This paper presents a path following application for vehicles based on a simple linear and time invariant single- track model, which is calculated based on a constant nominal longitudinal speed. In order to consider the differences in vehicle dynamics between the real vehicle and this constant nominal model, a tube-based robust Model Predictive Control (MPC) approach is introduced. The proposed control algorithm is designed in order to guarantee proper path tracking, not only considering lateral error, but also orientation error to the target trajectory. Additionally, strict constraints are considered in the control signal and the lateral path following error. The control approach is designed in order to guarantee certain comfort standards for a wide range of velocities, with guaranteed stability.

A recently growing literature discusses the topics of direct yaw moment control based on model predictive control (MPC), and energy-efficient torque-vectoring (TV) for electric vehicles with multiple powertrains. To reduce energy consumption, the available TV studies focus on the control allocation layer, which calculates the individual wheel torque levels to generate the total reference longitudinal force and direct yaw moment, specified by higher level algorithms to provide the desired longitudinal and lateral vehicle dynamics. In fact, with a system of redundant actuators, the vehicle-level objectives can be achieved by distributing the individual control actions to minimize an optimality criterion, e.g., based on the reduction of different power loss contributions. However, preliminary simulation and experimental studies - not using MPC - show that further important energy savings are possible through the appropriate design of the reference yaw rate. This paper presents a nonlinear model predictive control (NMPC) implementation for energy-efficient TV, which is based on the concurrent optimization of the reference yaw rate and wheel torque allocation. The NMPC cost function weights are varied through a fuzzy logic algorithm to adaptively prioritize vehicle dynamics or energy efficiency, depending on the driving conditions. The results show that the adaptive NMPC configuration allows stable cornering performance with lower energy consumption than a benchmarking fuzzy logic TV controller using an energy-efficient control allocation layer.

This paper presents the design and the experimental validation of a new linear multivariable generalized predictive control for speed and rotor flux of induction motor. This control approach has been designed in the d-q rotating reference frame, and the indirect vector control has been employed. Load and flux observers, as well as the possibility of including a model-reference-adaptive-system speed estimator, have been considered in the implementation. The proposed controller not only provides enhanced dynamic performance but also guarantees compliance with physical voltage and current constraints. Hence, it ensures that the space vector pulsewidth modulation (SVPWM) always operates in the linear area and that the stator windings are not damaged due to overcurrent. Moreover, the controller includes a novel torque current tracker that allows obtaining an effective electromagnetic torque without a chattering phenomenon. Several simulation and experimental tests have been carried out, both in suitable and adverse conditions, even at zero speed zone, demonstrating that the proposed controller provides an efficient speed tracking and suggesting its use in industry.

Transport electrification is currently a priority for authorities, manufacturers, and research centers around the world. The development of electric vehicles and the improvement of their functionalities are key elements in this strategy. As a result, there is a need for further research in emission reduction, efficiency improvement, or dynamic handling approaches. In order to achieve these objectives, the development of suitable Advanced Driver‐Assistance Systems (ADAS) is required. Although traditional control techniques have been widely used for ADAS implementation, the complexity of electric multimotor powertrains makes intelligent control approaches appropriate for these cases. In this work, a novel intelligent Torque Vectoring (TV) system, composed of a neuro‐fuzzy vertical tire forces estimator and a fuzzy yaw moment controller, is proposed, which allows enhancing the dynamic behaviour of electric multimotor vehicles. The proposed approach is compared with traditional strategies using the high fidelity vehicle dynamics simulator Dynacar. Results show that the proposed intelligent Torque Vectoring system is able to increase the efficiency of the vehicle by 10%, thanks to the optimal torque distribution and the use of a neuro‐fuzzy vertical tire forces estimator which provides 3 times more accurate estimations than analytical approaches.