This paper deals with the adaptive sliding-mode control problem for nonlinear active suspension systems via the Takagi-Sugeno (T-S) fuzzy approach. The varying sprung and unsprung masses, the unknown actuator nonlinearity, and the suspension performances are taken into account simultaneously, and the corresponding mathematical model is established. The T-S fuzzy system is used to describe the original nonlinear system for the control-design aim via the sector nonlinearity approach. A sufficient condition is proposed for the asymptotical stability of the designing sliding motion. An adaptive sliding-mode controller is designed to guarantee the reachability of the specified switching surface. The condition can be converted to the convex optimization problems. Simulation results for a half-vehicle active suspension model are provided to demonstrate the effectiveness of the proposed control schemes.

Adaptive Sliding-Mode Control for Nonlinear Active Suspension Vehicle Systems Using T–S Fuzzy Approach

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.

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

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.

Fuzzy Sampled-Data Control for Uncertain Vehicle Suspension Systems

This brief addresses the problem of control for active vehicle suspension systems in finite frequency domain. The performance is used to measure ride comfort so that more general road disturbances can be considered. By using the generalized Kalman-Yakubovich-Popov (KYP) lemma, the norm from the disturbance to the controlled output is decreased in specific frequency band to improve the ride comfort. Compared with the entire frequency approach, the finite frequency approach suppresses the vibration more effectively for the concerned frequency range. In addition, the time-domain constraints, which represent performance requirements for vehicle suspensions, are guaranteed in the controller design. A state feedback controller is designed in the framework of linear matrix inequality (LMI) optimization. A quarter-car model with active suspension system is considered in this brief and a numerical example is employed to illustrate the effectiveness of the proposed approach.

Finite Frequency $H_{\infty }$ Control for Vehicle Active Suspension Systems

This brief investigates the problem of robust sampled-data H ∞ control for active vehicle suspension systems. By using an input delay approach, the active vehicle suspension system with sampling measurements is transformed into a continuous-time system with a delay in the state. The transformed system contains non-differentiable time-varying state delay and polytopic parameter uncertainties. A Lyapunov functional approach is employed to establish the H ∞ performance, and the controller design is cast into a convex optimization problem with linear matrix inequality (LMI) constraints. A quarter-car model is considered in this brief and the effectiveness of the proposed approach is illustrated by a realistic design example.

Robust Sampled-Data $H_{\infty}$ Control for Vehicle Active Suspension Systems

In the automotive industry, suspension systems are designed to provide desirable vehicle ride and handling properties. This paper presents the development of a robust intelligent nonlinear controller for active suspension systems based on a comprehensive and realistic nonlinear model. The inherent complex nonlinear system model's structure, and the presence of parameter uncertainties, have increased the difficulties of applying conventional linear and nonlinear control techniques. Recently, the combination of sliding mode, fuzzy logic, and neural network methodologies has emerged as a promising technique for dealing with complex uncertain systems. In this paper, a sliding mode neural network inference fuzzy logic controller is designed for automotive suspension systems in order to enhance the ride and comfort. Extensive simulations are performed on a quarter-car model, and the results show that the proposed controller outperforms existing conventional controllers with regard to body acceleration, suspension deflection, and tire deflection.

Sliding mode neural network inference fuzzy logic control for active suspension systems

We develop a comprehensive nonlinear model of a vehicle suspension system which can be used to study control algorithms for active suspension systems. This model is derived using standard kinematics and kinetics and takes into consideration several factors that are neglected in most existing models. Extensive computer simulations were performed using several road profiles and the results show that the proposed model coupled with appropriate control algorithms can result in improved vehicle performance when compared to the commonly used models. The model presented is being used to develop a nonlinear fuzzy-sliding-neural controller.

Nonlinear modeling of vehicle suspension system

This paper presents the design of a sliding mode fuzzy logic based controller for an active vehicle suspension system. The controller design is based on the Macpherson strut model and combines sliding mode techniques with fuzzy logic methodologies. The control approach has the advantage of improving the performance of active suspension systems, while reducing the control effort. Extensive simulations are performed for a quarter car model and the results show that the proposed controller provides good performance in improving and enhancing the road holding ability and ride quality.

Sliding mode-based fuzzy logic controller for a vehicle suspension system

We have developed an adaptive fuzzy logic controller (AFLC) for an active road vehicle suspension system. The main controller network integrates inference into a feedforward network. We describe the controller design and discuss simulation results. This paper demonstrate the performance and robustness of the AFLC.

Dae Sung Joo

Papers

Sliding mode neural network inference fuzzy logic control for active suspension systems

Nonlinear modeling of vehicle suspension system

Sliding mode-based fuzzy logic controller for a vehicle suspension system

Adaptive fuzzy logic based controller for an active suspension system