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A multi-period model for Portfolio optimization problems including a diversification quadratic constraint is considered in this paper. It is designed to minimize the risk, imposing a restriction on the return to be not less than a desired value. An illustrative example with 6 assets, based on data for 131 months historical period is constructed. The formulated optimization problem is solved for 10 different values of desired return by means of the Interior point method in Matlab’s fmincon solver. The experimental results show that the proposed optimization model is effective and successful in solving constrained multi-period portfolio optimization problems.

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.

Enclosing the reachable sets of nonlinear control systems is useful in state estimation and safety verification for chemical process models. We present a new approach for computing time-varying interval bounds for ordinary differential equation models based on differential inequalities. Instead of using interval arithmetic like established approaches, we instead generate bounding information for the right-hand side (RHS) function by optimizing its convex relaxations. Complementarity formulations are explored, and are found to be particularly beneficial if the RHS function is quadratic and we employ the αBB relaxations.

This paper presents a novel distributed active set method for model predictive control of linear systems. The method combines a primal active set strategy with a decentralized conjugate gradient method to solve convex quadratic programs. An advantage of the proposed method compared to existing distributed model predictive algorithms is the primal feasibility of the iterates. Numerical results show that the proposed method can compete with the alternating direction method of multipliers in terms of communication requirements for a chain of masses example.

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.

We derive sufficient conditions for strict dissipativity for optimal control of linear evolution equations on Hilbert spaces with a cost functional including linear and quadratic terms. We show that strict dissipativity with a particular storage function is equivalent to ellipticity of a Lyapunov-like operator. Further we prove under a spectral decomposition assumption of the underlying generator and an orthogonality condition of the resulting subspaces that this ellipticity property holds under a detectability assumption. We illustrate our result by means of an example involving a heat equation on a one-dimensional domain.

In this paper we study the integrability properties of Lie-Poisson adjoint systems associated to four-dimensional sub-Riemannian Lie groups with invariant quasi-contact distributions. We show an equivalence between the facts that this system admits three quadratic functionally independent first integrals, the optimal controls are elliptic functions, and the sub-Riemannian space is symmetric.

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.

The problem of infinite-horizon multi-agent differential games is investigated, where the process can be modeled by a set of uncertain linear dynamics. The players are divided into two teams, one of which consists of a fixed number of follower agents while the other has one leader agent. The two teams constitute the adversaries. The multi-agent differential games can be transformed into a two-player game. The dynamics of the agents are subjected to norm-bound model uncertainties. Based on quadratic stabilization techniques, a set of saddle point strategies of the game is designed to stabilize the closed-loop multi-agent system, where the weighting matrices of the cost function are properly selected. For any given cost function, by modifying the solution of the linear quadratic differential game of the nominal model, the sufficient conditions are presented such that the stabilization of the system is guaranteed and the uncertainties are compensated. It is proved that the modified solution achieves optimality. A numerical example is given to verify the effectiveness of the theoretical results.

In this paper we present a method to exactly certify the iteration complexity of a primal active-set algorithm for quadratic programs which is terminated early, given a specific multi-parametric quadratic program. The primal active-set algorithm’s real-time applicability is, hence, improved by early termination, increasing its computational efficiency, and by the proposed certification method, providing guarantees on worst-case behaviour. The certification method is illustrated on a multi-parametric quadratic program originating from model predictive control of an inverted pendulum, for which the relationship between allowed suboptimality and iterations needed by the primal active-set algorithm is presented.

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 paper, a heuristic path planning approach for the robotic distribution of granular-fill insulation material is presented. The initial coarse manual distribution of the material leads to an uneven surface with areas of excessive or insufficient material. In order to distribute the granular-fill insulation uniformly with a robot, first the worked area is captured as point cloud with an RGB-D camera, and afterwards these irregularities are located via agglomerative hierarchical clustering. Subsequently, their volumes are estimated providing weights for the path calculation. A path planning method, inspired by the usual working method of human construction workers, is developed and applied. In a test scenario, the total path length and the processing sequence are analysed, varying the blade size and the weight of the distance-to-goal parameter. This analysis yields, that the presented path planning algorithm is well suited for the described application, showing the best results with a larger blade size and a quadratic distance-to-goal behavior.

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.

A computational method is proposed for solving a structured quadratic optimal control problem subject to linear discrete-time dynamics arising from a directed tree structured interconnection of heterogeneous sub-systems. The problem is first formulated as a quadratic program with structure along three dimensions of the decision space. A nested algorithm based on block Jacobi iterations is proposed for the linear system of equations obtained from the corresponding first-order optimality conditions. It is shown that the per iteration computational burden scales favorably with increasing problem size in each dimension. The computations at each iteration are amenable to distributed implementation on a network of parallel processors mirroring the tree graph structure of the problem. Numerical experiments, based on a model data for an automated irrigation network, are used to demonstrate aspects of the approach, including the impact of early termination of the inner iterations in agreement with corresponding analysis.

