provide a complete explanation and correction. ii) The practical use of Theorem 1 reveals major drawbacks such as a severe limitation in the applicability (due to the restrictive conditions requested by the hypothesis) and a relatively low accuracy of the results (i.e., rough values for the right outer bounds of the eigenvalue ranges of interval matrices). The second part of the note presents three methods that circumvent the use of Theorem 1 refid="ref1"/ and provide the following information about the eigenvalue range of an interval matrix: \bullet a right outer bound—relying on an inequality proved by J. Rohn (see References); \bullet the right end point—by solving a constrained global maximization problem; \bullet a right outer bound (for arbitrary interval matrices) and the right end point (for some classes of interval matrices)—by calculating the spectral abscissa of a constant matrix that majorizes the considered interval matrix. For illustration by numerical examples, throughout the note we use three interval matrices related to Example 1 from the commented paper. [ABSTRACT FROM AUTHOR]

A worst-case identification method in frequency domain is proposed to cope with the identification of errors-in-variables models (EIVMs) in closed loop. With a priori bound for the disturbing noises of an EIVM in closed loop, a frequency-domain normalized coprime factor model (NCFM) with perturbation is derived and thus the identification of the EIVM becomes that of the NCFM. By employing the v-gap metric as an optimization criterion, the worst-case error for an identified nominal NCFM is easily quantified and the parameter optimization can be effectively solved by linear matrix inequalities (LMIs). During the parameter optimization, the derivative of the nominal NCFM is constrained to some degree to reduce the effect of overfitting phenomenon. Different from other EIVM identification methods, we use v-gap metric to characterize the disturbing noises and quantify the worst-case error for the nominal NCFM. As a result, the identification result is not a deterministic model but a model set. Moreover, this model set can be perfectly combined with the robust controller design. Finally, a numerical simulation is presented to verify the proposed method. [ABSTRACT FROM AUTHOR]

This paper deals with a method for the approximation of a spectral density function among the solutions of a generalized moment problem à la Byrnes/Georgiou/Lindquist. The approximation is pursued with respect to the Kullback–Leibler pseudo-distance, which gives rise to a convex optimization problem. After developing the variational analysis, we discuss the properties of an efficient algorithm for the solution of the corresponding dual problem, based on the iteration of a nonlinear map in a bounded subset of the dual space. Our main result is the proof of local convergence of the latter, established as a consequence of the central manifold theorem. Supported by numerical evidence, we conjecture that, in the mentioned bounded set, the convergence is actually global. [ABSTRACT FROM AUTHOR]

In this paper, the robust state feedback stabilization of uncertain discrete-time constrained nonlinear systems in which the loop is closed through a packet-based communication network is addressed. In order to cope with model uncertainty, time-varying transmission delays, and packet dropouts (typically affecting the performances of networked control systems), a robust control scheme combining model predictive control with a network delay compensation strategy is proposed in the context of non-acknowledged UDP-like networks. The contribution of the paper is twofold. First, the issue of guaranteeing the recursive feasibility of the optimization problem associated to the receding horizon control law has been addressed, such that the invariance of the feasible region under the networked closed-loop dynamics can be guaranteed. Secondly, by exploiting a novel characterization of regional Input-to-State Stability in terms of time-varying Lyapunov functions, the networked closed-loop system has been proven to be Input-to-State Stable with respect to bounded perturbations. [ABSTRACT FROM AUTHOR]

In this work, a sampled-data nonlinear observer is designed using a continuous-time design coupled with an inter-sample output predictor. The proposed sampled-data observer is a hybrid system. It is shown that under certain conditions, the robustness properties of the continuous-time design are inherited by the sampled-data design, as long as the sampling period is not too large. The approach is applied to linear systems and to triangular globally Lipschitz systems. [ABSTRACT FROM AUTHOR]

This paper deals with ℋ∞ delay-scheduled control of linear systems with time-varying delays. First, a new model transformation is given which allows to provide a unified approach to stability analysis and state-feedback control synthesis for time-delay systems represented in "linear fractional transformation (LFT)" form. A new type of controller is then synthesized, where the state-feedback is scheduled by the value of the delay (delay-scheduled controllers). The results are provided in terms of linear matrix inequalities (LMIs) which are known to be efficiently solvable. [ABSTRACT FROM AUTHOR]

This note considers the problem of the optimal "steady-state" tracking for an unknown first-order plant with an unknown control delay, under the assumption of known upper bounds on model parameters and the control delay. The plant is subjected to perturbations in output and control as well as an exogenous disturbance with unknown upper bounds. The solution of the problem is based on treating the control criterion, which is the worst-case steady-state value of an error signal, as the identification criterion. [ABSTRACT FROM AUTHOR]

Higher order sliding mode (HOSM) control design is considered for systems with a known permanent relative degree. In this paper, we introduce the Robust Fuller's Problem that is a robust generalization of the Fuller's problem, a standard optimal control problem for a chain of integrators with bounded control. By solving the Robust Fuller's Problem it is possible to obtain feed. back laws that are HOSM algorithms of generic order and, in addition, provide optimal finite-time reaching of the sliding manifold. A common difficulty in the use of existing HOSM algorithms is the tuning of design parameters: our methodology proves useful for the tuning of HOSM controller parameters in order to assure desired performances and prevent instabilities. The convergence and stability properties of the proposed family of controllers are theoretically analyzed. Simulation evidence demonstrates their effectiveness. [ABSTRACT FROM AUTHOR]

In this note, a robust model predictive control scheme for constrained discrete-time nonlinear systems affected by bounded disturbances and state-dependent uncertainties is presented. In order to guarantee the robust satisfaction of the state constraints, restricted constraint sets are introduced in the optimization problem, by exploiting the state-dependent nature of the considered class of uncertainties. Moreover, unlike the nominal model predictive control algorithm, a stabilizing state constraint is imposed at the end of the control horizon in place of the usual terminal constraint posed at the end of the prediction horizon. The regional input-to-state stability of the closed-loop system is analyzed. A simulation example shows the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

