Model Predictive Control Allocation of Systems with Different Dynamics

International audienceSeveral systems are integrated in passenger cars. Some of them are just redundant systems due to safety requirements. Others, are completely different and can interact with each other as long as they are operating inside the same vehicle. Control allocation methods have been successfully implemented in advanced aircrafts to avoid conflicts, especially in the context of redundant systems. In this paper, we will rather focus on coordinating non-redundant advanced chassis systems with different dynamics. This difference in dynamics can be especially problematic when systems exhibit different communication delays. Model Predictive Control Allocation (MPCA) methods are therefore investigated in order to activate the right system at the right moment. Results show that particularly when the most effective system is saturated, another system with a different time delay can be activated few steps before saturation to instantly take over the maneuver. With good knowledge of actuator dynamics and higher computation power, MPCA methods are able to solve complex problems in severe situations

MPC-Based Haptic Shared Steering System: A Driver Modeling Approach for Symbiotic Driving

Advanced Driver Assistance Systems (ADAS) aim to increase safety and reduce mental workload. However, the gap in the understanding of the closed-loop driver-vehicle interaction often leads to reduced user acceptance. In this study, an optimal torque control law is calculated online in the Model Predictive Control (MPC) framework to guarantee continuous guidance during the steering task. The research contribution is in the integration of an extensive prediction model covering cognitive behaviour, neuromuscular dynamics, and the vehicle- steering dynamics, within the MPC-based haptic controller to enhance collaboration. The driver model is composed of a preview cognitive strategy based on a Linear-Quadratic-Gaussian, sensory organs, and neuromuscular dynamics, including muscle co-activation and reflex action. Moreover, an adaptive cost-function algorithm enables dynamic allocation of the control authority. Experiments were performed in a fixed-base driving simulator at Toyota Motor Europe involving 19 participants to evaluate the proposed controller with two different cost functions against a commercial Lane Keeping Assist (LKA) system as an industry benchmark. The results demonstrate the proposed controller fosters symbiotic driving and reduces driver-vehicle conflicts with respect to a state-of-the-art commercial system, both subjectively and objectively, while still improving path-tracking performance. Summarising, this study tackles the need to blend human and ADAS control, demonstrating the validity of the proposed strategy

MPC-Based Haptic Shared Steering System: A Driver Modeling Approach for Symbiotic Driving

Advanced Driver Assistance Systems (ADAS) aim to increase safety and reduce mental workload. However, the gap in the understanding of the closed-loop driver-vehicle interaction often leads to reduced user acceptance. In this study, an optimal torque control law is calculated online in the Model Predictive Control (MPC) framework to guarantee continuous guidance during the steering task. The research contribution is in the integration of an extensive prediction model covering cognitive behaviour, neuromuscular dynamics, and the vehicle- steering dynamics, within the MPC-based haptic controller to enhance collaboration. The driver model is composed of a preview cognitive strategy based on a Linear-Quadratic-Gaussian, sensory organs, and neuromuscular dynamics, including muscle co-activation and reflex action. Moreover, an adaptive cost-function algorithm enables dynamic allocation of the control authority. Experiments were performed in a fixed-base driving simulator at Toyota Motor Europe involving 19 participants to evaluate the proposed controller with two different cost functions against a commercial Lane Keeping Assist (LKA) system as an industry benchmark. The results demonstrate the proposed controller fosters symbiotic driving and reduces driver-vehicle conflicts with respect to a state-of-the-art commercial system, both subjectively and objectively, while still improving path-tracking performance. Summarising, this study tackles the need to blend human and ADAS control, demonstrating the validity of the proposed strategy

A Real-time Nonlinear Model Predictive Controller for Yaw Motion Optimization of Distributed Drive Electric Vehicles

This paper proposes a real-time nonlinear model predictive control (NMPC) strategy for direct yaw moment control (DYC) of distributed drive electric vehicles (DDEVs). The NMPC strategy is based on a control-oriented model built by integrating a single track vehicle model with the Magic Formula (MF) tire model. To mitigate the NMPC computational cost, the continuation/generalized minimal residual (C/GMRES) algorithm is employed and modified for real-time optimization. Since the traditional C/GMRES algorithm cannot directly solve the inequality constraint problem, the external penalty method is introduced to transform inequality constraints into an equivalently unconstrained optimization problem. Based on the Pontryagin’s minimum principle (PMP), the existence and uniqueness for solution of the proposed C/GMRES algorithm are proven. Additionally, to achieve fast initialization in C/GMRES algorithm, the varying predictive duration is adopted so that the analytic expressions of optimally initial solutions in C/GMRES algorithm can be derived and gained. A Karush-Kuhn-Tucker (KKT) condition based control allocation method distributes the desired traction and yaw moment among four independent motors. Numerical simulations are carried out by combining CarSim and Matlab/Simulink to evaluate the effectiveness of the proposed strategy. Results demonstrate that the real-time NMPC strategy can achieve superior vehicle stability performance, guarantee the given safety constraints, and significantly reduce the computational efforts

When Backpressure Meets Predictive Scheduling

Motivated by the increasing popularity of learning and predicting human user behavior in communication and computing systems, in this paper, we investigate the fundamental benefit of predictive scheduling, i.e., predicting and pre-serving arrivals, in controlled queueing systems. Based on a lookahead window prediction model, we first establish a novel equivalence between the predictive queueing system with a \emph{fully-efficient} scheduling scheme and an equivalent queueing system without prediction. This connection allows us to analytically demonstrate that predictive scheduling necessarily improves system delay performance and can drive it to zero with increasing prediction power. We then propose the \textsf{Predictive Backpressure (PBP)} algorithm for achieving optimal utility performance in such predictive systems. \textsf{PBP} efficiently incorporates prediction into stochastic system control and avoids the great complication due to the exponential state space growth in the prediction window size. We show that \textsf{PBP} can achieve a utility performance that is within $O(\epsilon)$ of the optimal, for any $\epsilon>0$, while guaranteeing that the system delay distribution is a \emph{shifted-to-the-left} version of that under the original Backpressure algorithm. Hence, the average packet delay under \textsf{PBP} is strictly better than that under Backpressure, and vanishes with increasing prediction window size. This implies that the resulting utility-delay tradeoff with predictive scheduling beats the known optimal $[O(\epsilon), O(\log(1/\epsilon))]$ tradeoff for systems without prediction

Non-centralized Control for Flow-based Distribution Networks: A Game-theoretical Insight

This paper solves a data-driven control problem for a flow-based distribution network with two objectives: a resource allocation and a fair distribution of costs. These objectives represent both cooperation and competition directions. It is proposed a solution that combines either a centralized or distributed cooperative game approach using the Shapley value to determine a proper partitioning of the system and a fair communication cost distribution. On the other hand, a decentralized noncooperative game approach computing the Nash equilibrium is used to achieve the control objective of the resource allocation under a non-complete information topology. Furthermore, an invariant-set property is presented and the closed-loop system stability is analyzed for the non cooperative game approach. Another contribution regarding the cooperative game approach is an alternative way to compute the Shapley value for the proposed specific characteristic function. Unlike the classical cooperative-games approach, which has a limited application due to the combinatorial explosion issues, the alternative method allows calculating the Shapley value in polynomial time and hence can be applied to large-scale problems.Generalitat de Catalunya FI 2014Ministerio de Ciencia y Educación DPI2016-76493-C3-3-RMinisterio de Ciencia y Educación DPI2008-05818Proyecto europeo FP7-ICT DYMASO