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Model predictive control (MPC) is based on the systematic resolution of an online optimization problem at each time step. In practice, the computation cost is often very high, especially for the non-linear case under constraints, thus complicating the application of MPC to real-time systems. This paper proposes to improve the non-linear quadratic dynamic matrix control (NLQDMC) algorithm for MPC by solving constrained optimization problems only when necessary, and defaulting to the unconstrained solution whenever possible. The new algorithm is called fast NLQDMC (FNLQDMC) and is applied to the control of a nonlinear system comprised of converter and a DC machine, and implemented in a microcontroller board. The results obtained show that, depending to the setpoint profiles, this algorithm saves more than 64% computation of the constrained problem compared to the conventional NLQDMC, while keeping identical performance in terms of setpoint tracking and constraint satisfactions.

This paper proposes augmenting the traditional quadratic event-triggered conditions for Takagi-Sugeno (TS) fuzzy models, by also considering triggering conditions defined by a variation of the membership function values. Suitable Linear Matrix Inequality (LMI) conditions are presented that make full use of the proposed modified triggering conditions to co-design the control law and triggering conditions. Under some mild assumptions, the closed loop system is guaranteed to avoid the Zeno behavior for its triggering instants. A numerical example is presented to illustrate the proposed conditions.

The paper presents a worst-case touchdown condition analysis of an autolanded aircraft under crosswind. Aircraft dynamics are generally considered altitude- and speed-dependent and, thus, well covered inside the linear parameter-varying framework. Following a specific parameter trajectory tracking the instrument landing system’s guidance signal, its dynamics amount to a finite horizon linear time-varying (LTV) system. This allows to explicitly respect the varying dynamics during the flare maneuver, changing control laws, and the finite horizon of the approach using a finite horizon LTV analysis. A time-varying trajectory uncertainty is proposed to respect the effects of different environmental conditions and aircraft parameters in the analysis. By representing the uncertainty as an integral quadratic constraint (IQC), recent advances in the worst-case gain analysis of finite horizon LTV systems can be utilized. The analysis condition is based on a parameterized Riccati differential equation, which leads to an efficiently solvable nonlinear optimization problem. Applying the robust LTV framework, worst-case touchdown conditions of an autolanded large airliner under crosswind wind are calculated. The obtained results are evaluated against Monte Carlo analyses of the nonlinear model.

This paper establishes a methodology based on linear matrix inequalities (LMIs) to design a shifting H∞ linear parameter varying (LPV) state-feedback controller for systems affected by time-varying input saturations. By means of the shifting paradigm, the instantaneous saturation values are linked to a scheduling parameter vector. Then, the disturbance rejection is dealt with the quadratic boundedness concept and the shifting H∞ methodology. The design conditions are obtained within the LPV framework using ellipsoidal invariant sets, thus obtaining an LMI-based feasibility problem that can be solved via available solvers. Finally, the main characteristics of the proposed approach are validated by means of an illustrative example.

In this work, the H∞ decentralized reduced order observer based control for a class of large scale nonlinear stochastic systems is concerned. In this context we consider subsystems which are interconnected by some nonlinear interconnections under quadratic boundedness and Lipschitz property of the system. The proposed control law is based on the resolution of some LMI.

A Lyapunov design of a least-squares model-reference adaptive control (LS-MRAC) algorithm is presented. The plants considered are continuous with relative degree one. A Monopoli multiplier, originally proposed to extend the MRAC algorithm to the case of relative degree two, is introduced. As a result, fast convergence of the tracking error is achieved and, moreover, the Lyapunov analysis shows that a quadratic term depending on the parametric error belongs to L2, which improves the stability properties of the system. This is the key feature that allows a more powerful LS algorithm to be employed in the update law. The resulting LS-MRAC seems to be a missing algorithm in the literature. Simulation results illustrate the improvement in the transient behavior as well as in the parameter convergence attained with the proposed adaptive schemes.

