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This paper proposes a model predictive control (MPC) scheme based on linear parameter variation to enhance the damping control of speed-dependent active suspensions. The controller is developed by introducing a speed-dependent term, specifically front- and rear-wheel time delays, to the half-car model using the Padé approximation. Subsequently, the model is augmented with time-varying parameter dependence. An adaptive Kalman filter based on variance matching is employed to estimate system states affected by imprecise sensor measurement noise. Finally, a set of explicit control laws incorporating road preview information and available vehicle speed are determined offline using multi-parameter linear programming (mp-LP), simplifying online implementation to searching for optimal solutions in a lookup table. Simulation results demonstrate a significant improvement in active suspension control under changing vehicle speeds compared to passive control. [ABSTRACT FROM AUTHOR]

Copyright of Systems Engineering & Electronics is the property of Journal of Systems Engineering & Electronics Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

During photovoltaic (PV) conversion in solar panels, a part of the solar radiation is not converted to electricity by the cells, producing heat that could increase their temperature. This increase in temperature deteriorates the performance of the PV panel. In this paper, a hybrid PV/thermal (PV/T) water system is proposed to mitigate this problem. This system combines a PV panel and a thermal collector. In this paper, we focused on the modeling and control of this hybrid system in the linear parameter varying (LPV) framework. An optimal linear quadratic regulator (LQR) is proposed to control the PV cell temperature around an optimal value that maximises electricity generation. Since the system model is nonlinear, an optimal LQR gain-scheduling state-feedback control approach based on an LPV representation of the nonlinear model is designed using the Linear Matrix Inequality (LMI) method. The goal is to obtain the maximum electrical power for each solar panel. Since a reduced number of sensors is available, an LPV Kalman filter is also proposed to estimate the system states required by the state-feedback controller. The obtained results in a laboratory setup in simulation are used to assess the proposed approach, showing promise in terms of control performance of the PV/T system. [ABSTRACT FROM AUTHOR]

This paper proposes a model predictive control (MPC) scheme based on linear parameter variation to enhance the damping control of speed-dependent active suspensions. The controller is developed by introducing a speed-dependent term, specifically front- and rear-wheel time delays, to the half-car model using the Padé approximation. Subsequently, the model is augmented with time-varying parameter dependence. An adaptive Kalman filter based on variance matching is employed to estimate system states affected by imprecise sensor measurement noise. Finally, a set of explicit control laws incorporating road preview information and available vehicle speed are determined offline using multi-parameter linear programming (mp-LP), simplifying online implementation to searching for optimal solutions in a lookup table. Simulation results demonstrate a significant improvement in active suspension control under changing vehicle speeds compared to passive control.

Dynamic trajectory planning (DTP) and Dynamic trajectory tracking (DTT) are the real-time mutual coupling in the process of the dynamic lateral obstacle avoidance (DLOA) of connected and automated vehicles (CAVs). Meanwhile, the varying velocity and acceleration of obstacle vehicles (OVs) increase the difficulties of DTP. Furthermore, the parameters perturbation in CAVs (such as mass and cornering stiffness), the varying velocities of CAVs and the signal disturbances, raise the difficulties of DTT. Therefore, the DLOA is challenging due to the interaction of the above multiple factors. To address the problem, this paper proposes a robust gain-scheduling control strategy of DLOA for CAVs. The strategy is divided into two modules namely DTP and DTT, and the two modules cooperate with each other in real time. In the module of DTP, the optimal trajectory considering the efficiency, passenger comfort and safety is real-time optimized in the dynamic safe limit which is real time predicted according to the information from CAVs and OVs. In the module of DTT, the real-time trajectory reference is tracked. Robust gain-scheduling control is realized to cope with variation of real-time trajectory reference, varying velocity, parameters perturbation and signal disturbances during the process of DLOA. The simulation results indicate that the strategy can effectively achieve DLOA maintaining the vehicle stability across various working conditions. [ABSTRACT FROM AUTHOR]