Alarm filtering is a structurally simple, easy to implement, and effective method to improve industrial alarm systems. Owing to these advantages, alarm filters are widely used in industrial applications. Linear and quadratic are the main types of alarm filters. Although a linear filter can detect mean changes, it can not be used to detect variation changes. However, a quadratic filter can be used to detect both types of changes. Although this remarkable feature of quadratic filters has been addressed in the literature, no explicit performance analysis is performed yet. So, deriving an analytical solution for quadratic filters is of paramount importance. To this aim, we propose an analytical method for performance assessment and design of quadratic filters. On the other side, in industrial applications, many process variables are acquired. So one challenge is to identify the process variable that provides the best alarm performance after filtering. We will derive an analytical solution to this problem. Furthermore, we will prove that this optimal solution is a function of the statistical feature of historical data and alarm filter structure.

This article considers the distributed finite-time optimization problem of multi-agent systems within the Zero-Gradient-Sum (ZGS) framework. We employ a distributed algorithm to drive the estimate of each agent to converge to the optimal solution of the global objective function, the sum of the local objectives. In a general case with non-quadratic local functions, we can obtain a finite-time convergence. Furthermore, when all the local cost functions are quadratic, the proposed algorithm can achieve a fixed-time result such that the upper bound of settling time can be estimated regardless of the initial conditions. Considering that the communication network may be affected by some external disturbances, we also extend to consider the case with switching topologies. Finally, the algorithms are demonstrated via an example simulation.

Motivated by the potential for parallel implementation of batch-based algorithms and the accelerated convergence achievable with approximated second order information a limited memory version of the BFGS algorithm has been receiving increasing attention in recent years for large neural network training problems. As the shape of the cost function is generally not quadratic and only becomes approximately quadratic in the vicinity of a minimum, the use of second order information by L-BFGS can be unreliable during the initial phase of training, i.e. when far from a minimum. Therefore, to control the influence of second order information as training progresses, we propose a multi-batch L-BFGS algorithm, namely MB-AM, that gradually increases its trust in the curvature information by implementing a progressive storage and use of curvature data through a development-based increase (dev-increase) scheme. Using six discriminative modelling benchmark problems we show empirically that MB-AM has slightly faster convergence and, on average, achieves better solutions than the standard multi-batch L-BFGS algorithm when training MLP and CNN models., Comment: Accepted at the 21st IFAC World Congress, 12-17 July 2020, Berlin, Germany

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.

We present a method to reduce the computational burden of solving a sequence of convex quadratic programs (QPs). By determining offline what search space is most important, we can restrict our online problem to that subspace, reducing the dimension and computational cost of the QP solver. The process we present is very simple requiring surprisingly little data. Further, we present a modified sequential QP algorithm that leverages the restricted QP approach to solve nonlinear programming problems found in model predictive control. Lastly, we apply these to a benchmark MPC problem and demonstrate their effectiveness using a variety of established QP solvers. We demonstrate that QP problems can be solved faster with minimal MPC performance degradation and highlight future directions for this work.

In this work, we propose a non-parametric technique for online modeling of systems with unknown nonlinear Lipschitz dynamics. The key idea is to successively utilize measurements to approximate the graph of the state-update function using envelopes described by quadratic constraints. The proposed approach is then demonstrated on two control applications: (i) computation of tractable bounds for unmodeled dynamics, and (ii) computation of positive invariant sets. We further highlight the efficacy of the proposed approach via a detailed numerical example.

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 addresses a learning problem for nonlinear dynamical systems with incorporating any specified dissipativity property. The nonlinear systems are described by the Koopman operator, which is a linear operator defined on the infinite-dimensional lifted state space. The problem of learning the Koopman operator under specified quadratic dissipativity constraints is formulated and addressed. The learning problem is in a class of the non-convex optimization problem due to nonlinear constraints and is numerically intractable. By applying the change of variable technique and the convex overbounding approximation, the problem is reduced to sequential convex optimization and is solved in a numerically efficient manner. Finally, a numerical simulation is given, where high modeling accuracy achieved by the proposed approach including the specified dissipativity is demonstrated.

This paper generalises stability analysis of Nonlinear Model Predictive Control without terminal constraints to incorporate possible suboptimality of MPC solutions and develops a framework for minimisation of computational efforts associated with obtaining such a solution. The framework is applied to primal-dual interior-point solvers by choosing the length of the prediction horizon together with a degree of suboptimality of the solution in a way that reduces algorithmic complexity while satisfying certain stability and performance guarantees. The framework ensures an optimal choice for the prediction horizon in order to minimise computational complexity if applied to linear or convex quadratic MPC problems, and acts as a good indicator to this end in the more general case of nonlinear systems. This is illustrated in a numerical case study, where we apply the proposed framework to a nonholonomic robot.

