LPV/H ∞ suspension robust control adaption of the dynamical lateral load transfers based on a differential algebraic estimation approach

version soumise de 8 pagesInternational audienceThis paper is concerned with a new global chassis strategy combining the LPV/H ∞ control framework and the differential algebraic estimation approach. The main objective is to enhance the vehicle performances by adapting its control to the dynamical lateral load transfers using a very efficient algebraic dynamical behaviour estimation strategy. Indeed, the lateral load transfers influence considerably the vehicle dynamical behaviour, stability and safety especially in dangerous driving situations. It is important to emphasize that the dynamical load transfers are different from the static ones generated mainly by the bank of the road. The computation of these dynamics must be based on the effective lateral acceleration and roll behaviour of the car. Such effective data cannot be given directly by the hardware sensors (which give correlated measures). The information on the real dynamical lateral load transfers is very important to ensure a good adaptation of the vehicle control and performances to the considered driving situation. A very interesting differential algebraic estimation approach allows to provide the effective needed measures for the control strategy using only sensors available on most of commercial cars. It is based on the differential flatness property of nonlinear systems in an algebraic context. Then, thanks to this estimation approach, the dynamical lateral load transfers can be calculated and used to adapt the vertical performances of the vehicle using the LPV/H ∞ for suspension systems control. Simulations performed on non linear vehicle models with data collected on a real car are used to validate the proposed estimation and control approaches. Results show the efficiency of this vehicle control strategy

On the global stability of departure time user equilibrium: A Lyapunov approach

In (Jin, 2018), a new day-to-day dynamical system was proposed for drivers' departure time choice at a single bottleneck. Based on three behavioral principles, the nonlocal departure and arrival times choice problems were converted to the local scheduling payoff choice problem, whose day-to-day dynamics are described by the Lighthill-Whitham-Richards (LWR) model on an imaginary road of increasing scheduling payoff. Thus the departure time user equilibrium (DTUE), the arrival time user equilibrium (ATUE), and the scheduling payoff user equilibrium (SPUE) are uniquely determined by the stationary state of the LWR model, which was shown to be locally, asymptotically stable with analysis of the discrete approximation of the LWR model and through a numerical example. In this study attempt to analytically prove the global stability of the SPUE, ATUE, and DTUE. We first generalize the conceptual models for arrival time and scheduling payoff choices developed in (Jin, 2018) for a single bottleneck with a generalized scheduling cost function, which includes the cost of the free-flow travel time. Then we present the LWR model for the day-to-day dynamics for the scheduling payoff choice as well as the SPUE. We further formulate a new optimization problem for the SPUE and demonstrate its equivalent to the optimization problem for the ATUE in (Iryo and Yoshii, 2007). Finally we show that the objective functions in the two optimization formulations are equal and can be used as the potential function for the LWR model and prove that the stationary state of the LWR model, and therefore, the SPUE, DTUE, and ATUE, are globally, asymptotically stable, by using Lyapunov's second method. Such a globally stable behavioral model can provide more efficient departure time and route choice guidance for human drivers and connected and autonomous vehicles in more complicated networks.Comment: 17 pages, 3 figure

A New Approach to Speeding Up Topic Modeling

Latent Dirichlet allocation (LDA) is a widely-used probabilistic topic modeling paradigm, and recently finds many applications in computer vision and computational biology. In this paper, we propose a fast and accurate batch algorithm, active belief propagation (ABP), for training LDA. Usually batch LDA algorithms require repeated scanning of the entire corpus and searching the complete topic space. To process massive corpora having a large number of topics, the training iteration of batch LDA algorithms is often inefficient and time-consuming. To accelerate the training speed, ABP actively scans the subset of corpus and searches the subset of topic space for topic modeling, therefore saves enormous training time in each iteration. To ensure accuracy, ABP selects only those documents and topics that contribute to the largest residuals within the residual belief propagation (RBP) framework. On four real-world corpora, ABP performs around $10$ to $100$ times faster than state-of-the-art batch LDA algorithms with a comparable topic modeling accuracy.Comment: 14 pages, 12 figure

