M.M. ElMadany

Bio: M.M. ElMadany is an academic researcher from King Saud University. The author has contributed to research in topics: Suspension (vehicle) & Active suspension. The author has an hindex of 15, co-authored 29 publications receiving 516 citations.

Linear quadratic gaussian control of a quarter-car suspension

Abstract: This paper presents a method for designing linear multivariable controllers in the frequency-domain for an intelligent controlled suspension system for a quarter-car model. The design methodology uses singular value inequalities and optimal control theory. The vehicle system is augmented with additional dynamics in the form of an integrator to affect the loop shapes of the system. The measurements are assumed to be obtained in a noisy state, and the optimal control gain and the Kalman filter gain are derived using system dynamics and noise statistics. A combination of singular value analysis, eigenvalue analysis, time response, and power spectral densities of random response is used to describe the performance of the active suspension systems.

Quadratic Synthesis of Active Controls for a Quarter-Car Model:

Abstract: The design of active vehicle suspensions with integral constraints to control the response of a vehicle traversing a road is considered. The problem is initially formulated in the linear quadratic regulator (LQR) framework with full-state feedback. Alternate formulations based on optimal output feedback (OOFB) and the minimum norm criterion (MNC) approach in the absence of complete state information are then presented. A general expression for the required optimal value of the control force based on easily measurable feedback quantities is developed. To reduce the effect of the body jerk, the high rate of change of actuator control force is penalized by adding a roll-off prefilter to the control. Results based on LQR, OOFB, and MNC are compared, and it is shown that the OOFB and MNC give performances as effective as the full-state feedback without the need to measure the tire deflection. The effectiveness of the roll-off prefilter in reducing the body jerk is demonstrated in an example problem.

Optimal Linear Active Suspensions with Multivariable Integral Control

Abstract: SUMMARY In this paper, an optimal suspension system is derived for a quarter-car model using multivariable integral control. The suspension system features two parts. The first part is an integral control acting on suspension deflection to ensure zero steady-sate offset due to body and maneuvering forces as well as road inputs. The second is a proportional control operating on the vehicle system states for vibration control and performance improvement. The optimal ride performance of the active suspensions based on linear full-state feedback control laws with and without integral control together with the performance of passive suspensions are compared.

Reliable Fuzzy Control for Active Suspension Systems With Actuator Delay and Fault

Abstract: This paper is focused on reliable fuzzy H∞ controller design for active suspension systems with actuator delay and fault. The Takagi-Sugeno (T-S) fuzzy model approach is adapted in this study with the consideration of the sprung and the unsprung mass variation, the actuator delay and fault, and other suspension performances. By the utilization of the parallel-distributed compensation scheme, a reliable fuzzy H∞ performance analysis criterion is derived for the proposed T-S fuzzy model. Then, a reliable fuzzy H∞ controller is designed such that the resulting T-S fuzzy system is reliable in the sense that it is asymptotically stable and has the prescribed H∞ performance under given constraints. The existence condition of the reliable fuzzy H∞ controller is obtained in terms of linear matrix inequalities (LMIs) Finally, a quarter- vehicle suspension model is used to demonstrate the effectiveness and potential of the proposed design techniques.

Fuzzy Sampled-Data Control for Uncertain Vehicle Suspension Systems

Abstract: This paper investigates the problem of sampled-data $H_{\infty}$ control of uncertain active suspension systems via fuzzy control approach. Our work focuses on designing state-feedback and output-feedback sampled-data controllers to guarantee the resulting closed-loop dynamical systems to be asymptotically stable and satisfy $H_{\infty}$ disturbance attenuation level and suspension performance constraints. Using Takagi-Sugeno (T-S) fuzzy model control method, T-S fuzzy models are established for uncertain vehicle active suspension systems considering the desired suspension performances. Based on Lyapunov stability theory, the existence conditions of state-feedback and output-feedback sampled-data controllers are obtained by solving an optimization problem. Simulation results for active vehicle suspension systems with uncertainty are provided to demonstrate the effectiveness of the proposed method.