Topic
Vehicle dynamics
About: Vehicle dynamics is a research topic. Over the lifetime, 12909 publications have been published within this topic receiving 204091 citations.
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23 May 1990
TL;DR: In this paper, it is shown that the performance of such a robust automatic steering system can be considerably improved by the addition of a gyro measuring the yaw rate and feeding it back into the controller.
Abstract: Robust control problems in automatic steering are due to the wide range of velocity, mass and road conditions under which such vehicles operate. In earlier design studies and road tests for a bus it has been shown that it is possible to design a fixed gain controller such that the automatic steering operates satisfactorily over the entire range of parameters. In the present paper it is shown that the performance of such a robust automatic steering system can be considerably improved by the addition of a gyro measuring the yaw rate and feeding it back into the controller.
199 citations
TL;DR: A nonlinear model predictive control (NMPC) is used to design a high-level controller for a fixed-wing unmanned aerial vehicle (UAV) and the control objective is extended to track adjoined multiple line segments.
Abstract: In this paper, a nonlinear model predictive control (NMPC) is used to design a high-level controller for a fixed-wing unmanned aerial vehicle (UAV). Given the kinematic model of the UAV dynamics, which is used as a model of the UAV with low-level autopilot avionics, the control objective of the NMPC is determined to track a desired line. After the error dynamics are derived, the problem of tracking a desired line is transformed into a problem of regulating the error from the desired line. A stability analysis follows to provide the conditions that can assure the closed-loop stability of the designed high-level NMPC. Furthermore, the control objective is extended to track adjoined multiple line segments. The simulation results demonstrate that the UAV controlled by the NMPC converged rapidly with a small overshoot. The performance of the NMPC was also verified through realistic ?hardware in the loop simulation.?
199 citations
TL;DR: A new concept of formation learning control is introduced to the field of formation control of multiple autonomous underwater vehicles (AUVs), which specifies a joint objective of distributed formation tracking control and learning/identification of nonlinear uncertain AUV dynamics.
Abstract: In this paper, a new concept of formation learning control is introduced to the field of formation control of multiple autonomous underwater vehicles (AUVs), which specifies a joint objective of distributed formation tracking control and learning/identification of nonlinear uncertain AUV dynamics. A novel two-layer distributed formation learning control scheme is proposed, which consists of an upper-layer distributed adaptive observer and a lower-layer decentralized deterministic learning controller. This new formation learning control scheme advances existing techniques in three important ways: 1) the multi-AUV system under consideration has heterogeneous nonlinear uncertain dynamics; 2) the formation learning control protocol can be designed and implemented by each local AUV agent in a fully distributed fashion without using any global information; and 3) in addition to the formation control performance, the distributed control protocol is also capable of accurately identifying the AUVs’ heterogeneous nonlinear uncertain dynamics and utilizing experiences to improve formation control performance. Extensive simulations have been conducted to demonstrate the effectiveness of the proposed results.
199 citations
TL;DR: In this article, the normal pressure is calculated by satisfying contact conditions at the geometrical point of contact, which may be carried out on-line in MultiBody Systems (MBS) computer codes.
Abstract: The presented model assumes semi-elliptical normal pressure distribution in the direction of rolling. The contact area is found by virtual penetration of wheel and rail. The normal pressure is calculated by satisfying contact conditions at the geometrical point of contact. The calculation is non-iterative, fast and completely reliable. It may be carried out on-line in MultiBody Systems (MBS) computer codes. The tests using the programme CONTACT by Kalker and experience from application in MBS codes show that the model is suitable for technical applications. The creep forces have been calculated with the FASTSIM algorithm, adapted for a non-elliptical contact area. Some applications in rail vehicle dynamics and wear simulation have been outlined.
197 citations
TL;DR: The stability and stabilization criteria are derived by taking into consideration an asynchronous difference between the normalized membership function of the T–S fuzzy DPS and that of the controller, which can stabilize states of the UMV.
Abstract: This paper is concerned with a Takagi–Sugeno (T–S) fuzzy dynamic positioning controller design for an unmanned marine vehicle (UMV) in network environments. Network-based T–S fuzzy dynamic positioning system (DPS) models for the UMV are first established. Then, stability and stabilization criteria are derived by taking into consideration an asynchronous difference between the normalized membership function of the T–S fuzzy DPS and that of the controller. The proposed stabilization criteria can stabilize states of the UMV. The dynamic positioning performance analysis verifies the effectiveness of the networked modeling and the controller design.
197 citations