This paper proposes a new algorithm to reduce the number of gains of a polytopic LPV controller considering generic tuples of vertices, for which a common controller gain can be used. The use of Frobenius norm and the inclusion of the input matrix in the LMIs perturbation matrix allows decreasing the conservativeness to select vertices which are combinable, with respect to a previous approach based on Gershgorin circles. A combinability metric that can be applied to an arbitrary partition of the set of vertices is defined. Then, a recursive algorithm finds a lesser-fragmented combinable partition at each iteration by combining together two elements of a partition. The algorithm aims at finding combinable partitions with minimal cardinality in fewer attempts, always preserving the original control performance specifications. The proposed method is validated using numerical examples, a twin rotor MIMO system and a two-link robotic manipulator. [ABSTRACT FROM AUTHOR]

Over the past decades, power engineers have begun to connect power grids to other networks such as microgrids associated with renewable units using long transmission lines to provide higher reliability and greater efficiency in production and distribution besides saving resources. However, many dynamic problems such as low frequency oscillations were observed as a result of these connections. Low frequency oscillation is a normal phenomenon in most power systems that causes perturbations and, thus, the grid stability and damping process are of paramount importance. In this paper, to attenuate these oscillations, a novel method for designing Power System Stabilizer (PSS) is presented via Linear Parameter-Varying (LPV) approach for a Single Machine Infinite Bus system (SMIB). Because the system under study is subject to frequent load and production changes, designing the stabilizer based on the nominal model may not yield the desired performance. To guarantee the flexibility of the stabilizer with respect to the aforementioned issues, the power system polytopic representation is used. In order to apply the new method, the nonlinear equations of the system at each operating point, located in a polytope, are parametrically linearized by scheduling variables. Scheduling variables can be measured online in any operating point. By using this model and following the H∞ synthesis, feedback theories, and Linear Matrix Inequalities (LMIs), LPV controllers at all operating points are obtained. Finally, the simulation results verify the effectiveness of the proposed controller over classic and robust controllers with regard to uncertainties and changes in system conditions.

This work analyzes the design and implementation of piecewise affine (PWA) Linear Parameter Varying (LPV) controllers. PWA-LPV models can be employed to describe complex nonlinear or time-varying systems with the aim of control design. The complexity of a PWA-LPV controller depends on the number of local models used to describe the plant. Here we propose a methodology to reduce this number and thus simplify the design and implementation complexity of the resulting controller without a significant performance degradation. The methodology is illustrated by an academic example and a more realistic wind turbine control application. [ABSTRACT FROM AUTHOR]

This paper addresses the new method for output voltage regulation of DC–DC buck converter nonlinear systems by a sampled-data linear parameter varying (LPV) controller. For this purpose, an output-error state-space affine LPV model is presented for DC–DC buck converter nonlinear systems. The sampled-data structure of the controller is considered as a time delay in the input, and stabilization conditions are obtained for LPV systems with affine dependence on the parameter by using a parameter-dependent Lyapunov–Krasovskii functional. Then, the design condition of the sampled-data LPV controller with an appropriate sampling period is derived to guarantee that the output voltage of the DC–DC buck converter can be adjusted to the desired voltage. Finally, simulation results are provided to show the validity of the presented approach in practical control applications where there are limitations on the value of the sampling period and the cost of the digital implementation. [ABSTRACT FROM AUTHOR]

For years now, phase-amplitude cross frequency coupling (CFC) has been observed across multiple brain regions under different physiological and pathological conditions. It has been suggested that CFC serves as a mechanism that facilitates communication and information transfer between local and spatially separated neuronal populations. In non-invasive brain computer interfaces (BCI), CFC has not been thoroughly explored. In this work, we propose a CFC estimation method based on Linear Parameter Varying Autoregressive (LPV-AR) models and we assess its performance using both synthetic data and electroencephalographic (EEG) data recorded during attempted arm/hand movements of spinal cord injured (SCI) participants. Our results corroborate the potentiality of CFC as a feature for movement attempt decoding and provide evidence of the superiority of our proposed CFC estimation approach compared to other commonly used techniques. [ABSTRACT FROM AUTHOR]

