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.
The use of extra sensors in parallel robots can provide an increase in control performance, making it possible to fully exploit the potential of these mechanisms. In this paper, a comprehensive redundant dynamic modelling procedure for the six-degree-of-freedom Gough platform is presented. The proposed methodology makes it possible to define the model in terms of all sensorized joint variables in order to implement redundant information-based control, and an example, the Extended Computed Torque Control (Extended CTC) approach, is developed. This, applied to parallel robots, ensures better dynamic performance than the traditional CTC approach. In order to validate dynamic modelling, a two-step procedure is used in this paper. First, the redundant dynamic model is validated by comparing its dynamic performance with the previous research in the field. Second, an exhaustive study is carried out that demonstrates the advantages of the redundant dynamic model when used in the Extended CTC approach.
Knowing accurate model of a system is always beneficial to design a robust and safe control while allowing reduction of sensors-related cost as the system outputs are predictable using the model. In this context, this paper addresses the kinematical and dynamical model identification of the multipurpose rehabilitation robot, Universal Haptic Pantograph (UHP), and present experimental
Abstract-Guaranteeing simplicity and safety is a real challenge of Advanced Driver Assistance Systems (ADAS), being these aspects necessary for the development of decision and control stages in highly automated vehicles. Considering that a human-centered design is generally pursued, exploring comfort boundaries in passenger vehicles has a significant importance. This work aims to implement a simple Model Predictive Control (MPC) for longitudinal maneuvers, considering a bare speed planner based on the curvature of a predefined geometrical path. The speed profiles are constrained with a maximum value at any time, in such way that total accelerations are lower than specified constraint limits. A double proportional with curvature bias control was employed as a simple algorithm for lateral maneuvers. The tests were performed within a realistic simulation environment with a virtual vehicle model based on a multi-body formulation. The results of this investigation permits to determine the capabilities of simplified control algorithms in real scenarios, and comprehend how to improve them to be more efficient.
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.
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