Fadly Jashi Darsivan

Ride Comfort Performance of a Vehicle Using Active Suspension System with Active Disturbance Rejection Control

Active disturbance rejection control (ADRC) for a quarter-car active suspension system is proposed. The performance of ADRC is compared with benchmark (LQR control) from current literature. A simulation experiment is performed to investigate the performance of ADRC in comparison with passive system and the LQR control, as well as to test the ability of ADRC to cope with varying process. Results show that ADRC performs better than the benchmark control and is able to cope with varying process commonly seen in active suspension system.

Active engine mounting control algorithm using neural network

This paper proposes the application of neural network as a controller to isolate engine vibration in an active engine mounting system. It has been shown that the NARMA-L2 neurocontroller has the ability to reject disturbances from a plant. The disturbance is assumed to be both impulse and sinusoidal disturbances that are induced by the engine. The performance of the neural network controller is compared with conventional PD and PID controllers tuned using Ziegler-Nichols. From the result simulated the neural network controller has shown better ability to isolate the engine vibration than the conventional controllers.

Active engine mounting controller using extended minimal resource allocating networks

The application of the Extended Minimal Resource Allocation Network (EMRAN) in the field of active vibration isolation for an automotive engine system is presented. As the source of disturbance the engine is mounted on a two passive mounts in parallel with an active force actuator. EMRAN is implemented to control the force actuator and to isolate this disturbance. The characteristic of EMRAN and the criteria for growing and pruning of hidden layers are presented. EMRANs capability is compared with a PID controller and NARMA-L2 neural controller and shows superior performance in rejecting sinusoidal disturbances and achieving the chassiss vibration isolation.

Optimisation and control of semi-active suspension using genetic algorithm for off-road full vehicle

The main role of a suspension system of a vehicle is to prevent the road excitations from being transmitted to the passengers. In this study, we develop and obtain a strategy to optimise the main design parameters of a semi-active suspension for a two-axle full off-road vehicle. The objective is to minimise the maximum bouncing acceleration of the sprung mass. Owing to the importance of ride comfort for off-road vehicles, minimising the settling time and peak-to-peak of the vertical, pitch and roll acceleration would lead to better ride comfort. In solving this problem, the genetic algorithms have consistently found near-optimal solutions within specified parameters ranges for several independent runs.

Natural logarithm sliding mode control (ln-SMC) using EMRAN for active engine mounting system

Robustness of the controller becomes major important factor in active engine mounting system to ensure the vibration of the engine can be attenuated in entire range of operating speed. As robustness is the main advantage of sliding mode control, it has been proposed for vibration control applications. However, most of the proposed sliding mode control are using linear sliding surface. In addition, there is no schematic method to determine the optimum sliding function. In this paper, natural logarithm-based sliding function is introduced. By its nature, natural logarithm function is nonlinear function. Since it consists of one parameter only, which is related to maximum allowable vibration level, it is more easily to be determined by the designer. Moreover, the advantage of fast on-line training using Extended Minimum Resource Allocating Network (EMRAN) algorithm, made it possible to design feedback control law without having to obtain the precise system model which is normally becomes major difficulties of sliding mode approach.

Terminal sliding mode control for active engine mounting system

This paper discusses terminal sliding mode control for active engine mounting (AEM) system to isolate vehicle engine body vibration. The engine vibration may occur due to road irregularities, at low frequency, and also reciprocating mechanism of the piston in the engine, at high frequency about 20 – 40 Hz. Active engine mounting system is designed to deal with isolation of vibration in high level frequency. A number of controllers for AEM system have been proposed. One of them is sliding mode control. However, there is no systematic method to determine sliding surface for conventional sliding mode technique to assure the system will be asymptotically stable. The main advantage of the proposed controller compared to the conventional (linear) sliding mode control is that the sliding motion to the origin (stable condition) can be reached in finite time by introducing the nonlinear sliding surface based on the concept of terminal attractors. The result shows that terminal sliding mode control not only able to attenuate the vibration but also robust to the different type of disturbance and parametric uncertainties such as mass, stiffness, and damping coefficient.

Ride comfort performance of a non-linear full-car using active suspension system with activedisturbance rejection control and input decoupling transformation

In this paper, active disturbance rejection control (ADRC) and a control method combining ADRC with input decoupling transformation (ADRC-IDT) are proposed to improve ride comfort of a non-linear full-car with active suspension system. Simulation of the model in frequency domain as well as time domain with three types of road profile - speed hump, double bumps and random excitation, as the disturbance to the system is performed to evaluate the performance of the proposed ADRC-IDT in comparison with ADRC and the passive system. Through experimental simulation studies, the ability of the proposed controllers to cope with varying process is investigated. Results show that ADRC-IDT is able to produce comparable performance to a typical ADRC control structure with fewer control parameters.

Modeling and simulation for heavy-duty mecanum wheelplatform using model predictive control

This paper presents a study on a control system for a heavy-duty four Mecanum wheel platform. A mathematical model for the system is synthesized for the purpose of examining system behavior, including Mecanum wheel kinematics, AC servo motor, gearbox, and heavy duty load. The system is tested for velocity control, using model predictive control (MPC), and compared with a traditional PID setup. The parameters for the controllers are determined by manual tuning. Model predictive control was found to be more effective with reference to a linear velocity

Evaluation of Elastomeric Models for Engine Mountings Applications

The requirements of modern vehicle engine vibration isolation systems have been upgraded from the traditional passive mode to semi-active mode or active mode. With the relative advantages of semi-active mode over active mode and passive mode, the dynamic analysis of the behaviours of elastomers when used in the semi-active mode requires more attention. This research compares the use of a three-parameter classic model and a four-parameter model on the isolation performance of elastomers. Using an analytically developed mathematical model, the effect of modifying the classic three-parameter model will be shown in the simulation results of the transmissibility properties of elastomeric mounts. The results show higher vibration isolation effects using the four-parameter model. The four-parameter elastomeric mounts have higher damping characteristics which is needed for elastomeric mounts at resonance frequency. These results will be incorporated into the study of the effect of varying the dynamic performance of elastomers in future research.

Artificial neural network based hysteresis compensation for piezoelectric tube scanner in atomic force microscopy

Piezoelectric tube scanner is a major component that used in nanoscale imaging tools such as atomic force microscopy (AFM). This is because it can provide precise nanoscale positioning. However the precision is limited by vibration and some nonlinear drawbacks represented by creep and hysteresis. Hysteresis problem appears when positioning is needed at wide range. In this paper, a feed forward multi-layer neural network (MLNN) is trained to shape a proper control signal based on reference input and actual output signals. The experimental results show that the developed neural network scheme improves the performance of the system by significantly minimizing the effect of hysteresis.