With the large-scale wind power integration into the power system, the inertia and damping characteristics of the system have been weakened, which is not conducive to fulfill the grid-friendly operation of wind power. Aiming at this, an adaptive control scheme combining virtual inertia control (VIC) and supplementary damping controller (SDC) is proposed in this paper. Firstly, the mathematical model of wind grid-connected power system with VIC is established. Secondly, the polytopic linear parameter varying (LPV) system is built to describe the uncertainty of system operating points, and the gap metric-based nonlinear evaluation method is used for polytope vertex configuration. Next, Youla-Kucera parametrization are extended to polytopic LPV system to design an adaptive damping control, in which VIC and SDC are coordinated to provide damping and virtual inertia simultaneously. Finally, the proposed adaptive control scheme is compared with several existing control techniques to validate its effectiveness and advantages in suppressing system oscillation in the modified IEEE 39-bus system integrated with 2 wind turbines. [ABSTRACT FROM AUTHOR]

This paper proposes a path-following controller and a torque allocation strategy for a four-wheel-independent-drive (FWID) electric autonomous ground vehicle (EAGV) on the slope roads. To improve the real-time accuracy of the controller, a model-predictive-control (MPC) controller with a linear-parameter-varying (LPV) model is proposed to maintain the vehicle dynamic stability and follow the given path for the EAGV. By updating the parameters of the states, the proposed controller can minimize the effects of the unequal normal tire forces and the gravity components of the vehicle on the slope road. As a result, the desired trajectory of the lateral position, longitudinal position, yaw angle, and velocity can be tracked by controlling the front-wheel-steering angle and the driving torque of each wheel. Finally, the high-fidelity simulations have been implemented on the CarSim-Matlab platform, and the results show that the proposed LPV-MPC controller with the torque allocation strategy is effective to achieve the control targets. [ABSTRACT FROM AUTHOR]

This study introduces an online reconfigurable road-adaptive semi-active suspension controller that reaches the performance objectives with satisfying the dissipativity constraint. The concept of the model is based on a nonlinear static model of the semi-active Magnetorheological (MR) damper with considering the bi-viscous and hysteretic behaviors of the damper. The input saturation problem has been solved by using the proposed method in the literature that allows the integration of the saturation actuator in the initial system to create a Linear Parameter Varying (LPV) system. The control input meets the saturation constraint; therewith, the dissipativity constraint is fulfilled. The online reconfiguration and adaptivity problem is solved by using an external scheduling variable that allows the trade-off between driving comfort and road holding/stability. The control design is based on the LPV framework. The proposed adaptive semi-active suspension controller is compared to passive suspension and Bingham model with Simulink simulation, and then the adaptivity of the controller is validated with the TruckSim environment. The results show that the proposed LPV controller has better performance results than the controlled Bingham and passive semi-active suspension model. [ABSTRACT FROM AUTHOR]

The control of general nonlinear systems is a challenging task in particular for large-scale models as they occur in the semi-discretization of partial differential equations (PDEs) of, say, fluid flow. In order to employ powerful methods from linear numerical algebra and linear control theory, one may embed the nonlinear system in the class of linear parameter varying (LPV) systems. In this work, we show how convolutional neural networks can be used to design LPV approximations of incompressible Navier-Stokes equations. In view of a possibly low-dimensional approximation of the parametrization, we discuss the use of deep neural networks (DNNs) in a semi-discrete PDE context and compare their performance to an approach based on proper orthogonal decomposition (POD). For a streamlined training of DNNs directed to the PDEs in a Finite Element (FEM) framework, we also discuss algorithmical details of implementing the proper norms in general loss functions.

