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Then, energy optimizing (i.e. gain scheduling of LQR controllers) rules are designed based on the vehicle lateral stability identification.
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The proposed method offers a simple and effective way to obtain the PID controller rules for parallel cascade control system which takes into account the interaction between primary and secondary control loops.
Obtaining a number of experiment data by driving a vehicle, classify the data according to the concept of data and fix the input and output variables of the cloud controller, design the control rules of the cloud controller of intelligent vehicle, and clouded and fix the parameter of cloud controller: expectation, entropy and hyper entropy.
The controller rules are selected to satisfy stability and optimality conditions for the new fuzzy time-optimal controller.
Although the controller rules are reduced, TSRFC is able to provide performance as good as former designs.
A Genetic Algorithm (GA) is employed to find optimum fuzzy controller rules, as well as adjusting the membership functions to minimize the overall damage index.
For the fuzzy model involving large plant rules, the proposed idea greatly reduces the total number of LMIs and controller rules so that TSRFC is easy to implement with simple hardware.
Fig. 3 Membership functions for Ret_adj (a) and SINR50 (b) fuzzy inputs Table 1 Fuzzy logic controller rules No Ret_adj i,j) SINR50(i) ΔHOM i,j) 1 L – Positive 2 H L Negative 3 H H Null. The proposed method has been tested using a dynamic LTE system level simulator implemented in MATLAB based on the one presented in [33].
Cultural algorithm and niche algorithm are mixed together to design Fuzzy-PID controller and optimize control rules, which can accelerate optimization speed and has a good global optimal performance.
Because of the limited resources of micro-controller, rule-based energy management strategies are still very popular for online control of plug-in hybrid electric vehicles, however, the control results may deviate from the optimal control results.
The classical negative velocity feedback controller rule is implemented to suppress the vibration response of the panel.
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