Event-triggering $${H}_{\infty }$$-based observer combined with NN for simultaneous estimation of vehicle sideslip and roll angles with network-induced delays

Abstract

Nowadays, vehicles are being fitted with systems that improve their maneuverability, stability, and comfort in order to reduce the number of accidents. Improving these aspects is of particular interest thanks to the incorporation of autonomous vehicles onto the roads. The knowledge of vehicle sideslip and roll angles, which are among the main causes of road accidents, is necessary for a proper design of a lateral stability and roll-over controller system. The problem is that these two variables cannot be measured directly through sensors installed in current series production vehicles due to their high costs. For this reason, their estimation is fundamental. In addition, there is a time delay in the relaying of information between the different vehicle systems, such as, sensors, actuators and controllers, among others. This paper presents the design of an $${H}_{\infty }$$ H ∞ -based observer that simultaneously estimates both the sideslip angle and the roll angle of a vehicle for a networked control system, with networked transmission delay based on an event-triggered communication scheme combined with neural networks (NN). To deal with the vehicle nonlinearities, NN and linear-parameter-varying techniques are considered alongside uncertainties in parameters. Both simulation and experimental results are carried out to prove the performance of observer design.

Simultaneous Estimation of Vehicle Sideslip and Roll Angles Using an Integral-Based Event-Triggered H$_{\infty }$ Observer Considering Intravehicle Communications

“…Based on an augmented Lyapunov-Krasovskii functional, sufficient conditions on the design of the integral-based event-triggered parameters and the observer to achieve global asymptotic stability are established in terms of linear matrix inequalities (LMIs). A comparison of the proposed observer with results previously presented by the authors [37], where a traditional event-triggering mechanism is used, shows that the number of measurements that need to be transmitted decreases while the estimates for both angles are very similar. The simulation and experimental results demonstrate the effectiveness of the proposed observer.…”

“…Table II presents the parameters used in the design of the proposed observer. The LMIs (19) and (20) in Theorem 1 were solved offline by using the LMI toolbox in MATLAB to obtain the observer gains, L i , and the triggering parameters, Ω i , for the three cases: Simulation results obtained from the proposed integralbased event-triggered H ∞ observer were compared with those obtained by considering a traditional event-triggered mechanism as described in [37]. For the traditional event-triggered mechanism, the observer gains and triggering parameters are listed in Appendix B.…”

“…We adopt a model of the vehicle's lateral dynamics that includes lateral, roll, and yaw motions around the center of mass. The equivalent first-order state-space model of the vehicle is given as [37], [41]…”

“…≥ 0, and N = 3. We refer to [37], [42] for further details about the polytope. The model can then be redefined as…”

LMI-Based H∞ Controller of Vehicle Roll Stability Control Systems with Input and Output Delays

“…In [ 22 ], the transfer alignment time delay of the strapdown Inertial Navigation Systems (INS) was filtered using the H method. In [ 23 ], an H observer combined with NN for the estimation of the sideslip and roll angle with network-induced delays was designed. In other studies [ 24 ], a delay controller was designed for active vibration suppression systems.…”