Copyright of Systems Engineering & Electronics is the property of Journal of Systems Engineering & Electronics Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In this article, an offline model predictive control (MPC) based on the linear parameter-varying (LPV) model is developed for free-floating space robots. Parameter set mapping is employed to obtain an LPV model with fewer scheduling parameters, which can enable easier control design and achieve more objectives by using linear control methods. In terms of the LPV system with external disturbance, an MPC method is proposed. Since large disturbance to the base attitude is not admissible in certain missions such as communication with the earth, a term related to the base angular velocity is considered as a part of the cost function of MPC. To deal with actuator saturation, input limitations are included in the constraints of MPC. Moreover, an offline algorithm is introduced to reduce the online computation. Numerical simulations are presented to demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

The present paper exhibits a real time assessment of a robust Energy Management Strategy (EMS) for battery-super capacitor (SC) Hybrid Energy Storage System (HESS). The proposed algorithm, dedicated to an electric vehicular application, is based on a self-gain scheduled controller, which guarantees the H∞ performance for a class of linear parameter varying (LPV) systems. Assuming that the duty cycle of the involved DC-DC converters are considered as the variable parameters, that can be captured in real time, and forwarded to the controller to ensure both; the performance and robustness of the closed-loop system. The subsequent controller is therefore time-varying and it is automatically scheduled according to each parameter variation. This algorithm has been validated through experimental results provided by a tailor-made test bench including both the HESS and the vehicle traction emulation system. The experimental results demonstrate the overall stability of the system, where the proposed LPV supervisor successfully accomplishes a power frequency splitting in an adequate way, respecting the dynamic of the sources. The proposed solution provides significant performances for different speed levels. [ABSTRACT FROM AUTHOR]

This work presents a robust control system for a twin hull-based unmanned surface vehicle, named EDSON-J, which is being built at the Universidad Nacional de San Agustín de Arequipa and whose main mission is the autonomous monitoring of ocean water quality and lagoons vulnerable to heavy metal contamination. The vehicle has been designed to carry additional payload consisting of electronic instrumentation destined to measure the water conditions in real time. This paper focuses on the control design of controllers for an unmanned surface vehicle with mass as parameter varying. The approach used is the linear parameter-varying control (LPV control); its goal is to synthesize a controller that guarantees robust stability and performance of the system despite the presence of parameter varying, disturbances and sensor noise. The simulation results, in frequency domain and time domain, show an improvement in the LPV robust control approach in comparison with LTI/ H ∞ controllers. [ABSTRACT FROM AUTHOR]

This study introduces an online reconfigurable road-adaptive semi-active suspension controller that reaches the performance objectives with satisfying the dissipativity constraint. The concept of the model is based on a nonlinear static model of the semi-active Magnetorheological (MR) damper with considering the bi-viscous and hysteretic behaviors of the damper. The input saturation problem has been solved by using the proposed method in the literature that allows the integration of the saturation actuator in the initial system to create a Linear Parameter Varying (LPV) system. The control input meets the saturation constraint; therewith, the dissipativity constraint is fulfilled. The online reconfiguration and adaptivity problem is solved by using an external scheduling variable that allows the trade-off between driving comfort and road holding/stability. The control design is based on the LPV framework. The proposed adaptive semi-active suspension controller is compared to passive suspension and Bingham model with Simulink simulation, and then the adaptivity of the controller is validated with the TruckSim environment. The results show that the proposed LPV controller has better performance results than the controlled Bingham and passive semi-active suspension model.

Several studies exist on topics of semi-active suspension and vehicle cruise control systems in the literature, while many of them just consider actual road distortions and terrain characteristics, these systems are not adaptive and their subsystems designed separately. This study introduces a new method where the integration of look-ahead road data in the control of the adaptive semi-active suspension, where it is possible to the trade-off between comfort and stability orientation. This trade-off is designed by the decision layer, where the controller is modified based on prehistorical passive suspension simulations, vehicle velocity and road data, while the behavior of the controller can be modified by the use of a dedicated scheduling variable. The adaptive semi-active suspension control is designed by using Linear Parameter Varying (LPV) framework. In addition to this, it proposes designing the vehicle velocity for the cruise controller by considering energy efficiency and comfort together. TruckSim environment is used to validate the operation of the proposed integrated cruise and semi-active suspension control system. [ABSTRACT FROM AUTHOR]

