In this paper, the modular adaptive robust control (MARC) technique is applied to design the force loop controller of an electro-hydraulic active suspension system. A key advantage of this modular design approach lies in the fact that the adaptation algorithm can be designed for explicit estimation convergence. The effect of parameter adaptation on force tracking performance can be compensated and thus it is possible to guaranteed certain control performance. Experimental results from a quarter-car active suspension test rig show that when realistic external disturbances and measurement noises exist, the modular design achieves a better estimate than the non-modular ARC design. The improved estimation was found to result in control signals with slightly lower magnitude while maintaining similar tracking performance.

This paper presents experimental results of a force tracking controller for a quarter-car active suspension system. In a previous publication (Chantranuwathana and Peng 1999), an active suspension architecture was presented. The overall active suspension system was decomposed into two loops. At the main loop, the desired force signal is calculated by using a standard LQ design process. The Adaptive Robust Control design technique is then used to design the force controller such that the desired force signal is achieved in a robust manner when actuator or other plant uncertainties are present. Experimental results of the proposed ARC force-tracking algorithm are reported in this paper. It was found that force-tracking of up to 5Hz can be reliably achieved.

This paper presents adaptive robust controllers for force tracking application in a quarter-car active suspension system. In previous publications (Chantranuwathana and Peng 1999a, 1999b), an active suspension architecture was presented. The overall active suspension system was decomposed into two loops. At the main-loop, the desired force signal is calculated while the sub-loop force tracking controller tries to keep the actual force close to this desired force. An Adaptive Robust Control (Yao and Tomizuka 1997) design technique was used to achieve good force tracking performance in a robust manner under plant uncertainties. It was found that force-tracking of up to 5Hz can be reliably achieved. It is, however, found to be unreliable in experiments, especially when high frequency disturbances are present. In this paper, we will show that unmodeled dynamics and especially, the delay (first order lag) in implementing the control signal is a main cause of the problem. With this insight, three controller modifications are proposed to reduce the effect of the unmodeled dynamics, 1) include the actuator dynamics in the ARC design, 2) cancellation of the actuator dynamics and 3) online-adaptation of an ARC parameter. A number of simulation results will be presented to show the effect of these remedies. The last two modifications were found to be promising for actual implementations.

SUMMARYIn this paper, a modular modification of the adaptive robust control (ARC) technique is presented. The modular design has all of the original ARC properties with an estimation-based update law instead of a Lyapunov-based update law. In this design, the controller is divided into two modules: a control module and an identification module. A key new idea is to set a priori bounds on the time derivatives of the estimates to be maintained by the update law. As a result, their effects on the system tracking accuracy can be dominated by the control law. A modification is proposed for the standard gradient and least-square update laws to guarantee the bounds. This modification also makes the controller robust against the generalized (unparameterized) uncertainties considered in the ARC formulation while allowing asymptotic output tracking without the generalized uncertainties. Both the ARC and the modular ARC techniques are applied to a force control problem for an active suspension system. Simulations and experimental results are provided to show that the update law of the modular design is less sensitive to measurement noise which results in smaller force tracking error and smaller control gain.

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