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A robust, adaptive Model Predictive Control (MPC) approach for asymptotically stable, constrained linear time-varying (LTV) systems with multiple inputs and outputs is proposed. The approach consists of two-steps, carried out on-line with a receding horizon strategy. In the first one, a real-time Set Membership identification algorithm exploits the measured input–output data and the available prior knowledge to build and refine a set of admissible models of the plant (Feasible Parameter Set, FPS). This set is guaranteed to contain also the true system dynamics under the considered working assumptions. In the second step, a robust finite-horizon optimal control problem is formulated and solved. The variation of system dynamics is taken into account by inflating the FPS over the prediction horizon, according to worst-case bounds, assumed a priori, on the parameters' rate of change. The resulting optimal control sequence guarantees that the outputs of all possible plants inside the FPS satisfy the operational constraints, also considering all possible future parameter changes. The main theoretical properties of the proposed approach are demonstrated and the method is showcased in numerical simulations, highlighting the fundamental improvement over previous approaches not designed for LTV systems. [ABSTRACT FROM AUTHOR]

Unmanned Aerial Vehicles are versatile tools for inspection tasks, for example of the built environment. Due to their limited flight time, it is sensible to employ multiple units to decrease the total mission time and avoid additional trips to change batteries. The use of more units makes the mission planning more complex; moreover, a fault tolerant plan that is resilient at least to a single failure is desirable. To solve these problems, a new algorithm is proposed, which, via hierarchical decomposition and numerical optimization, effectively deals with: (1) the efficient generation of a suitable path for each drone, and (2) guaranteeing mission robustness against a single fault. The automated generation and clustering of points of interest to be visited is addressed, too, as part of the whole procedure. Using accurate models of two real buildings, it is shown that the approach delivers close-to-optimal solutions with small computational time, thus being compatible with real-world operation. [ABSTRACT FROM AUTHOR]

A data-driven method to design reference tracking controllers for nonlinear systems is presented. The technique does not derive explicitly a model of the system, rather it delivers directly a time-varying state-feedback controller by combining an on-line and an off-line scheme. Like in other on-line algorithms, the measurements collected in closed-loop operation are exploited to modify the controller in order to improve the tracking performance over time. At the same time, a predictable closed-loop behavior is guaranteed by making use of a batch of available data, which is a feature of off-line algorithms. The feedback controller is parameterized with kernel functions and the design approach exploits results in set membership identification and learning by projections. Under the assumptions of Lipschitz continuity and stabilizability of the system’s dynamics, it is shown that if the initial batch of data is informative enough, then the resulting closed-loop system is guaranteed to be finite gain stable. In addition to the main theoretical properties of the approach, the design algorithm is demonstrated experimentally on a water tank system. [ABSTRACT FROM AUTHOR]

An approach to derive low-complexity models describing thermal radiation, employed to simulate the behavior of electric arcs in switchgear systems, is presented. The idea is to approximate the (high dimensional) full-order equations, describing the propagation of the radiated heat intensity in space, with a model of much lower dimension, whose parameters are identified by means of nonlinear system identification techniques. The proposed order reduction approach is able to systematically compute the partitioning of the electromagnetic spectrum in frequency bands, and the related absorption coefficients, that yield the best matching with respect to the finely resolved absorption spectrum of the considered gaseous medium. In addition to the order reduction approach and the related computational aspects, an analysis by means of Laplace transform is presented, providing a justification to the use of very low orders in the reduction procedure as compared with the full-order model. Finally, comparisons between the full-order model and the reduced-order one are presented. [ABSTRACT FROM AUTHOR]

The problem of experiment design for constrained linear systems with multiple inputs is addressed. A parametric model of the system is considered. The presented theoretical results provide a guideline on how to design experiments that minimize the worst-case identification error, as measured by the radius of information of the set of feasible model parameters, calculated in any norm. In addition, it is shown that an alternative, simpler approach can be employed when input constraints are symmetric and the worst-case identification error is minimized in either 1 - or ∞ -norm. For such cases, on the basis of the derived results, a computationally tractable algorithm for the experiment design is proposed. The presented results are valid for a general model representation, which admits the commonly used finite impulse response model as a special case. The features of the presented method are illustrated in a numerical example. [ABSTRACT FROM AUTHOR]