Morphings are usually used for aircrafts to achieve a better performance in variable environment, whereas this paper focuses on how to achieve a trajectory-attitude separation control effect with an active morphing strategy. Aimed at this target, a systemic work, including modeling, linearization, and control, is presented. An accurate nonlinear dynamic model of morphing aircraft is built with centroid dynamic equations, of which all the additional terms that stem from morphing are expatiated for gullwing aircraft. Then, a linear parameter varying (LPV) approach is applied to linearize the equations for a controller design. A state feedback with a feedforward H∞ control approach for an LPV system is proposed in this paper and is applied to the attitude control of gull-wing aircraft. Based on the stabilized aircraft attitude, a dynamic inversion control approach for the trajectory channels with active morphing is designed. The nonlinear simulation cases validate the feasibility of trajectory-attitude separation control with active morphing, as well as the performance of the control approach proposed in this paper.

In this paper, a novel technique for synthesizing static anti-windup compensator (AWC) is explored for dynamic nonlinear plants with state interval time-delays, exogenous input disturbance, and input saturation nonlinearity, by means of reformulated Lipschitz continuity property. A delay-range-dependent approach, based on Wirtinger-based inequality, is employed to derive a condition for finding the static AWC gain. By using the Lyapunov–Krasovskii functional, reformulated Lipschitz continuity property, Wirtinger-based inequality, sector conditions, bounds on delay, range of time-varying delay, and L 2 gain reduction, several conditions are derived to guarantee the global and local stabilization of the overall closed-loop system. Further, when the lower time-delay bound is zero, the delay-dependent stabilization condition is derived for saturated nonlinear time-delay systems as a particular scenario of the suggested static AWC design approach. Furthermore, a static AWC design strategy is also provided when a delay-derivative bound is not known. An application to the nonlinear dynamical system is employed to demonstrate the usefulness of the proposed methodologies. A comparative numerical analysis with the existing literature is provided to show the superiority of the proposed AWC results. [ABSTRACT FROM AUTHOR]

Several studies exist on topics of semi-active suspension and vehicle cruise control systems in the literature, while many of them just consider actual road distortions and terrain characteristics, these systems are not adaptive and their subsystems designed separately. This study introduces a new method where the integration of look-ahead road data in the control of the adaptive semi-active suspension, where it is possible to the trade-off between comfort and stability orientation. This trade-off is designed by the decision layer, where the controller is modified based on prehistorical passive suspension simulations, vehicle velocity and road data, while the behavior of the controller can be modified by the use of a dedicated scheduling variable. The adaptive semi-active suspension control is designed by using Linear Parameter Varying (LPV) framework. In addition to this, it proposes designing the vehicle velocity for the cruise controller by considering energy efficiency and comfort together. TruckSim environment is used to validate the operation of the proposed integrated cruise and semi-active suspension control system.

By several simple local linear models a complex nonlinear process can be well approximated, so the linear parameter varying (LPV) method can largely simplify the complexity of model development and is widely used in the continuous process. Directly employing the regular LPV method in batch processes may develop a model for a single batch run; however, a whole batch process usually covers a number of batch runs. To develop a model for the whole batch process, this paper proposes a multi-way LPV (MLPV) approach for batch process modeling. In the proposed MLPV method, a sample is constructed by the measurements of all batch runs at each sampling instant. Comparing with the regular LPV model, the MLPV method extends the input, output and scheduling variables along the time dimension to along both the time dimension and the batch dimension. Then, the regular LPV method is performed on these “extended” samples according to the “extended” scheduling variable, by which both the variation among the batch runs and the dynamics within each batch run are summarized in the model development. The application of the MLPV method in three cases illustrates its advantages. [ABSTRACT FROM AUTHOR]

