An Efficient Model-Free Setting for Longitudinal and Lateral Vehicle Control: Validation Through the Interconnected Pro-SiVIC/RTMaps Prototyping Platform

Abstract

Abstract-In this paper, the problem of tracking desired longitudinal and lateral motions for a vehicle is addressed. Let us point out that a "good" modeling is often quite difficult or even impossible to obtain. It is due for example to parametric uncertainties, for the vehicle mass, inertia or for the interaction forces between the wheels and the road pavement. To overcome this type of difficulties, we consider a model-free control approach leading to "intelligent" controllers. The longitudinal and the lateral motions, on one hand, and the driving/braking torques and the steering wheel angle, on the other hand, are respectively the output and the input variables. An important part of this work is dedicated to present simulation results with actual data. Actual data, used in Matlab as reference trajectories, have been previously recorded with an instrumented Peugeot 406 experimental car. The simulation results show the efficiency of our approach. Some comparisons with a nonlinear flatnessbased control in one hand, and with a classical PID control in another hand confirm this analysis. Other virtual data have been generated through the interconnected platform SiVIC/RTMaps, which is a virtual simulation platform for prototyping and validation of advanced driving assistance systems.

Combination of Data-Driven Active Disturbance Rejection and Takagi-Sugeno Fuzzy Control with Experimental Validation on Tower Crane Systems

“…As in the previous case, the PDTSFC2 component is built around a TISO-FC respecting the representation given in Figure 2. Defining again the parameters Bê and B ∆ê of the input membership functions, using the same inference engine of the fuzzy component that makes use of the SUM and PROD operators [56], the rule base is the same, it consists of nine rules presented in Table 1 with γ i in (12) used to adjust the overshoot of the CS with the ADRC-PDTSFC2 technique for TCS control [56].…”

“…ADRC [1][2][3][4] is one of the most popular data-driven techniques along with Model-Free Adaptive Control [5][6][7][8][9], Model-Free Control [10][11][12][13][14] or Virtual Reference Feedback Tuning [15][16][17][18]. The main advantage that made data-driven techniques so popular is that they use only the input/output data from the process.…”

Performances comparison between ultra-local model control, integral sliding mode control and PID control for a coupled tanks system

“…Combining the equations (25) and (26) yields: e (t) + K Dė (t) + K P e (t) + K I e (t) = 0 (27) whereF (t) andα (t) do not appear anymore. the gain tuning becomes therefore quite straightforward.…”

Intelligent Proportional–Integral–Derivative Control-Based Modulating Functions for Laser Beam Pointing and Stabilization

“…In addition to being very simple to implement, i-PID controller is robust with respect to system's uncertainties, modeling errors and disturbances (see, e.g., [19]). MFC in general and i-PID in particular have been considered in several applications such as: shape memory alloys [20], DC/DC converters [21], active magnetic bearing [22], two-dimensional planar manipulator [23], agricultural greenhouse [24], quadrotor vehicle and aerospace [25]- [28], automotive engine [29], mechanical system [30] and references therein. The system uncertainties and the modeling errors that represent the overall time-varying dynamics in MFC uses a derivative operation on the output system.…”