Summary This article investigates the dynamic output feedback control problem for a class of networked Markovian jump systems via sliding mode control method. The stochastic communication protocol schedules the communication between sensors and the controller, by which only one sensor node can access the network at one instant. First, a compensator at the controller side is utilized to yield the available measurement signals composed of the current measurement output transmitted by the selected sensor node and the past measurement output from other sensor nodes. A joint Markovian chain incorporating the Markovian chains of the system as well as the stochastic communication protocol is introduced to model the Markovian jump system subject to the stochastic communication protocol. And then, the dynamic output feedback sliding mode controller is designed and the stochastic stability is analyzed. Moreover, a feasible solving algorithm is provided via a projection technique. Finally, the proposed control scheme is verified via an electrical motor model.
Electronic throttle is widely used in modern automotive engines. An electronic throttle system regulates the throttle plate angle by using a DC servo motor to adjust the inlet airflow rate of an internal combustion engine. Its application leads to improvements in vehicle drivability, fuel economy, and emissions. In this paper, by taking into account the dynamical behavior of the electronic throttle, the mechanism model is first built, and then the mechanism model is transformed into the state-space model. Based on the state-space model and using the backstepping design technique, a new backstepping controller is developed for the electronic throttle. The proposed controller can make the actual angle of the electronic throttle track its set point with the satisfactory performance. Finally, a computer simulation is performed, and simulation results verify that the proposed control system can achieve favorable tracking performance.
A novel attitude tracking control scheme is presented for overactuated spacecraft to address the attitude stabilization problem in presence of reaction wheel installation deviation, external disturbance and uncertain mass of moment inertia. An adaptive sliding mode control technique is proposed to track the uncertainty. A Lyapunov-based analysis shows that the compensation control law can guarantee that the desired attitude trajectories are followed in finite-time. The key feature of the proposed control strategy is that it globally asymptotically stabilizes the system, even in the presence of reaction wheel installation deviation, external disturbances, and uncertain mass of moment inertia. The attitude track performance using the proposed finite-time compensation control is evaluated through a numerical example.
Due to the complexity of determining the initial rotor flux and detecting errors, conventional rotor flux observation methods are easily affected by direct current (DC) components and harmonics. To address this issue, this paper proposes an in-phase filter (IPF)-based rotor flux observation strategy for sensorless control of permanent magnet synchronous machines (PMSMs). The core components of the IPF consist of a double second-order generalized integrator (DSOGI) and a phase angle compensation transfer function (PACTF). The DSOGI provides a accurate electrical angular frequency, while the PACTF implements a phase correction to the v q ′ signals. By employing IPF structure, accurate observations for rotor flux, electronic speed, and rotor position are achieved, which can be effectively used in the sensorless control of PMSMs, eliminating the need for magnitude and phase compensations. Finally, the proposed observation strategy is applied to an experimental bench of a PMSM, and its effectiveness is illustrated by experimental results. From experimental results, it can be concluded that the IPF is significantly better than the LPF, and 5% more accurate than the observer based on cascade second-order generalized integral(CSOGI) overall.
This paper investigates the H ∞ control problem for the networked control systems with stochastic sampling. The network topology structure is CAN network owing to the fact that network-induced delay is always less than a sampling period and event driven is supported in CAN network. The continuous-time model with a variable satisfying a Bernoulli distribution is established for network control systems with stochastic sampling by employing the input-delay approach. Based on this, sufficient conditions are obtained, which guarantee the mean-square exponential stability of the system with an H ∞ performance. Moreover, an H ∞ controller design procedure is proposed. The simulation shows the validity of the control strategy and less conservatism.
No abstract is provided for this article.
No abstract is provided for this article.
Due to the significant growth in transportation industries, the importance of interactions between vehicle technologies and humans from the safety aspect plays an important role in the new developments of complex vehicle systems. Therefore, from academic and industrial viewpoints, increasing attention has been paid to complex vehicle correlative technologies with full consideration of issues like safety, robust performance, impact analysis, optimization, automation, motion control, etc. All aforementioned issues provide a basis for the design and operation of practical vehicle systems in order to achieve desired complex tasks.