Many practical applications in control require that constraints on the inputs and states of the system are respected, while some performance criterion is optimized. In the presence of model uncertainties or disturbances, it is often sufficient to satisfy the state constraints for at least a prescribed share of the time, such as in building climate control or load mitigation for wind turbines. For such systems, this paper presents a new method of Scenario-Based Model Predictive Control (SCMPC). The basic idea is to optimize the control inputs over a finite horizon, subject to robust constraint satisfaction under a finite number of random scenarios of the uncertainty and/or disturbances. Previous SCMPC approaches have suffered from a substantial gap between the rate of constraint violations specified in the optimal control problem and that actually observed in closed-loop operation of the controlled system. This paper identifies the two theoretical explanations for this gap. First, accounting for the special structure of the optimal control problem leads to a substantial reduction of the problem dimension. Second, the probabilistic constraints have to be interpreted as average-in-time, rather than pointwise-in-time. Based on these insights, a novel SCMPC method can be devised for general linear systems with additive and multiplicative disturbances, for which the number of scenarios is significantly reduced. The presented method retains the essential advantages of the general SCMPC approach, namely a low computational complexity and the ability to handle arbitrary probability distributions. Moreover, the computational complexity can be adjusted by a sample-and-remove strategy. [ABSTRACT FROM AUTHOR]

An adaptive control algorithm for open-loop stable, constrained, linear, multiple input multiple output systems is presented. The proposed approach can deal with both input and output constraints, as well as measurement noise and output disturbances. The adaptive controller consists of an iterative set membership identification algorithm, that provides a set of candidate plant models at each time step, and a model predictive controller, that enforces input and output constraints for all the plants inside the model set. The algorithm relies only on the solution of standard convex optimization problems that are guaranteed to be recursively feasible. The experimental results obtained by applying the proposed controller to a quad-tank testbed are presented. [ABSTRACT FROM AUTHOR]

The scenario-based optimization approach ("scenario approach" ) provides an intuitive way of approximating the solution to chance-constrained optimization programs, based on finding the optimal solution under a finite number of sampled outcomes of the uncertainty ("scenarios"). A key merit of this approach is that it neither requires explicit knowledge of the uncertainty set, as in robust optimization, nor of its probability distribution, as in stochastic optimization. The scenario approach is also computationally efficient because it only requires the solution to a convex optimization program, even if the original chance-constrained problem is nonconvex. Recent research has obtained a rigorous foundation for the scenario approach, by establishing a direct link between the number of scenarios and bounds on the constraint violation probability. These bounds are tight in the general case of an uncertain optimization problem with a single chance constraint. This paper shows that the bounds can be improved in situations where the chance constraints have a limited "support rank," meaning that they leave a linear subspace unconstrained. Moreover, it shows that also a combination of multiple chance constraints, each with individual probability level, is admissible. As a consequence of these results, the number of scenarios can be reduced from that prescribed by the existing theory for problems with the indicated structural property. This leads to an improvement in the objective value and a reduction in the computational complexity of the scenario approach. The proposed extensions have many practical applications, in particular, high-dimensional problems such as multistage uncertain decision problems or design problems of large-scale systems. [ABSTRACT FROM AUTHOR]

Abstract: A terminal state equality constraint for Model Predictive Control (MPC) laws is investigated, where the terminal state/input pair is not fixed a priori but it is a free variable in the optimization. The approach, named “generalized” terminal state constraint, can be used for both tracking MPC (i.e. when the objective is to track a given steady state) and economic MPC (i.e. when the objective is to minimize a cost function which does not necessarily attains its minimum at a steady state). It is shown that the proposed technique provides, in general, a larger feasibility set with respect to the existing approaches, given the same prediction horizon. Moreover, a new receding horizon strategy is introduced, exploiting the generalized terminal state constraint. Under mild assumptions, the new strategy is guaranteed to converge in finite time, with arbitrarily good accuracy, to an MPC law with an optimally-chosen terminal state constraint, while still enjoying a larger feasibility set. The features of the new technique are illustrated by an inverted pendulum example in both the tracking and the economic contexts. [Copyright &y& Elsevier]

