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In this paper, we focus on the active vehicular suspension controller design. A quarter-vehicle suspension system is employed in the system analysis and synthesis. Due to the difficulty and cost in the measuring of all the states, we only choose two variables to construct the feedback loop, that is, the control law is a static-output-feedback (SOF) control. However, the sensor reduction would induce challenges in the controller design. One of the main challenges is the NP-hard problem in the corresponding SOF controller design. In order to deal with this challenge, we propose a two-stage design method in which a state-feedback controller is firstly designed and then the state-feedback controller is used to decouple the nonlinear conditions. To better compensate for the varying vehicle load, a robust load-dependent control strategy is adopted. The proposed design methodology is applied to a suspension control example.

Tremendous amount of useful information can be extracted from the cylinder pressure signal for engine combustion control. However, the physical cylinder pressure sensors are undesirably expensive and their health need to be monitored for fault diagnostic purpose as well. This paper presents the results of the development of a virtual cylinder pressure sensor (VCPS) with individual variable-oriented independent estimators. Two neural network-based independent cylinder pressure related variable estimators were developed and verified at steady state. The results show that these models can predict the variables correctly compared with the extracted variables from the measured physical cylinder pressure sensor signal. Good generalization capabilities of the developed models are observed in the sense that the models work well not only for the training data set but also for the new inputs that they have never been exposed to before.