This paper presents a data-driven predictive control (DPC) algorithm for linear time-invariant (LTI) systems in the behavioral framework. The system is described by the parametrization of the Hankel matrix constructed from its measured trajectories. The proposed structure follows a two-step procedure. The existence of a controlled behavior is firstly verified from the perspective of dissipativity with the aid of quadratic difference forms (QdFs), then the controlled trajectory is selected from the original uncontrolled behavior through optimization. An illustrative example is presented to demonstrate the effectiveness of the proposed approach.

This paper proposes a mixed active/passive robust fault detection and isolation (FDI) method for discrete-time linear paramter varying (LPV) systems based on set-theoretic unknown input observers (SUIO) and invariant sets. The robustness against system uncertainties in FDI of LPV systems can be guaranteed by actively decoupling or passively bounding their effect on residual signal. Furthermore, the quadratic H∞ stability condition of the LPV-form state-estimation-error dynamics is established based on a group of linear matrix inequalities (LMIs). Under the precondition of stability, a family of residual sets are constructed to establish set-separation guaranteed fault isolation (FI) conditions using invariant sets off-line. As long as the occurred faults satisfy the guaranteed FI conditions, they can be isolated from each other. At the end, a numerical example is used to illustrate the effectiveness of the proposed method.

In this paper, we consider networked control systems under Denial-of-Service (DoS) attacks. The control objective is to synthesize a quantized controller in which the quantizer is as coarse as possible for a networked control system subject to DoS attacks, while still guaranteeing (quadratic) stability. Our main result will explicitly show the trade-offs between system robustness against DoS and quantizer coarseness. A simulation example will demonstrate the strengths of the new method.

The goal of this work is to identify the optimal operating input for a lithiation reaction that is performed in a highly innovative pilot scale continuous flow chemical plant in an industrial environment, taking into account the process and safety constraints. The main challenge is to identify the optimum operation in the absence of information about the reaction mechanism and the reaction kinetics. We employ an iterative real-time optimization scheme called modifier adaptation with quadratic approximation (MAWQA) to identify the plant optimum in the presence of plant-model mismatch and measurement noise. A novel NMR PAT-sensor is used to measure the concentration of the reactants and of the product at the reactor outlet. The experiment results demonstrate the capabilities of the iterative optimization using the MAWQA algorithm in driving a complex real plant to an economically optimal operating point in the presence of plant-model mismatch and of process and measurement uncertainties.

This paper presents a new approach to include thrust and mass uncertainties in the worst case loads analysis of launch vehicles during the atmospheric ascend. The analysis is based on recent results on the worst case gain computation of uncertain, finite time horizon linear time varying (LTV) systems. Representing the uncertainties as integral quadratic constraints, the worst case gain condition can be formulated as a parameterized Riccati differential equation (RDE). While this framework allows including certain parametric uncertainties, e.g., aerodynamic uncertainties, it is not straightforward to include thrust uncertainty in the launcher analysis. The reason being that there is an inherent coupling between the thrust and the mass of the launcher, such that any uncertainty in the thrust also effects the mass of the launcher. Further, both thrust and mass have a direct effect on the launch trajectory, whereas the LTV model is obtained via linearization along the nominal trajectory. Hence, it is no longer valid, for large perturbations of the launch trajectory. The former issue is resolved in the paper by including a mass state in the launcher model and treating the thrust uncertainty as an external disturbance. For the latter problem it is proposed to cover a set of launch trajectories with a dynamic uncertainty. Using the robust LTV framework, a worst case aerodynamic loads analysis under thrust uncertainty and wind disturbances is performed in this paper. The results are compared to a Monte Carlo simulation on a high fidelity nonlinear launcher model.

This paper deals with the optimal operation of a continuously operated laboratory membrane separation plant. The goal is to find an economically optimal regime of operation using the transmembrane pressure (TMP) and the operating temperature as adjustable set-points for the low-level controllers. The main challenge is to identify the optimum in the absence of an accurate process model. We employ an iterative real-time optimization scheme, modifier adaptation with quadratic approximation (MAWQA), to identify the plant optimum in the presence of the plant-model mismatch and measurement noise. Two experiments are performed; one with and one without a productivity constraint. The experimental results show the capabilities of the MAWQA scheme to identify the process optimum in real-world scenarios. The optimum identified by the MAWQA scheme coincides with the optimum of a surrogate model that was built using a larger data set.