Scheduling Dimension Reduction of LPV Models -- A Deep Neural Network Approach

In this paper, the existing Scheduling Dimension Reduction (SDR) methods for Linear Parameter-Varying (LPV) models are reviewed and a Deep Neural Network (DNN) approach is developed that achieves higher model accuracy under scheduling dimension reduction. The proposed DNN method and existing SDR methods are compared on a two-link robotic manipulator, both in terms of model accuracy and performance of controllers synthesized with the reduced models. The methods compared include SDR for state-space models using Principal Component Analysis (PCA), Kernel PCA (KPCA) and Autoencoders (AE). On the robotic manipulator example, the DNN method achieves improved representation of the matrix variations of the original LPV model in terms of the Frobenius norm compared to the current methods. Moreover, when the resulting model is used to accommodate synthesis, improved closed-loop performance is obtained compared to the current methods.Comment: Accepted to American Control Conference (ACC) 2020, Denve

A Novel Control Engineering Approach to Designing and Optimizing Adaptive Sequential Behavioral Interventions

abstract: Control engineering offers a systematic and efficient approach to optimizing the effectiveness of individually tailored treatment and prevention policies, also known as adaptive or ``just-in-time'' behavioral interventions. These types of interventions represent promising strategies for addressing many significant public health concerns. This dissertation explores the development of decision algorithms for adaptive sequential behavioral interventions using dynamical systems modeling, control engineering principles and formal optimization methods. A novel gestational weight gain (GWG) intervention involving multiple intervention components and featuring a pre-defined, clinically relevant set of sequence rules serves as an excellent example of a sequential behavioral intervention; it is examined in detail in this research.   A comprehensive dynamical systems model for the GWG behavioral interventions is developed, which demonstrates how to integrate a mechanistic energy balance model with dynamical formulations of behavioral models, such as the Theory of Planned Behavior and self-regulation. Self-regulation is further improved with different advanced controller formulations. These model-based controller approaches enable the user to have significant flexibility in describing a participant's self-regulatory behavior through the tuning of controller adjustable parameters. The dynamic simulation model demonstrates proof of concept for how self-regulation and adaptive interventions influence GWG, how intra-individual and inter-individual variability play a critical role in determining intervention outcomes, and the evaluation of decision rules.   Furthermore, a novel intervention decision paradigm using Hybrid Model Predictive Control framework is developed to generate sequential decision policies in the closed-loop. Clinical considerations are systematically taken into account through a user-specified dosage sequence table corresponding to the sequence rules, constraints enforcing the adjustment of one input at a time, and a switching time strategy accounting for the difference in frequency between intervention decision points and sampling intervals. Simulation studies illustrate the potential usefulness of the intervention framework. The final part of the dissertation presents a model scheduling strategy relying on gain-scheduling to address nonlinearities in the model, and a cascade filter design for dual-rate control system is introduced to address scenarios with variable sampling rates. These extensions are important for addressing real-life scenarios in the GWG intervention.Dissertation/ThesisDoctoral Dissertation Chemical Engineering 201

Simple Tracking Output Feedback H ∞ Control for Switched Linear Systems: Lateral Vehicle Control Application

International audienceIn this paper, the problem of the switched H ∞ tracking output feedback control problem is studied. The control design problem is addressed in the context of discrete-time switched linear systems. Then, the design of continuous-time case becomes trivial. Linear Matrix Inequality (LMI) and Linear Matrix Equality (LME) representations are used to express all sufficient conditions to solve the control problem. Some transformations leading to sufficient conditions for the control problem are also used. All conditions are established for any switching using a switched Lyapunov function and a common Lyapunov function. The effectiveness of the proposed control approach is shown through a steering vehicle control implementation. Interesting simulation results are obtained using real data acquired by an instrumented car

Study and comparison of non linear and LPV control approaches for vehicle stability control

International audienceThis paper proposes a study and a comparison between two efficient and novel vehicle control dynamics strategies, namely, the non linear Flatness control strategy and the LPV/Hinf control strategy. The first one concerns a controller based on the differential algebraic flatness of non linear systems and an algebraic non linear estimation applied to commercial vehicles. The second one is a LPV/Hinf (Linear Varying Parameter with the Hinf norm ) control using a stability monitoring system to achieve the vehicle dynamics control objective. These two strategies use Active Steering and Electro- Mechanical Braking actuators and aim at improving the vehicle stability and steerability by designing a multivariable controller that acts simultaneously on the lateral and longitudinal dynam- ics of the car. Simulations are performed on a complex nonlinear full vehicle model, the same driving scenario is applied for the two control strategies. The model parameters are those of a Renault Mégane Coupé (see table.I), obtained by identification with real data. Promising simulations results are obtained. Comparison between the two proposed strategies and the uncontrolled vehicle show the reliability and the robustness of the proposed solutions, even if one is governed within the linear control framework while the other one is a non linear control approach