Adaptive feedforward control design for gust loads alleviation and LCO suppression

An adaptive feedforward controller is designed for gust loads alleviation and limit cycle oscillations suppression. Two sets of basis functions, based on the finite impulse response and modified finite impulse response approaches, are investigated to design the controller for gust loads alleviation. Limit cycle oscillations suppression is shown by using the modified finite impulse response controller. Worst case gust search is performed by using a nonlinear technique of model reduction to speed up the costs of calculations. Both the “one–minus–cosine” and Von Kármán continuous turbulence gusts of different intensities were generated to examine the performance of controllers. The responses of these two types of gust can be reduced effectively by finite impulse response controller in the whole process, while the modified finite impulse response controller is found to increase the loads during the initial transient response. The above two types of gust induced limit cycle oscillations were used to test the modified finite impulse response controller. Results show that it can suppress limit cycle oscillations to some exten

Adaptive feedforward control design for gust loads alleviation and LCO suppression

An adaptive feedforward controller is designed for gust loads alleviation and limit cycle oscillations suppression. Two sets of basis functions, based on the finite impulse response and modified finite impulse response approaches, are investigated to design the controller for gust loads alleviation. Limit cycle oscillations suppression is shown by using the modified finite impulse response controller. Worst case gust search is performed by using a nonlinear technique of model reduction to speed up the costs of calculations. Both the “one–minus–cosine” and Von Kármán continuous turbulence gusts of different intensities were generated to examine the performance of controllers. The responses of these two types of gust can be reduced effectively by finite impulse response controller in the whole process, while the modified finite impulse response controller is found to increase the loads during the initial transient response. The above two types of gust induced limit cycle oscillations were used to test the modified finite impulse response controller. Results show that it can suppress limit cycle oscillations to some exten

Result Integrity Check for MapReduce Computation on Hybrid Clouds

Abstract — Large scale adoption of MapReduce computations on public clouds is hindered by the lack of trust on the participat-ing virtual machines, because misbehaving worker nodes can compromise the integrity of the computation result. In this paper, we propose a novel MapReduce framework, Cross Cloud MapRe-duce (CCMR), which overlays the MapReduce computation on top of a hybrid cloud: the master that is in control of the entire computation and guarantees result integrity runs on a private and trusted cloud, while normal workers run on a public cloud. In order to achieve high accuracy, CCMR proposes a result integrity check scheme on both the map phase and the reduce phase, which combines random task replication, random task verification, and credit accumulation; and CCMR strives to reduce the overhead by reducing cross-cloud communication. We implement our ap-proach based on Apache Hadoop MapReduce and evaluate our implementation on Amazon EC2. Both theoretical and experi-mental analysis show that our approach can guarantee high result integrity in a normal cloud environment while incurring non-negligible performance overhead (e.g., when 16.7 % workers are malicious, CCMR can guarantee at least 99.52 % of accuracy with 33.6 % of overhead when replication probability is 0.3 and the credit threshold is 50)

Vehicle steering wheel angle identification research based on dynamic program method

In order to improve the safety of vehicle manipulation as well as to provide a theoretical basis for the study of vehicle steering system and intelligent parking systems, a new method of vehicle steering wheel angle identification is presented – the dynamic program method. First, three freedom degrees of vehicle model is established. Then Bellman’s principle of optimality is used for minimizing the objective function. The dynamic optimization model of the load identification that the yawing angular velocity, lateral acceleration and vehicle body roll angle identified the steering wheel angle and angle velocity. The result shows that the dynamic program method for the steering wheel angle identification problem containing the measurement noise has strong adaptability, high accuracy and good anti-jamming capability

Pavement roughness identification research in time domain based on neural network

A new simulation study method based on general regression neural network (GRNN) is proposed for identifying the pavement roughness in the time domain. First, a seven degree-of-freedoms vehicle vibration model is estbalished for the vehicle’s riding comfort analysis. The vertical acceleration and pitching angular acceleration of vehicle body centroid are calculated by simulation. The nonlinear mapping relations between the two above accelerations and pavement roughness in time domain are built by GRNN, and then the pavement roughness is identified by training the networks. Finally, the vertical acceleration and pitching angular acceleration of the vehicle body centriod are acquired by ADAMS/View virtual experiment simulation and the result are used to identify pavement roughness. In the end, the availability for identifying the pavement roughness by GRNN is confirmed