In this paper, a novel adaptive cruise control (ACC) algorithm based on model predictive control (MPC) and active disturbance rejection control (ADRC) is proposed. This paper uses an MPC algorithm for the upper controller of the ACC system. Through comprehensive considerations, the upper controller will output desired acceleration to the lower controller. In addition, to increase the accuracy of the predictive model in the MPC controller and to address fluctuations in the vehicle’s acceleration, an MPC aided by predictive estimation of acceleration is proposed. Due to the uncertainties of vehicle parameters and the road environment, it is difficult to establish an accurate vehicle dynamic model for the lower-level controller to control the throttle and brake actuators. Therefore, feed-forward control based on a vehicle dynamic model (VDM) and compensatory control based on ADRC is used to enhance the control precision and to suppress the influence of internal or external disturbance. Finally, the proposed optimal design of the ACC system was validated in road tests. The results show that ACC with APE can accurately control the tracking of the host vehicle with less acceleration fluctuation than that of the traditional ACC controller. Moreover, when the mass of the vehicle and the slope of the road is changed, the ACC–APE–ADRC controller is still able to control the vehicle to quickly and accurately track the desired acceleration.
An improved multiple linear regression method has been proposed to predict the content of alpha-helix and beta-strand of a globular protein based on its primary sequence. The amino acid composition and the auto-correlation functions based on the hydrophobicity profile of the primary sequence have been taken into account in the algorithm. The resubstitution test shows that the average absolute errors are 0.077 and 0.073 with the standard deviations 0.059 and 0.057 for the prediction of the content of alpha-helix and beta-strand, respectively. A stringent cross-validation test, i.e., the jackknife test, shows that the average absolute errors are 0.087 and 0.081 with the standard deviations 0.067 and 0.065 for the prediction of the content of alpha-helix and beta-strand, respectively. Both tests indicate the self-consistency and the extrapolating effectiveness of the new algorithm. This greatly improves on previous results (Eisenhaber,F., Imperiale,F., Argos,P. and Frommel,C., 1996, Proteins, 25, 157-168). Compared with other methods currently available, our method has the merits of simplicity and ease-of-use as well as a higher prediction accuracy. The only input of the method is the primary sequence of the query protein to be predicted. The program is available on request via e-mail: ctzhang@tju.edu.cn.
Large-scale process control problems usually have multiple, conflicting objectives, making it difficult for control strategies based on single-objective, on-line optimization to provide the desired performance. The present work considers a goal-programming approach that treats multiple objectives explicitly. It is intended to reduce tuning complexity, yet provide flexibility in defining performance objectives. An example application to the Tennessee Eastman Challenge Process is presented.
The key technology to realize intelligent unmanned coal mining is the strapdown inertial navigation system (SINS); however, the gradual increase of cumulative error during the working process needs to be solved. On the basis of an SINS/odometer (OD)-integrated navigation system, this paper adds magnetometer (MAG)-aided positioning and proposes an SINS/OD/MAG-integrated shearer navigation system. The velocity observation equation is obtained from the speed constraints during shearer movement, and the yaw angle observation equation is obtained from the magnetometer output. The position information of the SINS output is calibrated using these two observations. In order to improve the fault tolerance of the integrated navigation system, an adaptive federated Kalman filter is established to complete the data fusion of the SINS. Experimental results show that the positioning accuracy of the SINS/OD/MAG-integrated navigation system is 75.64% and 74.01% higher in the east and north directions, respectively, than the SINS/OD-integrated navigation system.
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