We consider a linear-quadratic-Gaussian optimal control problem where the sensor and the controller are remotely connected over a communication channel. The communication of the measurement from the sensor to the controller requires a certain cost which is augmented with the quadratic control cost. We formulate a control and communication co-design problem where we solve for the joint optimal pair of controller and transmitter. We emphasize on the fact that absence of measurement communication at any time instance also conveys certain information to the controller, and such implicit information should be taken into account while designing a controller. We decompose the problem into two subproblems to construct the optimal controller and the optimal transmitter. While the optimal controller can be constructed by solving a certain Riccati equation, the optimal transmitter can be found solving a certain dynamic programming problem. We first characterize a sub-optimal solution for this dynamic program and then design an iterative algorithm to further improve the sub-optimal solution.

This manuscript proposes a robust switched controller design with minimization of an upper bound of a quadratic performance index (guaranteed cost) related to the system output for a class of uncertain nonlinear systems with actuator saturation described by Takagi-Sugeno (TS) fuzzy models. The switched control eliminates the necessity of finding or estimating the membership functions, which can be uncertain or complex to obtain. In most practical implementations, systems and actuators have physical limitations. Therefore, in order to approximate the theoretical switched controller design closer to its implementation, it will be considered that the system has an operating region and control signal saturation. The proposed switched controller design will be implemented in a bench active suspension system considering actuator saturation with uncertain mass and actuator fault. A comparison will be shown between robust single-gain and switched controller with the same design parameters.

In this report we illustrate that nonlinear control and observer design can be described as a Positivstellensatz problem, we consider a quadratic candidate function to stabilize nonlinear systems that have been modeled via the nonlinear sector methodology as an exact convex representation. New shape-independent conditions for satisfying positiveness in double sums are established based on the concept of copositivity. The conditions obtained are compared with previous approaches found in the literature via a numerical example.

This paper presents a robust linear time varying (LTV) analysis of a space launcher. It evaluates the worst case aerodynamic loads during the atmospheric flight phase under wind turbulence. The results of the LTV analysis are evaluated against a Monte Carlo simulation of the respective industry-sized nonlinear launcher model over a set of allowable uncertainties and disturbances. The recent extension of the strict/ finite horizon bounded real lemma (BRL) for integral quadratic constraints is applied, covering the robustness analysis of finite horizon linear time varying (LTV) systems. Here, an equivalent formulation of the strict BRL in the form of a Riccati differential equality (RDE) is used. The RDEs direct dependence on the IQC multiplier leads to a nonlinear optimization problem, which can be solved efficiently. Using this, it is possible to explicitly respect the time varying dynamics of the space launcher in the worst case analysis. Furthermore, the incorporation of IQCs opens the analysis to a broader range of analyzable perturbations.

This paper deals with the design and analysis of a linear parameter-varying (LPV) path-following controller for an autonomous underwater vehicle (AUV). The LPV controller is designed to provide guaranteed performance for any planar path whose inverse radius of curvature is bounded. Such a flexibility provided by the LPV controller offers significant advantages in AUV missions such as seabed mapping and mine countermeasures. The control design approach uses a lumped system model that is based on a virtual vehicle formulation and combines the six degrees-of-freedom AUV dynamics with the path-following dynamics. An LPV path-following controller is designed using the l2-induced norm as the performance measure and the inverse radius of curvature of the path as the scheduling parameter. The penalty weights of the controller are tuned using a tool that utilizes integral quadratic constraint (IQC) theory to perform robustness analysis. The robustness and performance of the LPV controller are studied using IQC analysis and non-deterministic MATLAB simulations where hydrodynamic model uncertainties, unmodeled servo dynamics, AUV nonlinear dynamics, and sensor noise are considered.