Abstract: A stochastic receding-horizon control approach for constrained Linear Parameter Varying discrete-time systems is proposed in this paper. It is assumed that the time-varying parameters have stochastic nature and that the system’s matrices are bounded but otherwise arbitrary nonlinear functions of these parameters. No specific assumption on the statistics of the parameters is required. By using a randomization approach, a scenario-based finite-horizon optimal control problem is formulated, where only a finite number of sampled predicted parameter trajectories (‘scenarios’) are considered. This problem is convex and its solution is a priori guaranteed to be probabilistically robust, up to a user-defined probability level . The level is linked to by an analytic relationship, which establishes a tradeoff between computational complexity and robustness of the solution. Then, a receding horizon strategy is presented, involving the iterated solution of a scenario-based finite-horizon control problem at each time step. Our key result is to show that the state trajectories of the controlled system reach a terminal positively invariant set in finite time, either deterministically, or with probability no smaller than . The features of the approach are illustrated by a numerical example. [Copyright &y& Elsevier]

Abstract: This paper presents a novel approach to design estimators for nonlinear systems. The approach is based on a combination of linear Moving Horizon Estimation (MHE) and Direct Virtual Sensor (DVS) techniques, and allows the design of estimators with guaranteed stability, which can account for convex constraints on the variables to be estimated. It is also shown that the designed estimators are optimal, in the sense that they give minimal worst-case estimation error, on the basis of the available finite number of noise-corrupted data, with respect to an ideal MHE filter (obtained by assuming exact knowledge of the system dynamics and of the global solution of the related nonlinear program). The approach is tested on a nonlinear mass–spring–damper system. [Copyright &y& Elsevier]

SUMMARY This paper presents novel results related to an innovative airborne wind energy technology, named Kitenergy, for the conversion of high-altitude wind energy into electricity. The research activities carried out in the last five years, including theoretical analyses, numerical simulations, and experimental tests, indicate that Kitenergy could bring forth a revolution in wind energy generation, providing renewable energy in large quantities at a lower cost than fossil energy. This work investigates three important theoretical aspects: the evaluation of the performance achieved by the employed control law, the optimization of the generator operating cycle, and the possibility to generate continuously a constant and maximal power output. These issues are tackled through the combined use of modeling, control, and optimization methods that result to be key technologies for a significant breakthrough in renewable energy generation. Copyright © 2011 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Polynomial chaos expansions represent a powerful tool to simulate stochastic models of dynamical systems. Yet, deriving the expansion's coefficients for complex systems might require a significant and nontrivial manipulation of the model, or the computation of large numbers of simulation runs, rendering the approach too time consuming and impracticable for applications with more than a handful of random variables. We introduce a computationally tractable technique for computing the coefficients of polynomial chaos expansions. The approach exploits a regularization technique with a particular choice of weighting matrices, which allows to take into account the specific features of polynomial chaos expansions. The method, completely based on convex optimization, can be applied to problems with a large number of random variables and uses a modest number of Monte Carlo simulations, while avoiding model manipulations. Additional information on the stochastic process, when available, can be also incorporated in the approach by means of convex constraints. We show the effectiveness of the proposed technique in three applications in diverse fields, including the analysis of a nonlinear electric circuit, a chaotic model of organizational behavior, and finally a chemical oscillator. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, the use of Set Membership (SM) methods is investigated, in order to derive off-line an approximation of a discontinuous nonlinear model predictive control (NMPC) law. The approximating function can then be evaluated on-line, instead of solving the nonlinear program embedded in the NMPC scheme. This way, a significant decrease of computational times may be obtained, thus allowing the application of NMPC also to systems with “fast” dynamics. It is shown that the knowledge of the discontinuities is needed to achieve an approximated controller with arbitrarily small approximation error. By exploiting such a knowledge, SM techniques already developed for the case of continuous NMPC laws are generalized in order to approximate discontinuous ones. The stability of the origin of the closed loop system with the approximated control law is analyzed, and a numerical example is employed to illustrate the features of the proposed approach. [Copyright &y& Elsevier]

