Rodolfo Orjuela

Combined longitudinal and lateral control for automated vehicle guidance

This paper deals with the longitudinal and lateral control of an automotive vehicle within the framework of fully automated guidance. The automotive vehicle is a complex system characterised by highly nonlinear longitudinal and lateral coupled dynamics. Consequently, automated guidance must be simultaneously performed with longitudinal and lateral control. This work presents an automated steering strategy based on nonlinear model predictive control. A nonlinear longitudinal control strategy considering powertrain dynamics is also proposed to cope with the longitudinal speed tracking problem. Finally, a simultaneous longitudinal and lateral control strategy helps to improve the combined control performance. This whole control strategy is tested through simulations showing the effectiveness of the present approach.

State estimation for non-linear systems using a decoupled multiple model

The multiple model approach is an elegant and a powerful tool for modelling real-world complex processes. In this modelling framework, a judicious combination of a set of submodels makes it possible to describe the behaviour of a non-linear system. Two different structures of multiple models can be distinguished according to whether the submodels share a common state vector (Takagi-Sugeno multiple model) or not (decoupled multiple model). This latter structure is an interesting alternative to the popular Takagi-Sugeno multiple model because different dimensions of submodels can be considered. The decoupled multiple model is nowadays increasingly used to perform the identification and the control of non-linear systems. However, to our knowledge, the state estimation problem of non-linear systems represented by this structure is not thoroughly investigated. The present paper deals with this worthwhile problem.

Nonlinear system identification using heterogeneous multiple models

Multiple models are recognised by their abilities to accurately describe nonlinear dynamic behaviours of a wide variety of nonlinear systems with a tractable model in control engineering problems. Multiple models are built by the interpolation of a set of submodels according to a particular aggregation mechanism, with the heterogeneous multiple model being of particular interest. This multiple model is characterized by the use of heterogeneous submodels in the sense that their state spaces are not the same and consequently they can be of various dimensions. Thanks to this feature, the complexity of the submodels can be well adapted to that of the nonlinear system introducing flexibility and generality in the modelling stage. This paper deals with off-line identification of nonlinear systems based on heterogeneous multiple models. Three optimisation criteria (global, local and combined) are investigated to obtain the submodel parameters according to the expected modelling performances. Particular attention is paid to the potential problems encountered in the identification procedure with a special focus on an undesirable phenomenon called the no output tracking effect. The origin of this difficulty is explained and an effective solution is suggested to overcome this problem in the identification task. The abilities of the model are finally illustrated via relevant identification examples showing the effectiveness of the proposed methods.

Coupled longitudinal and lateral control strategy improving lateral stability for autonomous vehicle

In this paper a coupled longitudinal and lateral control strategy for an autonomous automotive vehicle is proposed. The proposed control scheme consists of a lateral guidance module based on a NonLinear Model-based Predictive Controller (NLMPC) and a cruise speed generation and regulation module. A cruise speed-profile generator allows the calculation of the cruise speed which preserves the lateral stability of the vehicle during lateral maneuvers. The calculation of the cruise speed profile is done considering road geometry as well as lateral dynamics criteria. The handling of both longitudinal and lateral dynamics in addition to the calculation of the limit cruise speed profile by considering lateral stability criteria illustrate the interest of the proposed approach. The developed control architecture is tested through simulation and shows good performance for guidance at high cruise speeds.

Proportional-Integral observer design for nonlinear uncertain systems modelled by a multiple model approach

In this paper, a decoupled multiple model approach is used in order to cope with the state estimation of uncertain nonlinear systems. The proposed decoupled multiple model provides flexibility in the modelling stage because the dimension of the submodels can be different and this constitutes the main difference with respect to the classically used multiple model scheme. The state estimation is performed using a proportional integral observer (PIO) which is well known for its robustness properties with respect to uncertainties and perturbations. The Lyapunov second method is employed in order to provide sufficient existence conditions of the observer, in LMI terms, and to compute the optimal gains of the PIO. The effectiveness of the proposed methodology is illustrated by a simulation example.

Estimating the state and the unknown inputs of nonlinear systems using a multiple model approach

This paper addresses both state and unknown input estimation problem of nonlinear systems modelled with the help of a particular class of multiple models, known as decoupled multiple model. The simultaneous estimation of the state and the unknown inputs is achieved using a proportional-integral observer that is well known by its robustness properties. The proposed observer allows the use of submodels with different dimensions and this fact offers potential applications in the multiple model framework. The LMI framework is used in order to provide sufficient conditions for ensuring exponential convergence of the estimation error and robust Hinfin performances with respect to perturbations.

Reference generation and control strategy for automated vehicle guidance

This paper describes a vehicle guidance strategy with a focus placed on the reference generation and the control levels. Further to a perception step, performed through the fusion of a Geographic Information System (GIS) and a vision system, the reference generation leads to the computation of a constrained smooth trajectory and a smooth speed profile integrating safety and comfort criteria. The obtained reference set is then used by a longitudinal and NLMPC-based lateral controller providing the steering angle and traction torque. The complete system performance are presented through simulation results based on real-time measurements.

A DECOUPLED MULTIPLE MODEL APPROACH FOR STATE ESTIMATION OF NONLINEAR SYSTEMS SUBJECT TO DELAYED MEASUREMENTS

The state estimation of nonlinear systems with delayed measurements is investigated in this paper. The proposed approach is based on the representation of the nonlinear system by a decoupled multiple model that, to our knowledge, has not been investigated extensively. This multiple model approach offers an interesting alternative to the classically used multiple model known as Takagi-Sugeno multiple model. Indeed, in contrast to this last, the decoupled multiple model makes it possible to introduce a state vector with a different dimension for each submodel. Sufficient conditions for ensuring the exponential convergence of the estimation error are provided in terms of LMIs.

Fault diagnosis for nonlinear systems represented by heterogeneous multiple models

This paper proposes two observer-based FDI strategies for nonlinear systems represented by a particular class of multiple model using heterogeneous submodels. The structure of this interesting multiple model is firstly presented in order to design two kinds of state observers. The first observer, known as proportional observer (PO), is an extension of the classic Luenberger observer, in this way, it can be used to obtain an estimation of the system state. The second proposed observer, known as proportional-integral observer (PIO), makes it possible the simultaneous state and unknown input (e.g. a fault) estimation of the system under investigation. The convergence towards zero of the estimation errors provided by these observers is investigated with the help of the Lyapunov method. The P observer as well as the PI observer are employed in a FDI strategy in order to accomplish the detection, the localisation and eventually the estimation of sensor faults acting on the system. These two strategies are finally validated in simulation by considering a simplified model of a bioreactor.

Observer design for nonlinear systems described by multiple models

This paper addresses both analysis and observer design for nonlinear systems modelled by decoupled multiple models. With respect to classic used multiple models, the decoupled multiple model is characterized by heterogeneous submodels in the sense that their state spaces may be of various dimensions. Thanks to this fact, flexibility and generality is introduced in the modelling stage. The main contribution of the paper is the development of new sufficient conditions on LMI form for ensuring the exponential convergence towards zero of the estimation error in the continuous and in the discrete-time. The new proposed conditions enable to obtain a better decay rate with respect to the existing conditions. The validity of the proposed methodology and its application to sensor faults detection and isolation is illustrated by an academic example.