Nonminimum phase systems are presented in many industrial processes bringing some difficulties in their control scheme. In this paper we propose a methodology to tune multi-input-multi-output proportional-integral-derivative controllers that deals this particularity. The controller design employed here is based in an attainable trajectory determination, for the nonminimum phase system, that considers performance and robustness metrics. Based on that, two different tuning procedures are proposed: an optimization problem, in the frequency domain, that involves nonconvex quadratic matrix inequalities with a linear matrix inequality restriction and a time domain optimization of the simulated system. The methods are an alternative for tuning this kind of system leading the process to the best operational scenario presenting stability, robustness and limited control actions.

A model-predictive scheme for load frequency control of a multi-area power system is proposed. The method depends on a decoupling technique which allows for a control design with a distributed architecture. Treating the total power inflows of each area as input variables, a decoupled linearized model for each area is derived. This allows for the formulation and solution of a model predictive control problem with a quadratic performance index and input saturating constraints on the individual tie-line power flows, along with an overall equality constraint to address the energy balance of the network. It is assumed that the interconnection topology (tie-lines) coincides with the communication topology of the network. The only information which needs to be shared between interconnected areas is the local frequency variables. The effectiveness of the method is illustrated via a simulation study of a three-area network. Future work will attempt to establish formally the stability of the control scheme and to enhance the versatility of the method by including constraints on the state variables.

In this work, the application of a reliable iterative real-time optimization (RTO) scheme to a continuously operated transition metal complex catalyzed process for the hy-droformylation of 1-dodecene is presented. The aim of the proposed scheme is to ensure optimal operation despite the presence of model uncertainties and measurement errors. Iterative optimization using Modifier Adaptation with Quadratic Approximation (MAWQA) is applied. Furthermore, additional modules for steady-state identification (SSI) and robust data reconciliation (DR) were designed and implemented. The proposed scheme was commissioned in a real miniplant, and a significantly improved performance in comparison to the optimal operating point that was obtained from the nominal process model was achieved.

In case when a nonlinear system is represented by quasi-LPV model with bounded disturbance, we adopt the parameter-dependent dynamic output feedback MPC (PDDOFMPC) with guaranteed quadratic boundedness and physical constraints. We pre-specify one sequence of nested ellipsoids for estimated state, and another for estimation error, then calculate PDDOFMPC parameters for each combination of estimated state ellipsoid and estimation error ellipsoid. A look-up table is constructed off-line to store these controller parameters each related to a unique combination. On-line, the estimated state is iterated and the estimation error set is refreshed. By checking the smallest off-line estimated state ellipsoid to contain the real-time estimated state, and the smallest off-line estimation error ellipsoid to include the real-time estimation error set, a unique set of control parameters is taken from the look-up table at each sampling instant. An example is given to illustrate the effectiveness of the approach.

Control of a system exhibiting input multiplicity at an optimum (singular) operating point poses a challenging control problem due to loss of invertibility and change in the sign of the steady state gain in the neighbourhood of the optimum. In this work, for controlling systems exhibiting input multiplicities, we develop a novel adaptive dual NMPC (ADNMPC) formulation based on a Wiener model which is parameterized using orthonormal basis filters (OBF). The static nonlinear output map in the Wiener model is constructed using multidimensional quadratic polynomials. The OBF poles are chosen through an off-line identification exercise and the parameters of the static nonlinear map are updated on-line using recursive least squares algorithm. Similar to Kumar et al. (2017), by introducing concept of excitation horizon, the objective function in the NMPC formulation is modified to include terms that are sensitive to the parameter covariance. The proposed formulation provides su¢cient degrees of freedom to shape the probing signals. E¢cacy of the proposed approach is demonstrated by simulating problem of controlling a continuously operated fermenter system at its optimum operating point. Analysis of the simulation results shows that the proposed ADNMPC scheme judiciously injects perturbations into the process as and when required. The probing perturbations subside when the parameter estimates stabilize. In particular, it is observed that the intensity of the perturbation increase as the excitation horizon increases. The proposed on-line model adaptation also ensures better control performance with reference to a non-adaptive NMPC controller.