Abstract: In this paper, Nonlinear Model Predictive Control is used to improve vehicle stability and handling by means of a rear active differential. In order to allow on-line control computations within the required sampling time, a Set Membership approximation of the designed controller is employed. The real applicability and effectiveness of such a technique, as well as the improvement over an existing control approach based on Internal Model Control, is shown through the implementation on a commercial embedded device with limited computational capacity and the testing via software-in-the-loop simulations of demanding maneuvers, using an accurate nonlinear vehicle model. [Copyright &y& Elsevier]

The paper presents the innovative technology of high altitude wind power generation, indicated as Kit energy, which exploits the automatic flight of tethered airfoils (e.g., power kites) to extract energy from wind blowing between 200 and 800 m above the ground. The key points of this technology are described and the design of large scale plants is investigated, in order to show that it has the potential to overcome the limits of the actual wind turbines and to provide large quantities of renewable energy, with competitive cost with respect to fossil sources. Such claims are supported by the results obtained so far in the Kit energy project, undergoing at Politecnico di Torino, Italy, including numerical simulations, prototype experiments, and wind data analyses. [ABSTRACT FROM AUTHOR]

The problem of vehicle yaw control is addressed in this paper using an active differential and yaw rate feedback. A reference generator, designed to improve vehicle handling, provides the desired yaw rate value to be achieved by the closed loop controller. The latter is designed using the second-order sliding mode (SOSM) methodology to guarantee robust stability in front of disturbances and model uncertainties, which are typical of the automotive context. A feedforward control contribution is also employed to enhance the transient system response. The control derivative is constructed as a discontinuous signal, attaining an SOSM on a suitably selected sliding manifold. Thus, the actual control input results in being continuous, as it is needed in the considered context. Simulations performed using a realistic non-linear model of the considered vehicle show the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

The control design of an airborne wind energy system with rigid aircraft, vertical take-off and landing, and pumping operation is described. A hierarchical control structure is implemented, in order to address all operational phases: take-off, transition to power generation, pumping energy generation cycles, transition to hovering, and landing. Control design at all hierarchical levels is described. The design approach is conceived and developed with real-world applicability as main driver. Aircraft design considerations in light of system maneuverability are presented, too, as well as three possible alternative strategies for the retraction phase of the pumping cycle. The automatic control approach is assessed in simulation with a realistic model of the overall system, and the results yield a comparison among the three retraction strategies, clearly indicating the most efficient one. The presented results allow one to simulate the dynamical behavior of an AWE system in all operational phases, enabling further studies on all-round system automation, towards fully autonomous and reliable operation. [ABSTRACT FROM AUTHOR]

A nonlinear model predictive control approach is studied, for problems where a fixed terminal instant and corresponding terminal set to be reached are imposed. The new technique features a shrinking horizon, rather than the most common receding one, and an input parametrization strategy to reduce computational burden. The property of transferability of the parametrization strategy is introduced. Under this property, theoretical convergence guarantees in nominal conditions are obtained by construction. Two relaxed techniques are then proposed to retain recursive feasibility in presence of bounded additive input disturbance. A bound on the constraint violation achieved by these relaxed techniques as a function of the uncertainty bound is derived, too. The developed strategy is applied to the problem of energy-efficient operation of trains, in either a fully autonomous mode (with continuous input values) or a driver assistance mode (with discrete input values, resulting in a nonlinear integer program if no parametrization is used). Realistic simulation results in this context illustrate the effectiveness of the approach. [ABSTRACT FROM AUTHOR]

A comprehensive approach addressing identification and control for learning-based Model Predictive Control (MPC) for linear systems is presented. The design technique yields a data-driven MPC law, based on a dataset collected from the working plant. The method is indirect, i.e. it relies on a model learning phase and a model-based control design one, devised in an integrated manner. In the model learning phase, a twofold outcome is achieved: first, different optimal p -steps ahead prediction models are obtained, to be used in the MPC cost function; secondly, a perturbed state-space model is derived, to be used for robust constraint satisfaction. Resorting to Set Membership techniques, a characterization of the bounded model uncertainties is obtained, which is a key feature for a successful application of the robust control algorithm. In the control design phase, a robust MPC law is proposed, able to track piece-wise constant reference signals, with guaranteed recursive feasibility and convergence properties. The controller embeds multistep predictors in the cost function, it ensures robust constraints satisfaction thanks to the learnt uncertainty model, and it can deal with possibly unfeasible reference values. The proposed approach is finally tested in a numerical example. [ABSTRACT FROM AUTHOR]