We consider the problem of designing a control law for a constrained linear system with bounded disturbances that ensures constraint satisfaction over an infinite horizon, while also guaranteeing that the closed-loop system has bounded ℓ2 gain. To this end, we propose a receding horizon control strategy based on the repeated calculation of optimal finite horizon feedback policies. We parameterize these policies such that the input at each time is an affine function of current and prior states, and minimize a worst-case quadratic cost where the disturbance energy is negatively weighted as in H∞ control. We show that the resulting receding horizon controller has two advantages over previous results for this problem. First, the policy optimization problem to be solved at each time step can be rendered convex-concave, with a number of decision variables and constraints that grows polynomially with the problem size, thereby making its solution amenable to standard techniques in convex optimization. Second, the achievable ℓ2 gain of the resulting closed-loop system is bounded and non-increasing with increasing control horizon. A numerical example is included to demonstrate the improvement in achievable ℓ2 gain relative to existing methods. [ABSTRACT FROM AUTHOR]

The scenario optimization method developed in [5] is a theoretically sound and practically effective technique for solving in a probabilistic setting robust convex optimization problems arising in systems and control design, that would otherwise be hard to tackle via standard deterministic techniques. In this note, we explore some further aspects of the scenario methodology, and present two results pertaining to the tightness of the sample complexity bounds. We also state a new theorem that enables the user to make a-priori probabilistic claims on the scenario solution, with one level of probability only. [ABSTRACT FROM AUTHOR]

This technical note presents a new, simple and effective, four-parameters proportional-integral-derivative (PID) optimization method. The set of adjustable parameters is defined by the proportional gain k, integral gain ki, damping ratio of the controller zeros (DRCZ), and desired value of the sensitivity to measurement noise Mn. Given Mn and desired value of the maximum sensitivity Ms,for both maximization of k and maximization of ki, only three nonlinear algebraic equations need to be solved for a few values of DRCZ. Contrary to the method based on maximization of ki,in the method based on maximization of k the improvement of performance is obtained by decreasing DRCZ from 1 to the value corresponding to the minimum of the integrated absolute error (IAE). Moreover, this is achieved without deteriorating robustness to the model uncertainties, for a large class of stable processes. Compared to the recently proposed PID optimization methods, for the same Ms and Mn lower values of IAE and Mp are obtained by using the method presented here. [ABSTRACT FROM AUTHOR]

Abstract-A formal theoretical explanation of the model-mismatch instability problem associated with certain adaptive control design schemes is proposed, and a solution is provided. To address the model-mismatch problem, a primary task of adaptive control is formulated as finding an asymptotically optimal, stabilizing controller, given the feasibility of adaptive control problem. A class of data-driven cost functions called cost-detectable is introduced that detect evidence of instability without reference to prior plant models or plant assumptions. The problem of designing adaptive systems that are robustly immune to mismatch instability problems is thus placed in a setting of a standard optimization problem. We call the result safe adaptive control because it robustly achieves adaptive stabilization goals whenever feasible, without prior assumptions on the plant model and, hence, without the risk of model-mismatch instability. The result improves the robustness of previous results in hysteresis switching control, both for discrete and for continuously-parameterized candidate controller sets. Examples are provided. [ABSTRACT FROM AUTHOR]

It is shown that (pre)asymptotic stability, which generalizes asymptotic stability, of a compact set for a hybrid system satisfying mild regularity assumptions is equivalent to the existence of a smooth Lyapunov function. This result is achieved with the intermediate result that asymptotic stability of a compact set for a hybrid system is generically robust to small, state-dependent perturbations. As a special case, we state a converse Lyapunov theorem for systems with logic variables and use this result to establish input-to-state stabilization using hybrid feedback control. The converse Lyapunov theorems are also used to establish semiglobal practical robustness to slowly varying, weakly jumping parameters, to temporal regularization, to the insertion of jumps according to an "average dwell-time" rule, and to the insertion of flow according to a "reverse average dwell-time" rule. [ABSTRACT FROM AUTHOR]

In this paper, we present robust stability results for constrained discrete-time nonlinear systems using a finite-horizon model predictive control (MPC) algorithm for which we do not require the terminal cost to have any particular properties. We introduce a definition that attempts to characterize the robustness properties of the MPC optimization problem. We assume the systems under consideration satisfy this definition (for which we give sufficient conditions) and make two further assumptions. These are that the value function is bounded by a K∞ function of a state measure (related to the distance from the state to some target set) and that this measure is detectable from the stage cost used in the MPC algorithm. We show that these assumptions lead to stability that is robust to sufficiently small disturbances. While in general the results are semiglobal and practical, when the detectability and upper bound assumptions are satisfied with linear K∞ functions, the stability and robustness are either semiglobal or global (with respect to the feasible set). We discuss algorithms employing terminal inequality constraints and also provide a specific example of an algorithm that employs a terminal equality constraint. [ABSTRACT FROM AUTHOR]

In this note the problem of restricted complexity stability margin maximization (RCSMM) for single-input-single-output (SISO) plants affected by rank one real perturbations is considered. This problem amounts to maximizing the real l2 parametric stability margin over an assigned class of restricted complexity controllers, which are described by rational transfer functions of fixed order with coefficients depending affinely on some free parameters. It is shown that the RCSMM problem, which is nonconvex in general, can be approached by means of convex optimization methods. Specifically, a lower bound of the stability margin, whose maximization can be accomplished via linear matrix inequality (LMI) techniques, is developed. [ABSTRACT FROM AUTHOR]