The control strategies for batch processes in the past can be categorized into two levels. The higher level is economic optimization running at low frequency and the lower one tracks the given reference using MPC or PID at the higher level. The lower level regards all the disturbances as something to be rejected according to a quadratic based optimization objective. However, not all the disturbances are unfavorable to batch processes; some of them are helpful. In this paper, an economic batch-to-batch optimization method for batch processes is directly applied at the lower level. It replaces the former tracking strategy. With the information of disturbances collected from the previous batches, the iterative learning control strategy (ILC) can find out better operation profiles. ILC has the advantage of continuously improving the economic performance of the current batch with enriched information of disturbances from batch to batch. To demonstrate the potential applications of the proposed design method, a typical fed-batch bioreactor is applied.

This paper proposes a Fractional Steepest Ascent Method (FSAM) for detecting faults in Traction Control Unit (TCU), which aims at detecting the transient fault caused by electromagnetic radiation, overheating, violent vibration, etc. In FSAM, the maximum value of the signal quadratic energy functional is obtained in time domain by means of Grumwald Letniko (G-L) fractional order technique. For fault detection purpose, the detection threshold is determined with the Root Mean Square (RMS) of the velocity residual absolute value and the fractional order is optimized by searching the maximum ratio of RMS. The simulation results verify that the method proposed is not only sensitive to the TCU fault signals, but also can effectively suppress the noise to some extent.

An adaptive speed observer for mechanical systems, which are linear in the velocity (momenta) after a change of coordinates (Venkatraman et al. [2010]), was recently reported in the literature (Romero and Ortega [2015]). The velocity observer considers input disturbances and some unknown friction parameters in the design. In this note, we relax some assumptions imposed in that work so that, the class of systems is enlarged and quadratic terms in the velocity(momenta) and all the friction parameters are included in the design of the velocity observer. Simulation results using the robotic–leg system illustrate the performance of the proposed adaptive observer.

In this paper, a new approach is proposed to design optimal PID controller for load frequency control (LFC) problem. This scheme is based on Quadratic Regulator Approach with Compensating Pole (QRAWCP) technique. Application of this control law is done to both single area and multi-area power system based load frequency problem. In addition to the nominal situation, robustness of this controller is also tested on the same systems with respect to parametric uncertainty, external disturbances, and non-linearities like Generation Rate Constraint (GRC) and Governor Dead Band (GDB). The performance evaluation is done using Matlab & Simulink based simulations and the obtained results are compared with the performance achieved using the recent control strategies designed for LFC.

This paper addresses the problem of mimicking short range air-to-surface ballistic trajectories using a feedback guidance law. An adaptive proportional navigation law is proposed satisfying the launch and impact position, and heading direction of the ballistic trajectory. Simulations are carried out for mimicking ideal and quadratic drag ballistic trajectories. Mimicking error studies present the efficacy of the proposed guidance method. Robustness of the proposed guidance method is verified against wind disturbances.

This paper proposes a scheme of robust model predictive control (MPC) for discrete-time nonlinear jump Markov systems (JMS) considering polytopic state and input constraints. The approach consists of an offline part and the online optimization: First, a time-varying linear JMS representation of the nonlinear JMS is used to determine time-varying feedback controllers and robust control invariant sets (RCIS) offline, solving a semi-definite program (SDP). Then, an MPC formulation uses online linearization, the determined RCIS, and Lyapunov functions to guarantee constraint satisfaction, mean square stability, and recursively feasibility. The proposed formulation is robust against bounded disturbances and the linearization errors. A simulation study demonstrates that the involved quadratically constrained quadratic programs (QCQP) can be solved quite efficiently, rendering this approach applicable for high-dimensional systems.