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 the design problem for discrete-time bilinear control systems subjected to arbitrary bounded exogenous disturbances. A procedure for the construction of the stabiliz-ability ellipsoids and stabilizability domain for discrete-time bilinear control systems is proposed and its efficiency is proved. The main tools are the linear matrix inequality technique and the apparatus of quadratic Lyapunov functions. This simple yet general approach is of great potential; for instance, it can be generalized to the various robust statements of the problem.

Many modern optimization problems admit a composite form in which the objective function is given by the sum of a smooth term and a nonsmooth regularizer. Such problems can be solved via proximal methods and their variants, including the Douglas-Rachford (DR) splitting algorithm. In this paper, we view the DR splitting flow as a dynamical system and leverage techniques from control theory to study its global stability properties. In particular, for problems with strongly convex objective functions, we utilize the theory of integral quadratic constraints to prove global exponential stability of the ordinary differential equation that governs the evolution of the DR splitting flow. In our analysis, we use the fact that this algorithm can be interpreted as a variable-metric gradient method on the DR envelope and exploit structural properties of nonlinear terms that arise from composition of reflected proximal operators.

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 paper considers nonconvex quadratically constrained programs over undirected connected graphs. We focus on problems whose quadratic constraint forms have sparse hessians, i.e., each constraint only involves a small subset of variables. We present both centralized and distributed treatment of the problem, where the centralized method is used as a benchmark with which we compare the performance of the proposed distributed algorithm. Towards this objective, we derive a lower bound on the optimal value of the problem using a semidefinite relaxation. Then, we develop a decentralized algorithm based on proximal gradient ADMM that exploits the structure of the constraints to distribute the computations among the graph nodes. Indeed, the proposed algorithm does away with a central node. Each node updates its local variables via solving a simple subproblem, then communicate with its immediate neighbors. An application of this work is the AC optimal power flow problem in power networks for which we provide preliminary numerical results to validate our findings.

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.

We present a Gradient-based extremum seeking algorithm for maximizing unknown maps in the presence of constant delays. It is incorporated a filtered predictor feedback with a perturbation-based estimate for the Hessian of locally quadratic maps. Exponential stability and convergence to a small neighborhood of the unknown extremum point are achieved by using backstepping transformation and averaging theory in infinite dimensions. The low-pass filter (with a high enough pole) in the predictor feedback allows the technical application of the Hale and Lunel’s averaging theorem for functional differential equations and also establishes an inverse optimal result for the closed-loop system. This inverse optimality property is for the first time demonstrated in extremum seeking designs and justifies the heuristic use of a low-pass filter between the demodulation and the integrator, which has historically been a part of the extremum seeking implementations free of delays.

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.

Steelmaking - continuous casting production scheduling is an important part of steelmaking production system. A scientific and reasonable steelmaking - continuous casting production scheduling is the key to improve the production efficiency of the steelmaking - continuous casting production system. In this paper, with the shortest total charge waiting time and the castings that are processed on time as the optimization goal, with the charges that are casted continuous within same casting and machine capacity constraints and so on as the constraints to established a mixed integer programming mathematical model. The augmented Lagrangian relaxation algorithm is used to solved the scheduling problem. The diagonal quadratic approximation method is used to linearize quadratic term so that it can be transformed into a problem to be solved with separable structure. Experiments show that the algorithm proposed has the advantages of fast convergence and short solution time for solving the steel-continuous casting scheduling problem compared to the traditional Lagrange algorithm, and the superiority is more obvious when the scale of the problem is increased.

In this paper, we consider the issue of synthesizing optimal control of nonlinear dynamic systems in case of a problem with an unspecified time of the transition process. To solve this problem, an algorithmic method for constructing a suboptimal control is suggested. The basis of this method is the behavior of the Hamiltonian on the optimal trajectory, which corresponds to the required condition of the optimal system. For the synthesis of suboptimal control, the original nonlinear model is replaced by an equivalent model with a linear structure with state-dependent parameters. The use of the quadratic quality functional allows to make the transition from solving the Hamilton-Jacobi-Bellman equation to the Riccati-type equation with state-dependent parameters. A computer simulation of a simple nonlinear system, for which the Riccati equation with state-dependent parameters can be solved, has been carried out. Also, for this system a suboptimal control was created with the use of a new algorithmic parametric optimization method. The simulation results confirm the effectiveness of the method. In addition, the constructed suboptimal control is close to the control obtained by solving the Riccati equation.

