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Recently, fractional-order proportional-integral-derivative (FOPID) controllers are demonstrated as a general form of the classical proportional-integral-derivative (PID) using fractional calculus. In FOPID controller, the orders of the derivative and integral portions are not integers which offer more flexibility in succeeding control objectives. This paper proposes a multi-objective genetic algorithm (MOGA) to optimize the FOPID controller gains to enhance the ride comfort of heavy vehicles. The usage of magnetorheological (MR) damper in seat suspension system provides considerable benefits in this area. The proposed semi-active control algorithm consists of a system controller that determines the desired damping force using a FOPID controller tuned using a MOGA, and a continuous state damper controller that calculates the input voltage to the damper coil. A mathematical model of a six degrees-offreedom seat suspension system incorporating human body model using an MR damper is derived and simulated using Matlab/Simulink software. The proposed semi-active MR seat suspension is compared to the classical PID, optimum PID tuned using genetic algorithm (GA) and passive seat suspension systems for predetermined chassis displacement. System performance criteria are examined in both time and frequency domains, in order to verify the success of the proposed FOPID algorithm. The simulation results prove that the proposed FOPID controller of MR seat suspension offers a superior performance of the ride comfort over the integer controllers.

Neural networks are highly useful for the modelling and control of magnetorheological (MR) dampers. A damper controller based on a recurrent neural network (RNN) of the inverse dynamics of an MR damper potentially offers significant advantages over conventional controllers in terms of reliability and cost through the minimal use of sensors. This paper introduces a neural-network-based MR damper controller for use in conjunction with the system controller of a semi-active vehicle suspension. A mathematical model of a semi-active quarter-vehicle suspension using an MR damper is derived. Control performance criteria are evaluated in the time and frequency domains in order to quantify the suspension effectiveness under bump and random road disturbance. Studies using the modified Bouc—Wen model for the MR damper as well as an actual damper fitted in a hardware-in-the-loop simulation (HILS) both showed that the inverse RNN damper controller potentially offers a significantly superior ride comfort and vehicle stability over a conventional MR damper controller based on continuous-state control. The neural network controller produces a smoother and lower input voltage to the MR damper coil, ensuring extended damper life and lower power requirement respectively. Further studies performed using an RNN model of the forward dynamics of the MR damper showed that it is a reliable substitute for HILS for validating multi-damper control applications.

Seat suspension is an important system to the ride comfort experience of a commercial vehicle's driver and passengers. The usage of magnetorheological (MR) dampers in seat suspension systems has been shown to offer a momentous enhancement regarding to the ride comfort. In the majority, research work on seat MR dampers has been emphasized on the control implementation but most of them were not quite appropriate for the semi-active and nonlinear hysteretic nature of the MR damper. This paper introduces a deeply investigation into the application of a semi-active MR damper for a truck seat suspension, enabling more efficient control algorithm. The proposed control system consists of a system controller that calculates the desired damper force using a fuzzy logic control (FLC) algorithm, and a signum function damper controller that provides an approximation of the command voltage required to track the desired damping force. A mathematical model and the equations of motion of a two degree-of-freedom semi-active seat suspension with an MR damper are derived and simulated using Matlab/Simulink software. The proposed semi-active MR seat suspension is compared to passive and uncontrolled seat suspensions for prescribed base displacements. These inputs are representative of the vibration of the body mass of a passive quarter-vehicle suspension under bump and random-profile road excitation. Seat travel distance and driver body acceleration are assessed as system performance criteria through bump and random road excitations, in order to quantify the efficiency of the proposed semi-active control technique. The simulated results indicate that the proposed FLC of the semi-active MR seat suspension provides a significant enhancement in ride comfort.