This study deals with linear parameter-varying modelling and output-feedback H∞ control design for an offshore wind turbine. The controller is designed with consideration that not all the information in the feedback loop will be used. This constraint is incorporated into the design procedure. Constrained information means that a special zero-non-zero pattern is forced upon the gain matrix. The constrained controller is obtained based on parameter-dependent Lyapunov functions and formulated in terms of linear-matrix inequalities. Since the functions are dependent on the wind speed and accurate wind speed measurements are rarely available in practice, an extended Kalman filter is used to estimate the wind speed. The controller is designed in such a way that it should maintain its stability and performance even if one of the sensors in the feedback loop should malfunction. The control objectives are to mitigate oscillations in the structure and drivetrain, to smoothen power/torque output in addition to keep the closed-loop system stable. This should be achieved by means of individual blade pitch. A traditional procedure for designing a controller for such a system is to choose an operating point and assume it works in a suitable way under the influence of turbulent wind. In this study, the wind turbine model is obtained from the software fatigue, aerodynamic, structural and turbulence (FAST). To design the controller, the model is linearised about several operating points. The degrees of freedom in the linearised model are chosen according to the controller objectives. The linear models are valid within the span of operating points. Finally, the controller is tested on the fully non-linear system under the influence of turbulent wind and a scenario where one of the sensors in the feedback loop is malfunctioning. The closed-loop response of the presented controller is compared to the closed-loop response of the baseline controller included in the FAST package along with a controller designed based on a single linearised model.
1 School of Astronautics, Harbin Institute of Technology, Harbin, Heilongjiang, China 2 School of Electrical and Electronic Engineering, The University of Adelaide, SA 5005, Australia 3 Department of Engineering, Faculty of Engineering and Science, University of Agder, 4898 Grimstad, Norway 4 Institute of Automation and Complex Systems, University of Duisburg-Essen, Duisburg, Germany 5 College of Automation, Chongqing University, Chongqing 400044, China
This paper presents development of a mathematical model to represent the real vehicle frontal crash scenario. The vehicle is modeled by a double spring-mass-damper system. The front mass m <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> represents the chassi of the vehicle and rear mass m <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> represents the passenger compartment. The physical parameters of the model (Stiffness and dampers) are estimated using Nonlinear least square method (Levenberg-Marquart algorithm) by curve fitting the response of a double spring-mass-damper system to the experimental displacement data from the real vehicle crash. The model is validated by comparing the results from the model with the experimental results from real crash tests available.
The Transport and Road Research Laboratory has previously reported on research which shows that leg protection for motorcyclists can be designed which will be of benefit to the legs without detriment to the head. It has also been shown that the United Kingdom draft specification on leg protection can be sucessfully applied to a large touring machine. This report describes the application of leg protection to a sports motorcycle Kawasaki GPZ 500S. The purpose of the test series was twofold. First, to show the effect of leg protection, designed and tested to the U.K. draft specification, on the potential leg and head injuries when fitted to a sports motorcycle. The second purpose was to compare the performance of the TRRL injury indicating leg, which is not frangible, with one that is, and to determine whether or not the TRRL leg is an appropriate device with which to assess potential leg injuries and dummy trajectory in motorcycle impact testing. For the covering abstract see IRRD 853578.
The potential of multiantenna interference cancellation receiver algorithms for increasing the uplink throughput in WLAN systems such as 802.11 is investigated. The medium access control (MAC) in such systems is based on carrier sensing multiple-access with collision avoidance (CSMA/CA), which itself is a powerful tool for the mitigation of intrasystem interference. However, due to the spatial dependence of received signal strengths, it is possible for the collision avoidance mechanism to fail, resulting in packet collisions at the receiver and a reduction in system throughput. The CSMA/CA MAC protocol can be complemented in such scenarios by interference cancellation (IC) algorithms at the physical (PHY) layer. The corresponding gains in throughput are a result of the complex interplay between the PHY and MAC layers. It is shown that semiblind interference cancellation techniques are essential for mitigating the impact of interference bursts, in particular since these are typically asynchronous with respect to the desired signal burst. Semiblind IC algorithms based on second- and higher-order statistics are compared to the conventional no-IC and training-based IC techniques in an open access network (OAN) scenario involving home and visiting users. It is found that the semiblind IC algorithms significantly outperform the other techniques due to the bursty and asynchronous nature of the interference caused by the MAC interference avoidance scheme.