In the paper, a methodology of actuator fault estimation and fault tolerant control for nonlinear systems is proposed. To solve such a challenging problem, a quadratic boundedness (QB) approach is used to design observer in such a way that the state and fault estimation error will converge to the origin (as close as possible) irrespective of the external disturbances. The same approach (QB) is used to design fault tolerant control to compensate the effect of an actuator fault in system, which is suitably integrated with state and fault estimation schemes. Sufficient conditions for the existence and stability of the proposed state and fault estimation scheme as well as the fault tolerant control are expressed in the form of linear matrix inequalities (LMIs). To show effectiveness of the proposed approach, the final part of the paper presents simulation results concerning a quadruple–tank system.

In this paper, the problem of local observer design for discrete-time Takagi-Sugeno fuzzy systems is addressed. The observers being designed are local in the sense that relaxed asymptotic convergence conditions are sought locally, rather than globally, while also determining the region where the conditions are valid. The design conditions are derived using both quadratic and non-quadratic Lyapunov functions. The obtained results are expressed in terms of linear matrix inequalities and they are illustrated on a numerical example.

This paper proposes a linear parameter varying (LPV) model reference-based control for fixed-wing unmanned aerial vehicles (UAVs), which achieves agile and high performance tracking objectives in extended flight envelopes, e.g. when near stall or deep stall flight conditions are considered. Each of the considered control loops (yaw, pitch and airspeed) delivers an error model that can be reshaped into a quasi-LPV form through an appropriate choice of the scheduling variables. The quasi-LPV error models are suitable for designing error feedback controllers using linear matrix inequalities (LMIs), which are derived within the quadratic Lyapunov framework. Simulation results are used to show the effectiveness of the proposed approach.

We consider the sparsity-promoting Linear Quadratic Gaussian (LQG) control for a network of linear systems. A sparse LQG controller requires fewer communication channels with the networked system. The bidirectional communication implies that the column-block-sparsity pattern of the Kalman filter and the row-block-sparsity pattern of the state feedback controller must be the same. We develop a two-step procedure: the first step being a sparsity-promoting design of the Kalman filter and the second step leveraging the structure identified in the first step to design the respective controllers. In both steps we employ the Alternating Directions Method of Multipliers (ADMM) and show its effectiveness for the identification and the structural LQG design. In particular, ADMM allows us to decompose the structured LQG problem into a sequence of convex quadratic problems that can be solved efficiently. We demonstrate via numerical experiments that our approach results in sparse controllers whose performance is comparable to that of the fully centralized controllers.

Iterative Real-Time Optimization (RTO) has gained increasing attention in the context of model-based optimization of the operating points of chemical plants in the presence of plant-model mismatch. In all iterative RTO schemes, it is necessary to wait until the plant has reached a steady-state to obtain the required information on plant performance and constraint satisfaction which leads to slow convergence in the case of processes with slow dynamics. It has recently been proposed to use a linear black-box model that is identified online to predict the steady-state values of the plant during the transient between different stationary operating points; these values are then employed in the modifier adaptation with quadratic approximation to drive the process to its optimum (Gao et al., 2016). In this contribution, this idea is extended by integrating nonlinear system identification into iterative RTO. Specifically, a Nonlinear Output Error (NOE) model is proposed to describe the dynamics of the process, thus providing a faster prediction of the steady-state of the plant. A robust scheme for the estimation of the model parameters is proposed. The performance of the strategy is illustrated by simulation studies of a continuous stirred-tank reactor. By means of the proposed methodology a fast convergence to the plant optimum can be achieved despite plant-model mismatches.

In this paper, an improved approach for the solution of the regulator problem for linear discrete-time dynamical systems with non-Gaussian disturbances and quadratic cost function is proposed. It is known that a sub-optimal control can be derived from the classical LQG solution by substituting the linear filtering part with a quadratic optimal filter. However, classical quadratic filters have some critical drawbacks when the system is not asymptotically stable and, as a consequence in that case, there is no guarantee on the stochastic stability of the controlled system. In order to enlarge the class of systems that can be controlled we will make use of the Feedback Quadratic Filter and a quadratically optimal controller is designed also for non asymptotically stable systems. Numerical results show the performance of these methods.