Airborne wind energy systems convert wind energy into electricity using tethered flying devices, typically flexible kites or aircraft. Replacing the tower and foundation of conventional wind turbines can substantially reduce the material use and, consequently, the cost of energy, while providing access to wind at higher altitudes. Because the flight operation of tethered devices can be adjusted to a varying wind resource, the energy availability increases in comparison to conventional wind turbines. Ultimately, this represents a rich topic for the study of real-time optimal control strategies that must function robustly in a spatiotemporally varying environment. With all of the opportunities that airborne wind energy systems bring, however, there are also a host of challenges, particularly those relating to robustness in extreme operating conditions and launching/landing the system (especially in the absence of wind). Thus, airborne wind energy systems can be viewed as a control system designer's paradise or nightmare, depending on one's perspective. This survey article explores insights from the development and experimental deployment of control systems for airborne wind energy platforms over approximately the past two decades, highlighting both the optimal control approaches that have been used to extract the maximal amount of power from tethered systems and the robust modal control approaches that have been used to achieve reliable launch, landing, and extreme wind operation. This survey will detail several of the many prototypes that have been deployed over the last decade and will discuss future directions of airborne wind energy technology as well as its nascent adoption in other domains, such as ocean energy. [Display omitted] • Review of two decades of modeling, control, and prototyping of airborne wind energy systems • Review of foundational formulae for modeling airborne wind energy systems • Summary of popular flight control and launch/land control strategies for airborne wind systems • Description of lab-scale and large-scale airborne wind energy system prototypes • Introduction to spinoff technology in the marine hydrokinetic energy field [ABSTRACT FROM AUTHOR]

The adoption of Unmanned Aerial Vehicles for energy efficiency assessment is a promising technique. Although there are some interesting ideas to automate the process, most of the operations are still done manually. An integrated methodology is presented to identify maintenance needs from both RGB and infrared images collected with a commercial drone. The real building selected as case study is reconstructed in a 3D environment through Structure-from-Motion Photogrammetry, while the infrared information is integrated and properly scaled on it to obtain a 3D point cloud model that provides, for each point: (i) information about its position in the space, and (ii) external surface temperature measured during data gathering phase. The point cloud is then segmented into the different sides of the envelope to identify each part of the façade and compared with a model that reconstructs the expected conditions of the building. This procedure enables the development of an automated pipeline to identify defects and failures in a building envelope and to suggest corrective actions. [ABSTRACT FROM AUTHOR]

This paper presents a study on the design of linear model predictive control (MPC) for wind turbines, with a focus on the controller's tuning tradeoffs. A continuously linearized MPC approach is described and applied to control a 3-bladed, horizontal axis, variable speed wind turbine. The tuning involves a multiobjective cost function so that the performance can be optimized with respect to five defined measures: power variation, pitch usage, tower displacement, drivetrain twist and frequency of violating the nominal power limit. A tuning approach based on the computation of sensitivity tables is proposed and tested via numerical simulations using a nonlinear turbine model. The paper further compares the performance of the MPC controller with that of a conventional one. [ABSTRACT FROM AUTHOR]

This note extends a recently proposed algorithm for model identification and robust model predictive control (MPC) of asymptotically stable, linear time-invariant systems subject to process and measurement disturbances. Independent output predictors for different horizon values are estimated with Set Membership methods. It is shown that the corresponding prediction error bounds are the least conservative in the considered model class. Then, a new multi-rate robust MPC algorithm is developed, employing said multi-step predictors to robustly enforce constraints and stability against disturbances and model uncertainty, and to reduce conservativeness. A simulation example illustrates the effectiveness of the approach. [ABSTRACT FROM AUTHOR]

Many science and engineering applications feature non-convex optimization problems where the objective function cannot be handled analytically, i.e. it is a black box. Examples include design optimization via experiments, or via costly finite elements simulations. To solve these problems, global optimization routines are used. These iterative techniques must trade-off exploitation close to the current best point with exploration of unseen regions of the search space. In this respect, a new global optimization strategy based on a Set Membership (SM) framework is proposed. Assuming Lipschitz continuity of the cost function, the approach employs SM concepts to decide whether to switch from an exploitation mode to an exploration one, and vice-versa. The resulting algorithm, named SMGO (Set Membership Global Optimization) is presented. Theoretical properties regarding convergence and computational complexity are derived, and implementation aspects are discussed. Finally, the SMGO performance is evaluated on a set of benchmark non-convex problems and compared with those of other global optimization approaches. [ABSTRACT FROM AUTHOR]

