GA-based multi-objective optimization of active nonlinear quarter car suspension system—PID and fuzzy logic control

Background The primary function of a suspension system is to isolate the vehicle body from road irregularities thus providing the ride comfort and to support the vehicle and provide stability. The suspension system has to perform conflicting requirements; hence, a passive suspension system is replaced by the active suspension system which can supply force to the system. Active suspension supplies energy to respond dynamically and achieve relative motion between body and wheel and thus improves the performance of suspension system. Methods This study presents modelling and control optimization of a nonlinear quarter car suspension system. A mathematical model of nonlinear quarter car is developed and simulated for control and optimization in Matlab/Simulink® environment. Class C road is selected as input road condition with the vehicle traveling at 80 kmph. Active control of the suspension system is achieved using FLC and PID control actions. Instead of guessing and or trial and error method, genetic algorithm (GA)-based optimization algorithm is implemented to tune PID parameters and FLC membership functions’ range and scaling factors. The optimization function is modeled as a multi-objective problem comprising of frequency weighted RMS seat acceleration, Vibration dose value (VDV), RMS suspension space, and RMS tyre deflection. ISO 2631-1 standard is adopted to assess the ride and health criterion. Results The nonlinear quarter model along with the controller is modeled and simulated and optimized in a Matlab/Simulink environment. It is observed that GA-optimized FLC gives better control as compared to PID and passive suspension system. Further simulations are validated on suspension system with seat and human model. Parameters under observation are frequency-weighted RMS head acceleration, VDV at the head, crest factor, and amplitude ratios at the head and upper torso (AR_h and AR_ut). Simulation results are presented in time and frequency domain. Conclusion Simulation results show that GA-based FLC and PID controller gives better ride comfort and health criterion by reducing RMS head acceleration, VDV at the head, CF, and AR_h and AR_ut over passive suspension system

Petrologic characteristics and genesis of dolostone from the Campanian of the SK-I Well Core in the Songliao Basin, China

The well SK-I in the Songliao Basin is the first scientific borehole targeting the continental Cretaceous strata in China. Oval concretions, thin laminae and beds of dolostone are found intercalated within mudstone and organic-rich black shale in the Nenjiang Formation of Campanian age. Low ordered ferruginous dolomite is composed of euhedral–subhedral rhombs with cloudy nucleus and light rims formed during the diagenesis, which are typical features of replacement. The heavy carbon isotopes (δ13CPDB – 1.16–16.0) are results of both the fermentation of organic matter by microbes and degassing of carbon dioxide during the period of diagenesis, and the presence of light oxygen isotopes (δ18OPDB – 18.53∼−5.1) is a characteristic feature of fresh water influence which means the carbonate may have been altered by ground water or rainwater in the late diagenesis. Marine water incursions into the normally lacustrine basin have been proved by both the salinity of Z value and the occurrence of foraminifera in the same strata where dolomite occurs. Pyrite framboids observed by SEM are usually enclosed in the dolomite crystals or in the mudstones, supporting the sulfate reducing bacteria (SRB). The formation of both dolomite and pyrite are associated with marine water incursions, which not only supply magnesium ion for dolomite, but also result in limited carbonate precipitation in the basin. The presence of pyrite framboids indicates the development of an anoxic environment associated with salinity stratification in the lake. The dolomite in the Nenjiang Formation is the results of marine water incursions, diagenetic replacement of calcareous carbonate and sulfate reducing bacteria (SRB)

A Novel Neural Network Based Modeling for Control of NOx Emission in

Abstract. A novel neural network based modeling for non-linear model identification technique is proposed. It combines a nonlinear steady state model with a linear one, to describe the disturbance and dynamics in the coal-fired power plant. The modeling and training algorithm is used to develop a model of nitrogen oxides (NOx) emitted from the process where one-step ahead optimal prediction formula are developed. Two cases show that the resulting model provides a better prediction of NOx and fitting capabilities