The problem of velocity estimation in a mechanical system has been paid a lot of attention for the past several years. In this paper we propose a finite-time observer for a class of non-linear systems containing the Lagrangian systems with disturbances. The discontinuous observer design, proposed here, does not need bounded trajectories in the system, as opposed to what is considered in the literature. To deal with the quadratic term in the velocity, a transformation of states is introduced. Dissipative properties are used for observer design and this is reduced to the solution of some matrix inequalities.

This paper examines the application of Regularized Model Predictive Control (RMPC) for Power Management (PM) of Hybrid Energy Storage Systems (HESSs). To illustrate, we apply the idea to the PM problem of a battery-supercapacitors (SCs) powertrain to reduce battery degradation in Electric Vehicles (EVs). While the application of Quadratic MPC (QMPC) in PM of HESS is not new, the idea to examine RMPC here is motivated by its capabilities to prioritize actuator actions and efficiently allocate control effort, as advocated by recent works in the control and MPC literature. Thorough simulations have been run over standard urban test drive cycles. It is found out that QMPC and RMPC, compared to rule-based PM strategies, could reduce the battery degradation over 70%. It is also shown that RMPC can slightly outperform QMPC in reducing battery degradation. Moreover, RMPC, compared to QMPC, could potentially extend the range of that SCs can be used, thus exploiting the degree of freedom of the powertrain to a larger extent. We also make some discussions on the feasibility issues and tuning challenges that RMPC faces, among others.

In this paper we propose a new approach to the design of a rudder roll stabilization system, integrated with a predesigned heading controller (autopilot). Whereas a number of methods for the roll oscillation damping have been proposed in the literature, this problem still remains challenging. In this paper we consider a linear-quadratic optimization problem, where the cost functional penalizes the roll angle, the heading deviation and the control effort (rudder angle). Approximating the wave disturbance by a polyharmonic signal with a known spectrum, we design a controller, which optimizes this quadratic performance index, providing thus a "tradeoff" between the maintenance of the desired heading, roll damping and the steering system utilization. The practical applicability of our algorithms is illustrated by numerical simulations.

The paper considers a discrete repetitive process described by the state-space model with static nonlinearity satisfying quadratic constraints (in particular, the SISO case with the sector bounded nonlinearity). Based on the vector Lyapunov function approach developed by the authors, sufficient conditions of pass profile exponential stability are obtained in the form of linear matrix inequalities. These conditions are then applied to the design of an iterative learning control law for a simple nonlinear mechanical system.

This paper considers local stability analysis of discrete-time Takagi-Sugeno fuzzy systems, for which classically in the TS literature only global stability is considered. Using a common quadratic and nonquadratic Lyapunov function, respectively, LMI conditions are developed to establish local stability of an equilibrium point. An estimate of the region of attraction of this point is also determined. The developed conditions are illustrated on a numerical example.

This paper presents a new approach to input design for closed-loop identification. The idea is to maximize the trace of the Fisher information matrix associated with the plant model, while enforcing explicit constraints on both inputs and outputs. The result is the richest possible excitation signal for which the operation of a running closed-loop system remains within acceptable bounds. The function to be maximized is a convex quadratic. A Moving Horizon Predictive (MHP) framework is used to solve the input design problem at each sample time. The method can be combined with a fixed model variable regressor technique to estimate time delays. The suggested technique is implemented and used to identify machine-directional processes in an industrial paper machine.

In permanent magnetic machines the nonlinearity due to the quadratic terms of the current makes difficulties in the control system. In order to cancel the nonlinearity, a controller based on a controlled invariant subspace for the currents is proposed. Simulation results using real data measurements are reported to validate the proposed technique.

