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CHAPTER 2
LITERATURE REVIEW
2.1 ACTIVE AND PASSIVE SUSPENSION SYSTEM
Rosheila Darus et al (2009) has explained the active and passive system for the full car and quarter car model to investigate the performance using LQR controller also to improve the comfort of the suspension. LQR technique gives higher amplitude on body displacement for active system but has faster settling time than passive system and also the wheel deflection amplitude is lower for active system compared to passive system. It was concluded that, it cannot perform in rough road disturbances especially for full car model but gives better ride comfort on quarter car active suspension system.

Abhijeet et al (2013) has developed a quarter car model to study the behaviour of passive and active suspension system. The response of passive system for step and two bump input was noted and also for the active system. It was concluded that using vibration absorber and additional dampers have improved the ride comfort slightly. The controlling element of the active suspension system is generally based on an actuator and the main problem faced was the power consumption of the actuator.
Phalke et al (2016) has explained the performance comparison of passive and semi active suspension system of a quarter car model for different velocities for half sine wave bump and then for different road profiles using MATLAB SIMULINK. A PID controller is developed which gives optimal and robust system by withstanding different road conditions and vehicle speeds to increase ride comfort. It is concluded that the settling time of semi active suspension system is very much reduced for all the bump inputs than the passive system using PID controller.

2.2 CONTROLLER
Ervin Alvarez Sanchez et al (2013) has designed a sliding mode controller that allows avoid the induced road variations over the car body. The road perturbations profiles are differentiated with different amplitudes and frequencies. A bump and speed reducer are used as an input to check the suspension behaviour. It was concluded that the simulation results on MATLAB SIMULINK with the Runge Kutta numerical method for the numerical values of the quarter car suspension. The sprung mass estimator reaches the sprung mass value of 208 kg in a small time of about 0.01 seconds which allows using it in a new robust control scheme.

Ali J. et al (2008) has explained the controlling a quarter car hydraulic active suspension system using SMC techniques. The SMCs are able to reject the matched disturbance completely and reduce the effect of unmatched disturbance of the system. It was concluded that the displacement of car body is between -8 and +8 cm. The chattering related to the discontinuous controller and the nature of the systems and that can be reduced by using higher order SMC.

Chong Chee Soon et al (2017) has evaluate the performance improvement of the sliding mode controller integrated with PID controller. The control scheme is established from the derived dynamic equation which stability is proven through Lyapunov theorem. The PSO self-tuning algorithm that implemented in the PID sliding surface. It was concluded that the integration of PID, PSO and ZN gives the better result when compared to conventional ZN approach.

Rui Bai et al (2017) has designed the sliding mode controller to control the vibration of the active suspension system. A sinusoidal roadway is introduced as the input disturbance signal in the suspension system and by using the sliding mode control the vibration was suppressed within a small range with an active control. It was concluded that the implementation of SMC gives the better result on active control than the passive control.

2.3 AIR SPRING SUSPENSION SYSTEM
Abishek et al (2018) has developed a cost-effective quarter car test rig model that can generate the road profile like step, ramp, sinusoidal input. Based on the input signal given, the suspension behaviour can be studied and the corresponding sprung mass acceleration are noted with the help of accelerometer. Further, various controller like LQR, PID and fuzzy logic can be implemented to improve the vibration in the sprung mass. It was concluded that the future work to be do is to generate the ISO 8608 road profile on the test rig for different frequencies and compare it with the simulation result.
Gao Zepeng et al (2017) has explained the performance of the air spring for the electric vehicle with electric controlled air suspension and the relevant factors of air spring are analysed and the characteristics of the gasbag are simulated and verified in AMESim. The model of the electric vehicle body is analysed according to the law of dynamics and the fuzzy control theory is used to set up the electric vehicle body model in Simulink. In the case of unbalanced load, the effectiveness of the fuzzy controller is usued to simulate the phenomenon of “overshoot” in the system. It was concluded that the vibration is gradually slow down with fuzzy control and there is no obvious sense of shock and ride comfort improved.

Haider J. Abid et al (2015) has investigated the GENSIS air spring suspension system equivalence to a passive suspension system. The SIMULINK simulation together with the OptiY optimisation is used to obtain the air spring suspension model equivalent to passive suspension system, where the car body response difference from both systems with the same road profile inputs is used as the objective function for OptiY program. The parameters of air spring system such as initial pressure, volume of bag, length of surge pipe and volume of reservoir are obtained from optimization. The simulation results show that the air spring suspension equivalent system can produce responses very close to the passive suspension system.

Sathishkumar et al (2014) is dealing with modeling and evaluation of suspension system with a pneumatic actuator controlled by PID controller. A non-linear mathematical model of the dynamic suspension system with two degrees of freedom is developed. The controller is designed by setting proper gain values obtained by comparing three tuning methods namely Zeigler Nicolas and Optimal control. The time response of the air suspension system is contrasted with the passive suspension system due to the road disturbance modelled as a single bump input. It was concluded that for given road input the peak vehicle body displacement in optimal is lower than passive suspension system. The suspension travel of optimal PID system is better than passive when considering the peak overshoot, system response and settling time.

Fanbiao Bao et al (2011) explained the calculation and design of the vertical stiffness and frequency on the basis of mathematical model of air spring suspension system. This paper had SIMULINK computed and analyzed the response character according to road excitation spectrum as input. The simulation results were in accord with the driving condition well. The model can be used for suspension control system exploiter and ECU with vehicle model.

Shaohua wang et al (2010) explained the multi body simulation software to model a bus air suspension system. The parameters like vertical acceleration, working space, dynamic tire load was selected as a performance index to analyze the matching of suspension system. On the basis of comparison of simulation data, Suitable damping was selected after matching with air spring suspension in corresponding condition and the performance of air spring suspension system was improved.

M.Presthus (2002) explained the GENSYS model parameters without any experiment. The vertical parameters and horizontal parameters were obtained. Different parameters like thermodynamics and fluid dynamics constitutions were considered. The stiffness valves obtained from the simulation are close to the stiffness of the air spring.