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Abstract In this paper, a novel fault detection method is developed based on robust characteristic dimensionality reduction (RCDR). The time-constrained sparse representation (TCSR) method is firstly introduced by considering the space and time characteristics of industrial process monitoring data simultaneously. It can remove space-related outliers, time-related outliers and noises by solving an optimization problem. Then, a new RCDR method is proposed, which fully utilizes the constructed robust adjacency graph and considers the data characteristics. Its scatter matrices are specially designed by consideration of the data characteristics of fault detection. The within-class scatter matrix only characterizes normal data set with a classic covariance matrix, while the inter-class scatter matrix characterizes the separability between normal data and fault data through a pre-defined scatter matrix. It is worth mentioning that our method does not make Gaussian assumptions about the distribution of the fault data, and the number of projection directions is not limited as well. The TCSR is also embedded into our proposed dimensionality reduction method, enabling it to handle the fault detection problem under strong disturbances. Simulations on Tennessee Eastman process (TEP) and a case study of electric multiple unit (EMU) braking system of high-speed trains fully demonstrate the effectiveness and applicability of our proposed fault detection method. Highlights • New time-constrained sparse representation method is put forward. • Novel robust characteristic dimensionality reduction method is proposed. • Online fault detection algorithm is developed and fault detectability is analyzed. • Experimental studies on TEP and EMU braking system validate the proposed methods. [ABSTRACT FROM AUTHOR]

Abstract A new model-reference data-driven approach is presented which uses the frequency response data of a system in order to avoid the problem of unmodeled dynamics associated with low-order parametric models. It is shown that a convex optimization problem can be formulated (in either the H ∞ , H 2 or H 1 sense) to shape the closed-loop sensitivity functions while guaranteeing the closed-loop stability. The effectiveness of the method is illustrated by considering several case studies where the proposed design scheme is applied in both simulation and to a power converter control system for a specific accelerator requirement at CERN. [ABSTRACT FROM AUTHOR]

Iterative learning control (ILC) is a method for improving the performance of stable, repetitive systems. Standard ILC is constructed in the temporal domain, with performance improvements achieved through iterative updates to the control signal. Recent ILC research focuses on reformulating temporal ILC into the spatial domain, where 2D convolution accounts for spatial closeness. This work expands spatial ILC to include optimization of multiple performance metrics. Performance objectives are classified into primary, complementary, competing, and domain specific objectives. New robustness and convergence criteria are provided. Simulation results validate flexibility of the spatial framework on a high-fidelity additive manufacturing system model. [ABSTRACT FROM AUTHOR]

Since their first appearance in the 1970's, industrial robotic manipulators have considerably extended their application fields, allowing end-users to adopt this technology in previously unexplored scenarios. Correspondingly, the way robot motion can be specified has become more and more complex, requiring new capabilities to the robot, such as reactivity and adaptability. For an even enhanced and widespread use of industrial manipulators, including the newly introduced collaborative robots, it is necessary to simplify robot programming, thus allowing this activity to be handled by non-expert users. Next generation robot controllers should intelligently and autonomously interpret production constraints, specified by an application expert, and transform them into motion commands only at a lower and real-time level, where updated sensor information or other kind of events can be handled consistently with the higher level specifications. The availability of several execution strategies could be then effectively exploited in order to further enhance the flexibility of the resulting robot motion, especially during collaboration with humans. This paper presents a novel methodology for motion specification and robust reactive execution. Traditional trajectory generation techniques and optimisation-based control strategies are merged into a unified framework for simultaneous motion planning and control. An experimental case study demonstrates the effectiveness and the robustness of this approach, as applied to an image-guided grasping task. [ABSTRACT FROM AUTHOR]

This paper presents a two-layer control scheme to address the difficulties in the modeling and control of solution purification process. Two concepts are extracted from the characteristics of solution purification process: additive utilization efficiency (AUE) and impurity removal ratio (IRR). The idea of gradient optimization of solution purification process, which transforms the economical optimization problem of solution purification process into finding an optimal decline gradient of the impurity ion concentration along the reactors, is proposed. A robust adaptive controller is designed to track the optimized impurity ion concentration in the presence of process uncertainties, disturbance and saturation. Oxidation reduction potential (ORP), which is a significant parameter of solution purification process, is also used in the scheme. The ability of the gradient optimization scheme is illustrated with a simulated case study of a cobalt removal process. [ABSTRACT FROM AUTHOR]