This paper presents an \mathcal H_\infty controller with an \mathcal H_\infty state estimator using a linear parameter-varying (LPV) model of permanent magnet synchronous motor (PMSM). The proposed control method comprises a nonlinear torque modulation, an LPV \mathcal H_\infty state estimator, and an LPV \mathcal H_\infty state feedback controller. The use of nonlinear torque modulation enables formulation of the electromechanical dynamics of the PMSM in the form of an LPV system. The LPV \mathcal H_\infty state estimator is designed to estimate the velocity, currents, and disturbance based solely on position measurement. We introduce a vertex expansion technique to cover all operating points of an LPV system. A velocity tracking controller is designed in the frame work of \mathcal H_\infty control to be robust against disturbance. The proposed controller was implemented and validated with a motor generator set consisting of two PMSMs. Experimental results are presented to validate the effectiveness of the proposed method compared to conventional field-oriented control. These results show improved transient responses to velocity reference in the presence of disturbance. [ABSTRACT FROM PUBLISHER]

This document is an editorial published in the journal "Frontiers in Control Engineering" on the topic of Linear Parameter Varying (LPV) Systems Modeling, Identification, and Control. The editorial introduces the concept of LPV systems, which are linear systems with parameters that vary based on a scheduling signal. LPV modeling and control techniques are widely used in various fields such as aeronautics, automotive, robotics, and bioengineering. The editorial highlights several articles included in the research topic, which cover applications of LPV systems in areas such as control of time-delay systems, path tracking control for electric vehicles, estimation of non-measurable variables in spinal cord injury patients, and control design for LPV systems with input delay and saturation. The authors express their gratitude to the contributing authors and emphasize the significance of the research presented in the editorial. [Extracted from the article]

In this paper, a novel decoupled architecture-based anti-windup compensator (AWC) synthesis technique is presented for the constrained nonlinear time-delay systems. The proposed decoupling AWC architecture is applicable to both stable and unstable nonlinear time-delay systems and it simultaneously fulfills the requirements of attainment of stability and reasonable closed-loop performance. The proposed AWC architecture is modified with the aim to characterize the synthesis goals of the delayed nonlinear AWC by using the linear parameter varying model-based reformulation property of the Lipschitz nonlinearities. To compute the gain matrices for global or local stability through the proposed AWC, several sufficient conditions have been formulated using a Lyapunov-Krasovskii functional, the $$L_2 $$ gain minimization, the generalized sector condition and the information of bounds of delay. The proposed approach is less conservative than the existing methods due to useful terms in handling the range of time-delay. Further, the resultant method is better than the conventional methods owing to the incorporation of reformulation property for efficient handling of the Lipchitz nonlinearities. The effectiveness of the proposed AWC schemes is demonstrated via simulation results. [ABSTRACT FROM AUTHOR]

Considered in this paper is a novel model-based, coordinated ramp metering strategy. It aims at maximizing the discharge flow in motorway networks by minimizing the divergence of the traffic density from its critical value caused by unknown demand flow. The suggested synthesis algorithm casts the traffic control objective into the form of an induced \mathcal L2 -norm minimization problem. Hence, we aim at rejecting the effect of disturbance on the overall network performance output while the ramp input flow is subjected to constraints. With such a problem formulation, it is not required to know the disturbance input in order to find the proper control input. Without any central decision unit (traffic control center), ramp meters coordinate by sharing their local variables with solely their neighbor units (upstream and downstream) to achieve the global performance goal. Under some network symmetry conditions, a compositionally inexpensive distributed flow control method is suggested to address scalability issues. The method is implemented in simulation environment and compared with other control algorithms in two comprehensive case studies. [ABSTRACT FROM PUBLISHER]

In order to track the desired trajectory for intelligent vehicle, a new hierarchical control strategy is presented. The control structure consists of two layers. The high-level controller adopts the model predictive control (MPC) to calculate the steering angle tracking the desired yaw angle and the lateral position. The low-level controller is designed as a gain-scheduling controller based on linear matrix inequalities. The desired longitudinal velocity and the yaw rate are tracked by the adjustment of each wheel torque. The simulation results via the high-fidelity vehicle dynamics simulation software veDYNA show that the proposed strategy has a good tracking performance and can guarantee the yaw stability of intelligent vehicle. [ABSTRACT FROM AUTHOR]