In this paper distributed adaptive linear quadratic control of discrete-time linear large-scale systems with unknown dynamics using distributed reinforcement learning is studied. Linear quadratic control based on dynamic programming (specifically policy iteration) and adaptive linear quadratic control based on reinforcement learning (especially Q learning) are reviewed first. Then distributed adaptive linear quadratic control is addressed. Two Q functions exploiting the quadratic structure of the value function and leading to a decentralized and a distributed policy are proposed and a decentralized as well as a distributed Q learning algorithm are presented. Finally the concepts are evaluated in a simulation study. The simulation results indicate that the distributed policy is near-optimal.

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.

The problem of output-feedback control for a class of semilinear unstable heat equations is addressed within the framework of dissipativity theory. It is shown that using a simple proportional output-feedback control in a collocated input-output pairing the closed-loop stability is ensured if the control gain is chosen sufficiently large and the nonlinearity satisfies a quadratic dissipativity condition. The approach is illustrated using numerical simulations.

Kalman Filters (KF) is a recursive estimation algorithm, a special case of Bayesian estimators under Gaussian, linear and quadratic conditions. For non-linear systems, Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF) provide first and higher order linearization approximations. Particle Filters (PF), on the other hand, are sequential Monte Carlo methods to provide estimations for non-linear non-Gaussian problems. For complex systems, Kalman or Particle Filter based single model filters may not be sufficient to model the system behaviour. Multiple Model (MM) Filters achieve more reliable estimates by using more than one filter with different models in parallel and the outputs of each filter are fused by assigning a probability to each filter. The most common methods used in the literature for multiple model estimation are Multiple Model Adaptive Estimation (MMAE) and Interacting Multiple Model (IMM). This paper presents an overview of the recent research on multiple model filters. (C) 2019, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

In this paper, we propose an innovative hybrid integrated approach between simulated annealing (SA) and control theoretic based distributed arrival time control (DATC), named SA-DATC. The two approaches have been combined to achieve the objective of Justin-Time (JIT) scheduling in an open-shop system. This approach makes the two algorithms work together aimed with combining the main advantages of each of them, to obtain a reactive control strategy. The SA algorithm plays a role as the machine-route explorer, while the DATC algorithm is inserted into the SA loop, allowing searching for the best arrival times of jobs to satisfy the JIT objective wanted. The performance of this new hybrid SA-DATC approach is evaluated with quadratic linear program solutions to test its relative performance in a static environment. Computational results show that the algorithm performs well on the most of the test problems generated randomly in this paper with an interesting computational time, proving that our approach is favorable for an open-shop scheduling problem.

The problem of state feedback control for a class of semilinear partial integro-differential equations is addressed within the framework of dissipativity theory. Adapting ideas from absolute stability theory the system is viewed as interconnection of a linear dynamic and a nonlinear static subsystem. Using this interpretation sufficient conditions for the stabilization of the zero solution by means of a backstepping state feedback controller are derived considering a class of nonlinearities satisfying a quadratic dissipativity condition. The approach is illustrated using numerical simulations for a representative case example.

The process industries becomes increasingly digital, integrated, and connected. Chemical production sites consist of many subsystems which are tightly interconnected and physically coupled by flows of material and energy. Coordination of these systems of systems is essential but challenging. Various barriers prohibit a joint optimization of the overall system. One of the barriers results from the complex management structure of these systems which does not allow for the exchange of all necessary information for a holistic formulation and solution of the optimization problem. Market-like coordination is one way to address this challenge by establishing a micro-market for shared resources between the competing subsystems. However, market-like coordination is known for its slow rate of convergence. Using quadratic approximation for an improved rate of convergence was proposed in Wenzel et al. (2016). In this contribution, we present an extension of this algorithm and apply it to a number of randomly generated medium- to large-scale test problems. The performance of the algorithm is compared to the performance of subgradient-based coordination and to the performance of the Alternating Direction Method of Multipliers (ADMM).

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.

Problems of the stochastic system identification are analyzed, which are concerned with application of non-linear measures of dependence of random values and processes. Approaches are considered using a consistent in the A.N. Kolmogorov sense measure of dependence based on quadratic Tsallis mutual information. A constructive procedure of deriving a linear input/output model is proposed, which is a statistical equivalent of a non-linear multi-input/multi-output dynamic stochastic system driven by a Gaussian white-noise process. A keystone of such a procedure is the statistical linearization criterion that is the condition of the component-wise coincidence of quadratic Tsallis mutual information of the system input and output processes, from one hand side, and the input and output processes of the linearized model, from another hand side. This approach enables one to obtain explicit analytical expressions determining coefficients of the weight matrix of the linearized model. Meanwhile, the technique proposed is distribution free and does not require applying restricting assumptions on an explicit analytical form of the joint probability distribution of the system input and output processes, which may degenerate the substantial problem statement at all.

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.