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

Abstract

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.

Multiobjective Genetic Algorithm-Based Optimization of PID Controller Parameters for Fuel Cell Voltage and Fuel Utilization

“…Meanwhile, in 1994, Srinivas and Deb [30] developed a new algorithm based on a GA called a nondominated sorting genetic algorithm (NSGA), and then in 2000, an improved version of the NSGA (called the NSGA-II) was developed by Deb et al [31]. With the merits of low computational complexities and the ability of parallel computation, it is widely used for MOPs [21,32]. Afterwards, in order to improve the distribution density of the Pareto front and avoid the problem of premature ripening, an improved NSGA-II was developed by introducing the parameter Pareto faction x, which represents the ratio of the optimal solutions to the total population (Equation (43)): where N represents the population size, and N Pareto represents the number of Pareto optimal solutions.…”

Integrated Vibration Control of In‐Wheel Motor‐Suspensions Coupling System via Dynamics Parameter Optimization

“…where n f is the number of nearby partners that is calculated by using the formula ‖AF j − AF i ‖ ≤ visual. e criteria for judging congestion are given by formula (10), which decides whether to follow formula (11).…”

“…e experimental results from a quarter car test rig which were done by Mitra et al [9] show that mass and damping coefficient are the most influential parameters for ride comfort. erefore, many scholars focus their attention on optimizing the mass, damping coefficient, and stiffness coefficient of suspension system and achieve many significant results in enhancing ride comfort by using GA [10][11][12][13][14]. Essentially, the dynamics parameter optimization of suspension system belongs to a multiobjective optimization problem (MOOP) which can be solved by using multiobjective optimization algorithm (MOOA) [15,16].…”

Fractional Order PID Control Based on Ball Screw Energy Regenerative Active Suspension

“…Narwade [11] studied the modelling and simulation of an automotive semi-active suspension system based on the PID controller, and conducted a simulation study for the application of the PID controller on automotive suspension in a more systematic way. Nagarkar [12] used PID and fuzzy control, based on a genetic algorithm, for an active non-linear 1/4 automotive suspension system to achieve multi-objective optimization of the suspension system. Although the above studies have achieved the optimization of suspension damping performance under passive suspension or classical PID control, it is still difficult to fundamentally solve the limitation of the PID controller control precision.…”