This article presents a novel sampled‐data static output‐feedback control technique for continuous‐time linear parameter‐varying (LPV) systems. Particularly, the input‐delay approach is introduced to map the sample‐and‐hold dynamics into the continuous‐time domain with delay in the states. This, together with a new parameter‐dependent Lyapunov–Krasovskii functional, results in a bounded real lemma for the underlying closed‐loop LPV systems. Furthermore, application of convex optimization techniques transforms the controller synthesis problem into the feasibility of a group of parameterized matrix inequalities. It is also shown that the convexification procedures allow for controller dependence on time‐varying parameters. Simulation studies are conducted on a case comparison and an overhead crane model to validate the efficacy and less conservatism of the result.
Recent advances in computing and network technologies have contributed much to the successful handling of certain problems in biology, physics, economics, and so forth that until recently were thought too difficult to be analyzed.These complex systems problems tend to share a number of interesting properties from a mathematical viewpoint.A key feature of such systems is that the nonlinear interactions among its components can lead to interesting emergent behavior.The overall aim of this special issue is to bring together the latest/innovative knowledge and advances in mathematics for handling complex systems, which may depend largely on methods from artificial intelligence, statistics, operational research, and engineering, including nonlinear dynamics, time series analysis, dynamic systems, cellular automata, artificial life, evolutionary computation, game theory, neural networks, multi-agents, and heuristic search methods.The solicited papers in this special issue should provide solutions, or early promises, to modeling, analysis, and control problems of real-world complex systems, such as communication systems, process control, environmental systems, intelligent manufacturing systems, transportation systems, and structural systems.Topics include, but are not limited to: (1) control systems theory (behavioural systems, networked control systems, delay systems, distributed systems, infinite-dimensional systems and positive systems), (2) networked control (channel capacity constraints, control over communication networks, distributed filtering and control, information theory and control, and sensor networks), and (3) stochastic systems (nonlinear filtering, nonparametric
This paper considers the problem of robust control for a class of uncertain state-delayed singularly perturbed systems with norm-bounded nonlinear uncertainties. The system under consideration involves state time delay and norm-bounded nonlinear uncertainties in the slow state variable. It is shown that the state feedback gain matrices can be determined to guarantee the stability of the closed-loop system for all ε ∈ (0, ∞) and independently of the time delay. Based on this key result and some standard Riccati inequality approaches for robust control of singularly perturbed systems, a constructive design procedure is developed. We present an illustrative example to demonstrate the applicability of the proposed design approach.
It is well known that cyber-physical systems (CPSs) commonly exist in both industrial manufacturing and people’s daily lives. As a hot topic within Industry 4.0, CPSs have attracted interest from both academia and industry. Typical examples of CPSs include autonomous vehicles, smart grid, process control systems, and industrial robotics systems. The traditional techniques are mainly focused on either the physical object or the abstract data model individually. A simultaneous consideration of both domains is needed.
Active noise control (ANC) methods have been extensively used for reducing the noise level over the past decade. In particular, in the range of low frequencies they can effectively reduce the noise level higher than passive control methods. However, except in some special cases, in ANC only local zones of quiet are obtained. In this paper one of such exceptions is investigated—an active noise-controlling casing. More specifically, a noise source is placed inside the casing. Casing vibrations are actively controlled to achieve global noise reduction in free-field conditions. The global noise reduction is obtained using the Switched Error FXLMS algorithm and using error signals from error microphones located at appropriate points around the casing. It is shown that selected microphone array configurations can provide global noise reduction in free-field conditions. Performance of this approach is experimentally verified in a laboratory room with an exemplary acoustic treatment.
No abstract is provided for this article.
The problem of trajectory tracking is considered in this paper for Lagrange systems disturbed by second moment processes. For random differential equations, the concept of noise-to-state practical stability and its criterion are proposed. A state-feedback tracking control is designed by using vectorial backstepping method, which covers Slotine–Li controller and “PD+” controller as special cases. As natural extension, adaptive control is further researched, and a practical equivalence principle is presented. For the above two cases, results of global noise-to-state stability of closed-loop systems are obtained, and practical trajectory tracking can be achieved under a practical parameters-tuning principle. Simulations are conducted for a nonlinear benchmark system to illustrate the effectiveness and advantages of the proposed new control strategies.
In this paper a state-space estimation procedure that relies on the time-domain analysis of input and output signals is used for mathematical modeling of vehicle frontal crash. The model is a double-spring–mass–damper system, whereby the front mass and real mass represent the chassis and the passenger compartment, respectively. It is observed that the dynamic crash of the model is closer to the dynamic crash from experimental when the mass of the chassis is greater than the mass of the passenger compartment. The dynamic crash depends on pole placement and the estimated parameters. It is noted that when the poles of the model are closer to zero, the dynamic crash of the model is far from the dynamic crash from the experimental data. The stiffness and damping coefficients play an important role in the dynamic crash.