In lighting systems consisting light-emitting diodes (LEDs), excessive temperature is a main cause of degraded luminous efficacy, which leads to reduced average illuminance and distorted illumination rendering. Modeling the thermal dynamics of LEDs is hence essential in designing thermal dissipators and controllers for maintaining constant illuminance or chromaticity. In the existing literature, both physical modeling and system identification have been proposed, which all find the dependence of the temperature on the input power. When the power fluctuates, e.g., due to dimming control, the thermal dynamics becomes nonlinear. Moreover, when a photoelectrothermal model is used in the control synthesis, the nonlinearity due to the product of the temperature-dependent efficacy and the input power must be considered. These nonlinearities are either ignored or linearized in most existing methods. The main contribution of this work is treating the aforementioned nonlinearities in a linear parameter varying (LPV) framework. First, the nonlinear thermal dynamics is identified by LPV system identification techniques. Then, a controller to track reference illuminance is designed by \mathcal H\infty control techniques to be robust to both the temperature and the disturbance from ambient light. The identification data and the designed controller are collected from and verified on a real experimental setup. [ABSTRACT FROM PUBLISHER]

This paper considers gain-scheduled control within a passive systems framework. Inspired by the passivity theorem and linear parameter varying systems theory, a very strictly passive gain-scheduled controller is introduced. The proposed gain-scheduled controller is used in tandem with an actuator saturation prevention technique. The actuator saturation prevention technique does not violate the passive properties of the controller, can account for feedforward control, and can have variable saturation limits. Scheduling signals are bounded and square integrable on finite time intervals. They have no restrictions on their derivatives, can be nonlinear, and can be functions of system states, exogenous signals, or time. The controller is applied to a trajectory tracking problem of a two-link flexible manipulator's heavy payload. The proposed controller is compared to two other gain-scheduling paradigms through experimentation. The theoretical and experimental results of this paper show that the proposed control architecture with a fixed number of integrating states is able to have improved stability properties over a traditional linear parameter varying controller without compromising performance. In addition, the proposed controller is able to asymptotically track a trajectory while avoiding actuator saturation. [ABSTRACT FROM PUBLISHER]

This paper is concerned with bump-less transfer of parameterized disturbance observer based controller with individual pitch control strategy to reduce cyclic loads of wind turbine in full load operation. Cyclic loads are generated due to wind shear and tower shadow effects. Multivariable disturbance observer based linear controllers are designed with objective to reduce output power fluctuation, tower oscillation and drive-train torsion using optimal control theory. Linear parameterized controllers are designed by using a smooth scheduling mechanism between the controllers. The proposed parameterized controller with individual pitch was tested on nonlinear Fatigue, Aerodynamics, Structures, and Turbulence (FAST) code model of National Renewable Energy Laboratory (NREL)’s 5 MW wind turbine. The closed-loop system performance was assessed by comparing the simulation results of proposed controller with a fixed gain and parameterized controller with collective pitch for full load operation of wind turbine. Simulations are performed with step wind to see the behavior of the system with wind shear and tower shadow effects. Then, turbulent wind is applied to see the smooth transition of the controllers. It can be concluded from the results that the proposed parameterized control shows smooth transition from one controller to another controller. Moreover, 3p and 6p harmonics are well mitigated as compared to fixed gain DOBC and parameterized DOBC with collective pitch.

This paper develops a model-based control strategy for a bistable positioning system based on a dielectric elastomer. The motion is generated by the electrostatic compressive force between two compliant electrodes applied on the surface of the elastomer. The membrane is connected to a bistable spring that acts as a biasing element and significantly extends the possible stroke. Such a design choice, however, makes the system strongly nonlinear and unstable in open-loop. Starting from the extension of a dynamic model developed for a simpler version of the actuator, this paper proposes a strategy based on robust control design tools for linear parameter-varying systems. The approach guarantees both stability and worst case performance in the whole operating range of the system. Both simulations and experiments are used to assess the advantages of the proposed design method. [ABSTRACT FROM AUTHOR]