A linear parameter-varying (LPV) model-based synthesis, tuning and assessment methodology is developed and applied for the design of a robust fault detection and diagnosis (FDD) system for several types of flight actuator faults such as jamming, runaway, oscillatory failure, or loss of efficiency. The robust fault detection is achieved by using a synthesis approach based on an accurate approximation of the nonlinear actuator-control surface dynamics via an LPV model and an optimal tuning of the free parameters of the FDD system using multi-objective optimization techniques. Real-time signal processing is employed for identification of different fault types. The assessment of the FDD system robustness has been performed using both standard Monte-Carlo methods as well as advanced worst-case search based optimization-driven robustness analysis. A supplementary industrial validation performed on the AIRBUS actuator test bench for the monitoring of jamming, confirmed the satisfactory performance of the FDD system in a true industrial setting. [ABSTRACT FROM AUTHOR]

Abstract: An industrial case study involving a large-scale hydraulic network is examined. The hydraulic network underlies a district heating system, with an arbitrary number of end-users. The problem of output regulation is addressed along with a optimization criterion for the control. The fact that the system is overactuated is exploited for minimizing the steady state electrical power consumption of the pumps in the system, while output regulation is maintained. The proposed control actions are decentralized in order to make changes in the structure of the hydraulic network easy to implement. [Copyright &y& Elsevier]

Abstract: Based on robust feedback incorporated with iterative learning control scheme, this paper proposes a 2D controller design for a kind of batch process with uncertainties and interval time-varying delay. The batch process is first transformed into a two-dimensional Fornasini—Marchsini (2D-FM) model with a delay varying in a range. Then the design of 2D controller scheme is cast as a robust H ∞ control problem for uncertain 2D systems. Based on the 2D control system theory, a design framework of 2D controller is formulated, which consists of two control actions: a robust feedback control for ensuring the performances over time and a P-type iterative learning control (P-type ILC) for improving the tracking performance from cycle to cycle. The proposed control law can guarantee closed-loop convergence along both the time and the cycle directions to satisfy H ∞ performance. Conditions for the existence of the proposed 2D control design scheme are given in terms of linear matrix inequalities. The minimum H ∞ norm bound is obtained by solving linear objective optimization problems. The generality of the proposed design method is shown by the results converted to constant delay case. Application to injection velocity control demonstrates the effectiveness and advantages of the proposed approach. [Copyright &y& Elsevier]

Abstract: Benchmarking is comparing the output of different systems for a given set of input data in order to improve the system’s performance. Faced with the lack of realistic and operational benchmarks that can be used for testing optimization methods and control systems in flexible systems, this paper proposes a benchmark system based on a real production cell. A three-step method is presented: data preparation, experimentation, and reporting. This benchmark allows the evaluation of static optimization performances using traditional operation research tools and the evaluation of control system's robustness faced with unexpected events. [Copyright &y& Elsevier]

Abstract: A method for black-box identification of a Wiener–Hammerstein system is described and applied to a set of Benchmark data originally presented at the 15th IFAC Symposium on System Identification. An incremental nonlinear optimisation procedure is used, which is able to avoid local minima, thus enabling the solution to converge to the global minimum. The use of a dual-polynomial to describe the static nonlinearity allows the number of parameters needed to be significantly reduced compared with the case if a single polynomial is utilised; this also improves robustness against extrapolation errors. The overall approach requires a relatively small number of parameters. [Copyright &y& Elsevier]