Despite the enormous benefit that has accrued to society from control technology and the continued vitality of control science as a research field, there is broad consensus that the practitioners of control and the academic research community are insufficiently engaged with each other. We explore this concern with reference to the oft-cited theory/practice gap but also from an industry perspective. The core of this article is comprised of ten "messages," intended primarily for researchers interested in the practical impact of their work, that we hope shed insight on the industry mindset. Results from surveys and other data are cited to underpin the points. Some educational synergies between industry and academia are also noted. To highlight the continuing relevance of control science to industry, several recent examples of successful, deployed advanced control solutions are presented. The authors of this article are members of the IFAC Industry Committee, formally established in 2017 with objectives that include (per the updated IFAC Constitution) "increasing industry participation in and impact from IFAC activities." [ABSTRACT FROM AUTHOR]

This paper investigates the simultaneous design of active attitude control and passive attitude compensation mechanism for a spacecraft to satisfy practically-motivated mission objectives and constraints. The expressions of these fitness-related metrics with respect to the design variables are not analytically available, due to the nontrivial interactions between the spacecraft components and the interactions with the environment. Thus, such functions can only be approximately learned from data derived from simulations. We approach this difficult design problem using a black-box optimization (BBO)-based approach, which combines learning and optimizing the objective and constraint functions by design of experiments. The proposed BBO-based approach is assessed in the context of a 3U CubeSat system design with both a passive magnetic attitude compensation and an active reaction wheel-based control, tested on a simulator considering orbital and environmental dynamics. Simulation results and statistical tests compared to other design methods show the capability of the BBO-based approach to provide a design with the best tracking performance while at the same time satisfying ground station communication requirements and power budget. [ABSTRACT FROM AUTHOR]

Unmanned Aerial Vehicles are employed for vision-based modal analysis of civil infrastructure, as they overcome range limitations of fixed cameras and measure the oscillations of a structure up close. Nevertheless, their potential is not fully exploited: they are often piloted manually and one at a time, though one drone is unable to capture high resolution displacement of a whole structure. An approach is explored here, employing multiple drones simultaneously to estimate natural frequencies and modal shapes of a structure, by synchronizing their measurement. The ability of the method to detect modal parameter variations is assessed, such that it can identify anomalies in the structure. Procedures are applied to a test structure, yielding maximum natural frequency estimation errors of 0.2% with respect to accelerometers. The results suggest the accuracy of the approach is high enough to warrant further development and support autonomous, multi-drone applications to the inspection of the built environment. • Multiple drone-mounted cameras can capture the oscillation of a structure at once. • Cross-correlation synchronizes footage from several airborne sources. • High accuracy in displacement tracking and mode estimation does not require markers. [ABSTRACT FROM AUTHOR]

This paper represents an expert view from Europe of future emerging technologies within the wind energy sector considering their potential, challenges, applications and technology readiness and how they might evolve in the coming years. These technologies were identified as originating primarily from the academic sector, some start-up companies and a few larger industrial entities. The following areas were considered: airborne wind energy, offshore floating concepts, smart rotors, wind-induced energy harvesting devices, blade tip-mounted rotors, unconventional power transmission systems, multi-rotor turbines, alternative support structures, modular high voltage direct current generators, innovative blade manufacturing techniques, diffuser-augmented turbines and small turbine technologies. The future role of advanced multiscale modelling and data availability is also considered. This expert review has highlighted that more research will be required to realise many of these emerging technologies. However, there is a need to identify synergies between fundamental and industrial research by correctly targeting public and private funding in these emerging technology areas as industrial development may outpace more fundamental research faster than anticipated. • A set of emerging technologies in the wind power sector was identified and studied. • For each technology, an analysis of the main advantages and challenges was made. • Potential for scaling each technology was identified. • Technology Readiness Levels and their development trends were assessed. [ABSTRACT FROM AUTHOR]