In this paper we propose a novel MPC scheme based on set theory that controls the temperature distribution in body tissue with the purpose of realizing local hyperthermia for improved cancer treatment. A new scheme is needed as there is no equilibrium nor invariant subset in the set of desirable temperature distributions subject to input and output constraints. The scheme fulfills the control objectives by using a dual-mode control strategy that steers the initial state to an appropriate reference set and keeps it inside the set of desirable temperature distributions for the (finite) length of the therapy duration. Feasibility of the scheme is guaranteed by off-line set-based computations. The online computations are formulated as standard quadratic programs. A numerical example is included to demonstrate the successful control of the transient temperature distribution in a one dimensional space.

Developed in the paper, is a systematic design framework for parameter-dependent fault diagnosis, with noise corrupted measurements of the scheduling parameter. A stochastic fault estimation filter design method is addressed for a class of discrete-time Linear Parameter Varying (LPV) systems. First, the average valued filter stability is ensured by means of a quadratic dissipativity condition. Apart stability, within the same mean square framework a robust performance objective is defined. Conditions to satisfy robust stability requirements are translated to an optimization problem subject to Bilinear Matrix Inequality (BMI) constraints. A comparative numerical example is given to illustrate and validate the proposed methodology.

In this paper, we develop a new tube-based robust economic MPC scheme for linear systems subject to bounded disturbances with given distributions. By using the error distribution in the predictions of the finite horizon optimal control problem, we can incorporate stochastic information in order to improve the expected performance while being able to guarantee strict feasibility. For this new framework, we can provide bounds on the asymptotic average performance of the closed-loop system. Moreover, a constructive approach is sketched in order to find an appropriate quadratic terminal cost accepting a slight degradation of the average performance statement.

In this talk, the problem of robust stabilization of bilinear control systems is considered. Based on the technique of linear matrix inequalities and quadratic Lyapunov function, the so-called robust stabilizability ellipsoid is designed such that the trajectory of the closed-loop system, starting inside the ellipsoid asymptotically tends to zero for all admissible uncertainties. The results obtained allow to design an approximation of the robust stabilizability domain of the uncertain bilinear control system.

In this study, the finite-horizon fault estimation (FE) problem for discrete-time singular systems affected by random sensor failures is dealt with. Through introducing a partially equivalent dynamic system and its corresponding performance index, the stochastic FE problem is converted into a deterministic quadratic minimization scheme. With the aid of the innovation re-organization technique and the projection formula in Krein space, a necessary and sufficient condition is obtained for the existence of the estimator. The parameter matrices of the estimator are calculated recursively via solving two standard Riccati equations. A numerical example is given to demonstrate the effectiveness and applicability of the proposed method.

This paper considers the problem of synthesizing dynamic output feedback controllers subject to structural sparsity constraints. It is well known that unless the plant and sparsity pattern satisfy the so-called quadratic invariance property, this problem is generically NP-hard. The main result of this paper shows that in these cases, a computationally attractive convex relaxation can be obtained by recasting the problem into an estimation form and exploiting recent results on sparse filter design. As illustrated with an example, the proposed approach compares favorably with existing techniques, in terms of its ability to find a suitable controller and the resulting closed-loop performance.

It is well known that the uncertainty arisen from the aerodynamic torque of wind speed imposes significant challenge for control of wind turbine, especially in the stage of maximum power point tracking (MPPT). In this paper, a sliding mode adaptive observer is proposed to estimate the nonlinear aerodynamic torque, based on which a linear quadratic tracking control optimization algorithm is developed. It is shown that this method can not only ensure precise rotor speed tracking, but also evidently reduce the vibration of the drive train. Both theoretical analysis and numerical simulations validate the effectiveness of the proposed method.

A new model predictive control (MPC) scheme is presented which is characterized by extremely simple and quickly executable online computations suitable for limited hardware implementations. The main idea behind the method is to replace the decision variable of the MPC optimization problem by several univariate parameterizations. This way, several univariate quadratic programs are obtained which can be solved online by executing a simple case analysis. Stability and recursive feasibility guarantees are given for the algorithm. In two numerical examples very simple online computations, high control performance and low storage requirements for the controller are verified.