Abstract: Modern process plants are highly integrated and as a result, decentralized PID control loops are often strongly interactive. The iterative SISO tuning approach currently used in industry is not only time consuming, but does also not achieve optimal performance of the inherently multivariable control system. This paper describes a method and a software tool that allows control engineers/technicians to calculate optimal PID controller settings for multi-loop process systems. It requires the identification of a full dynamic model of the multivariable system, and uses constrained nonlinear optimization techniques to find the controller parameters. The solution is tailored to the specific control system and PID algorithm to be used. The methodology has been successfully applied in many industrial advanced control projects. The tuning results that have been achieved for interacting PID control loops in the stabilizing section of an industrial Gasoline Treatment Unit as well as a Diesel Desulfurization plant are presented. [Copyright &y& Elsevier]

Abstract: This paper describes the main problems associated to the management of hydrogen networks in petrol refineries and presents an approach to deal with them with the aim of operating the installation in the most profitable way. In particular, the problems of data reconciliation, economic optimization and interaction with the underlying basic control system are reviewed. The paper provides also a proposal for the implementation of the system and illustrates the approach with results obtained using real data from an industrial site. [Copyright &y& Elsevier]

Abstract: In this paper the flood problem of the river Demer, a river located in Belgium, is discussed. First a simplified model of the Demer basin is derived based on the conceptual reservoir modeling concept. This model was calibrated to simulations results with a more detailed full hydrodynamic model. Afterwards, the focus is shifted to a nonlinear model predictive controller (NMPC) which is based on a new semi-condensed optimization procedure combined with a line search approach. Finally, simulations are performed based on historical data in which the NMPC is compared with the current control strategy used by the local water administration. Uncertainties are added to the rainfall predictions in order to assess the robustness of the NMPC. [Copyright &y& Elsevier]

Abstract: Based on a recent description of deadzone nonlinearities via local sector conditions, a new LMI characterization is proposed in this paper to compute static and full- or fixed-order anti-windup compensators for possibly open-loop unstable plants. The originality of this approach rests on the use of a specific performance criterion which is inspired by the framework. A significant part of the paper is then devoted to a challenging control problem which involves both magnitude and rate limitations. As an important point in this illustrative part, implementation and robustness issues of the anti-windup device are thoroughly discussed. [Copyright &y& Elsevier]

Abstract: This paper studies the optimization of malicious software removal or patch deployment processes across multiple networks. The well-known classical epidemic model is adapted to model malware propagation in this multi-network framework. The trade-off between the infection spread and the patching costs is captured in a cost function, leading to an optimal control problem. In the single network case the optimal feedback controller is found by solving an associated Hamilton–Jacobi–Bellman equation. This control law is numerically compared to the proportional response strategy typically assumed by the epidemic model. In the higher dimensional multiple-networks case, the system is linearized to derive feedback controllers using pole-placement, linear quadratic regulator (LQR) optimal control, and optimal control, where the measurement errors in the number of infected clients are explicitly modeled. The resulting patching strategies are analyzed numerically and their results are compared. [Copyright &y& Elsevier]

The design of robust controllers for processes with operating-point-dependent behaviour is a minimax optimisation problem which can, in general, e.g. for given controller structures or for practical relevant performance functionals, only be solved by numerical minimisation. The convergence of the minimisation algorithm is only ensured if the initial point needed to start the optimisation is in the neighbourhood of the local minimum. This makes the design difficult for the engineer because there is no systematic way to find an initial point. The paper presents a contribution to overcome this difficulty by using continuation methods. [Copyright &y& Elsevier]

This paper presents a control algorithm for the parallel interconnection of heterogeneous power converters. A single resistive load is assumed to be fed by an arbitrary number of buck converters via a common DC bus. The approach is based on control allocation theory and a constrained quadratic optimization algorithm. The strategy achieves a fast voltage response with an optimal current distribution among the converters, while taking into account the current limits, the dynamic response, and the efficiency of the individual converters. An interesting by-product of the approach is the ability to put converters in and out of service through trivial adjustments of the code. The benefits of the approach are assessed through simulations and an experimental evaluation. • For the parallel interconnection of buck converters, the proposed method ensures a fast response while maximizing efficiency by a free distribution of currents, including transients. • The proposed method guarantees stability under certain conditions with consideration to load uncertainty, converter dynamics, and input and current limits. • The proposed method facilitates the predictable connection and disconnection of converters in real-time to accommodate large load variations or for maintenance. [ABSTRACT FROM AUTHOR]