The last few decades have witnessed the rapid growth of research & development on large-scale systems (LSSs) due to the increasing complexity and the growing demand of modern engineering systems. The LSSs can normally be viewed as interconnections of multiple subsystems. Many practical systems can be described by the LSSs such as power systems, multi-robot systems, communication networks, transportation networks, and supply chains. In such large-scale systems, the centralized control framework may become impossible for its implementation. The decentralized and distributed controls have emerged as the attractive control methodologies to handle the scale and interactions of large-scale complex systems. However, the interactions among different subsystems introduce many challenges in the analysis and synthesis of such systems. Therefore, it is of significance to address several fundamental problems regarding real-time analysis, estimation and control of these systems.
Vehicle crash test is the most direct and common method to assess vehicle crashworthiness. Visual inspection and obtained measurements, such as car acceleration, are used, e.g. to examine impact severity of an occupant or to assess overall car safety. However, those experiments are complex, time-consuming, and expensive. We propose a method to reproduce car kinematics during a collision using nonlinear autoregressive (NAR) model which parameters are estimated by the use of feedforward neural network. NAR model presented in this study is derived from the more general one – nonlinear autoregressive with moving average (NARMA). Suitability of autoregressive systems for data-based modeling was confirmed by application of neural networks with a NAR model to experimental data – measurements of vehicle acceleration during a crash test. This model allows us to predict the kinematic responses (acceleration, velocity, and displacement) of a given car during a collision. The major advantage of this approach is that those plots can be obtained without additional teaching of a network.
This paper is devoted to tackling the control problem for a class of discrete-time stochastic systems with randomly occurring sensor saturations. The considered sensor saturation phenomenon is assumed to occur in a random way based on the time-varying Bernoulli distribution with measurable probability in real time. The aim of the paper is to design a nonfragile gain-scheduled controller with probability-dependent gains which can be achieved by solving a convex optimization problem via semidefinite programming method. Subsequently, a new kind of probability-dependent Lyapunov functional is proposed in order to derive the controller with less conservatism. Finally, an illustrative example will demonstrate the effectiveness of our designed procedures.
Earthquakes and ambient vibrations can cause serious problems for cultural heritage objects; consequently, preserving these objects against mentioned sources of vibration has received more attention in recent years. To address this problem, in this paper, inerter is used to overcome the deficiency of a vibration isolator in the lower frequency range and performance of this passive device is evaluated experimentally. Specifically, first, the scaled model of an actual isolator and statue is presented. This structure has been designed and manufactured based on the results of a performance test, which has been performed on a famous statue of Michelangelo Buonarroti: Pieta Rondanini. In order to improve the performance of the isolator, a ball-screw type inerter has been designed and manufactured in this research. This device is introduced to the scaled structure and its effect on the dynamic behavior of the isolator is checked using a sine sweep vibration test. The experimental tests were performed on a shaking table in the horizontal direction. Then, the effectiveness of inerters on the dynamic behavior of the isolation system is demonstrated. It is shown that the isolator equipped with the manufactured inerters has better performance in the lower frequency range.
No abstract is provided for this article.
This paper is devoted to the modeling and simulation of a full-scale commercially available magnetorheological (MR) brake installed in a semi-active suspension (SAS) system. The analysis of the Bouc-Wen and Dahl mathematical models of MR damper is presented. Influence of their parameters on the response is explored. Subsequently, by using the neural networks, the parameters characterizing each model are estimated. This makes it possible to perform the comparative analysis of the suggested damper models responses with the measured experimental results. The novelty of the presented methodology is the application of artificial intelligence methods to estimate model parameters of a MR brake utilized in a SAS system. The results of this approach have a strong potential to be successfully utilized in the area of model-based control of semi-active vibration suppression systems.
Various areas of research need to be considered in order to establish a mathematical model of a vehicle crash. To enhance the modeling process, a novel ANFIS-based approach to reconstruct behavior of impacting vehicles is presented in this paper. Kinematics of center of gravity (COG) a vehicle involved in an oblique barrier collision is reproduced by application of a five-layered ANFIS structure. Then, the same ANFIS system is used to simulate a different collision type than the one which was used in the training stage. The points of interests are selected to be the locations of accelerometers mounting. The accuracy of the proposed method is evaluated by the comparative analysis with the reference measurements from full-scale vehicle collisions.
