Survey of advanced suspension developments and related optimal control applications

This paper reviews computational-intelligence-involved approaches in active vehicle suspension control systems with a focus on the problems raised in practical implementations by their nonlinear and uncertain properties. After a brief introduction on active suspension models, the paper explores the state of the art in fuzzy inference systems, neural networks, genetic algorithms, and their combination for suspension control issues. Discussions and comments are provided based on the reviewed simulation and experimental results. The paper is concluded with remarks and future directions.

State of the Art in Vehicle Active Suspension Adaptive Control Systems Based on Intelligent Methodologies

This exciting new reference text is concerned with fluid power control. It is an ideal reference for the practicing engineer and a textbook for advanced courses in fluid power control. In applications in which large forces and/or torques are required, often with a fast response time, oil-hydraulic control systems are essential. They excel in environmentally difficult applications because the drive part can be designed with no electrical components and they almost always have a more competitive power/weight ratio compared to electrically actuated systems. Fluid power systems have the capability to control several parameters, such as pressure, speed, position, and so on, to a high degree of accuracy at high power levels. In practice there are many exciting challenges facing the fluid power engineer, who now must preferably have a broad skill set.

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Fundamentals of Fluid Power Control

A hybrid intelligent control technique based on combination of neural network and fuzzy logic will be proposed for hydraulic actuated active suspension system. A half car model will be used for design of Adaptive Neuro Fuzzy Inference System (ANFIS) controller for hydraulic actuated active suspension. The nonlinear behavior of hydraulic system and uncertain parameters in active suspension has increased the difficulty of creating mathematical model for active suspension system. The performance of most of the classical controller depends on nature of mathematical model of system. Hence it is very difficult to create classical controller without mathematical model of a system. Fuzzy logic controller has ability to predict the behavior of system without the need of mathematical model of a system. In this paper, ANFIS controller proposed for active suspension due to its ability to handle actuator dynamics and parameter uncertainty in hydraulic actuator. The simulation carried out for sinusoidal road profile in order to measure the performance of proposed controller. The result of simulation indicates performance of the ANFIS controller for active suspension with actuator dynamics.

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Adaptive Neuro Fuzzy Inference System control of active suspension system with actuator dynamics

A car suspension incorporating a Lotus actuator and a TVR suspension/wheel unit is studied both experimentally and analytically. An emphasis is placed on hydraulic modelling using a series of transfer functions linking the hydraulic and suspension components. This is significantly aided by the use of a Moog 2000 programmable servo controller (PSC) to equalize the extending and retracting flow gains of the servovalve in the Lotus actuator control loop, justifying the use of combined extending and retracting transient data for parameter identification. This then allows the system equations to be developed using linear state-space theory, and a suitable form is proposed for further design studies. It is shown that the hydraulic components significantly contribute to the system dynamics and hence cannot be neglected when control schemes are formulated. In particular, the significance of hydraulic bulk modulus on dynamic performance is evaluated, and the importance of accurately determining all components of velocity-type damping is highlighted.

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A.G. Thompson

Papers

A Technical Note on the Lotus Suspension Patents