Design of Automotive Control Systems Robust to Hardware Imprecision

Abstract

The hardware used for software implementation on a physical system introduces uncertainty into the controller. If neglected during design, this uncertainty can lead to poor controller performance, resulting in significant design and verification iterations. In this work, the effect of sampling time, quantization, and fixed-point computation are directly accounted for in the control design. Sampling time is compensated for by a discrete-time controller. A generic methodology is developed for modeling the worst-case scenario effect of quantization and fixed-point computation on the control commands. The cold-start emission control problem is used as a case study, and a discrete-time sliding surface controller is developed. Verification is performed to ensure the estimated worst-case scenario uncertainty bounds are accurate. The bounds are incorporated into a modified version of the control laws. During simulation the modified controller demonstrates significant reduction in tracking error in the presence of hardware imprecisions.

Adaptive Discrete Second-Order Sliding Mode Control With Application to Nonlinear Automotive Systems

“…in Eq. (12), and assuming a small enough sampling time (T ), which means all terms that contain T 2 can be neglected, Eq. (12) can be re-arranged as follows:…”

“…The main reason to add the switching function to the calculated equivalent control input of the SMC is reducing the chattering [6]. As it has been shown in [10,12,13], the addition of the switching function with a fix or variable gain enhances the robustness of the controller against uncertainties, which could overpower the chattering issues. The gain of the switching function (sgn(ξ i (k −1))), which depends on the DSMC tuning parameters (λ, β) and g (Eq.…”

Bridging the gap between designed and implemented controllers via adaptive robust discrete sliding mode control

“…For the first group, results in [3] showed that the robustness of the implemented controller against ADC uncertainties can be improved by incorporating the maximum ADC uncertainty bounds into the controller equations. Converting the controller design from a single-input single-output (SISO) to a multi-input multi-output (MIMO) Figure 1: Background of some previous studies [3,4,5,6,7,8,9,10,11,12,13,14,15,16,17] on SMC design against ADC and modeling uncertainties.…”

“…Quasi and baseline discrete sliding mode controllers were introduced and studied under implementation imprecisions in [7] and [8], respectively. The robustness of the DSMC design was enhanced by incorporating the maximum ADC uncertainty bounds on control inputs in [9]. The results showed that the proposed DSMC with maximum ADC uncertainty bounds can lead to better tracking performance in comparison with the baseline SMC/DSMC.…”

“…The incorporation of maximum ADC uncertainty bounds requires an ideal symbolic model of the controller which prevents the online uncertainty bounds calculation. Moreover, the results in [3] and [9] showed that the calculated maximum ADC uncertainty bounds, which are based on a worst case scenario, lead to conservative controller design and large control actions, which are not desired. To this end, in our previous work [10], we developed an online technique to predict and propagate sampling and quantization imprecisions on control signals.…”