Michael Glenn Fodor

Bio: Michael Glenn Fodor is an academic researcher from Ford Motor Company. The author has contributed to research in topics: Torque & Traction control system. The author has an hindex of 15, co-authored 48 publications receiving 879 citations.

An MPC/hybrid system approach to traction control

Abstract: This paper describes a hybrid model and a model predictive control (MPC) strategy for solving a traction control problem. The problem is tackled in a systematic way from modeling to control synthesis and implementation. The model is described first in the Hybrid Systems Description Language to obtain a mixed-logical dynamical (MLD) hybrid model of the open-loop system. For the resulting MLD model, we design a receding horizon finite-time optimal controller. The resulting optimal controller is converted to its equivalent piecewise affine form by employing multiparametric programming techniques, and finally experimentally tested on a car prototype. Experiments show that good and robust performance is achieved in a limited development time by avoiding the design of ad hoc supervisory and logical constructs usually required by controllers developed according to standard techniques.

A Hybrid Approach to Traction Control

Abstract: In this paper we describe a hybrid model and an optimization-based control strategy for solving a traction control problem currently under investigation at Ford Research Laboratories. We show through simulations on a model and a realistic set of parameters that good and robust performance is achieved. Furthermore, the resulting optimal controller is a piecewise linear function of the measurements that can be implemented on low cost control hardware.

Clutch torque trajectory correction to provide torque hole filling during a ratio upshift

Abstract: A control system and method for controlling a multiple gear ratio automatic transmission in a powertrain for an automatic transmission having pressure activated friction torque elements to effect gear ratio upshifts. The friction torque elements are synchronously engaged and released during a torque phase of an upshift event as torque from a torque source is increased while allowing the off-going friction elements to slip, followed by an inertia phase during which torque from a torque source is modulated. A perceptible transmission output torque reduction during an upshift is avoided. Measured torque values are used during a torque phase of the upshift to correct an estimated oncoming friction element target torque so that transient torque disturbances at an oncoming clutch are avoided and torque transients at the output shaft are reduced.

Traction control system and method for a vehicle

Abstract: In one example, A method for controlling a powertrain of a vehicle with wheels, the vehicle having a pedal actuated by a driver, is described. The method may include generating powertrain torque transmitted to the wheels in a first relation to actuation of the pedal by the driver during a first condition where said transmitted torque causes said wheels to slip relative to a surface; overriding said driver actuated powertrain torque to control said slip; and during a second condition after said first condition where said vehicle is moving less than a threshold, generating powertrain torque transmitted to the wheels in a second relation to actuation of the pedal by the driver, where for a given pedal position, less powertrain torque is transmitted with said second relation compared to said first relation.

Traction control system and method

Abstract: In one example, a first vehicle traveling on a road is provided. The vehicle comprises a communication device coupled in the first vehicle configured to receive information transmitted by a second vehicle traveling on the road, said information identifying road surface conditions experience by said second vehicle; and a controller configured to adjust a vehicle operating parameter of the first vehicle in response to receiving said transmitted information from said second vehicle.

Predictive Active Steering Control for Autonomous Vehicle Systems

Abstract: In this paper, a model predictive control (MPC) approach for controlling an active front steering system in an autonomous vehicle is presented. At each time step, a trajectory is assumed to be known over a finite horizon, and an MPC controller computes the front steering angle in order to follow the trajectory on slippery roads at the highest possible entry speed. We present two approaches with different computational complexities. In the first approach, we formulate the MPC problem by using a nonlinear vehicle model. The second approach is based on successive online linearization of the vehicle model. Discussions on computational complexity and performance of the two schemes are presented. The effectiveness of the proposed MPC formulation is demonstrated by simulation and experimental tests up to 21 m/s on icy roads

Predictive Control for Linear and Hybrid Systems

Abstract: Model Predictive Control (MPC), the dominant advanced control approach in industry over the past twenty-five years, is presented comprehensively in this unique book. With a simple, unified approach, and with attention to real-time implementation, it covers predictive control theory including the stability, feasibility, and robustness of MPC controllers. The theory of explicit MPC, where the nonlinear optimal feedback controller can be calculated efficiently, is presented in the context of linear systems with linear constraints, switched linear systems, and, more generally, linear hybrid systems. Drawing upon years of practical experience and using numerous examples and illustrative applications, the authors discuss the techniques required to design predictive control laws, including algorithms for polyhedral manipulations, mathematical and multiparametric programming and how to validate the theoretical properties and to implement predictive control policies. The most important algorithms feature in an accompanying free online MATLAB toolbox, which allows easy access to sample solutions. Predictive Control for Linear and Hybrid Systems is an ideal reference for graduate, postgraduate and advanced control practitioners interested in theory and/or implementation aspects of predictive control.

Model predictive control: Review of the three decades of development

Abstract: Three decades have passed since milestone publications by several industrialists spawned a flurry of research and industrial / commercial activities on model predictive control (MPC). This article reviews major developments and achievements during the three decades and attempts to put a perspective on them. The first decade is characterized by the fast-growing industrial adoption of the technology, primarily in the refining and petrochemical sectors, which sparked much interest and also confusion among the academicians. The second decade saw a number of significant advances in understanding the MPC from a control theoretician’s viewpoint, which included state-space interpretations / formulations and stability proofs. These theoretical triumphs contributed to the makings of the second generation of commercial software, which was significantly enhanced in generality and rigor. The third decade’s main focus has been on the development of “fast MPC,” a term chosen to collectively describe the various efforts to bring orders-of-magnitude improvement in the efficiency of the on-line computation so that the technology can be applied to systems requiring very fast sampling rates. Throughout the three decades of the development, theory and practice supported each other quite effectively, a primary reason for the fast and steady rise of the technology.

Constrained Optimal Control of Linear and Hybrid Systems

Abstract: Multiparametric Programming- Multiparametric Programming: a Geometric Approach- Optimal Control of Linear Systems- Constrained Finite Time Optimal Control- Constrained Infinite Time Optimal Control- Receding Horizon Control- Constrained Robust Optimal Control- Reducing On-line Complexity- Optimal Control of Hybrid Systems- Hybrid Systems- Constrained Optimal Control for Hybrid Systems- Applications- Ball and Plate- Traction Control

A survey on explicit model predictive control

Abstract: Explicit model predictive control (MPC) addresses the problem of removing one of the main drawbacks of MPC, namely the need to solve a mathematical program on line to compute the control action. This computation prevents the application of MPC in several contexts, either because the computer technology needed to solve the optimization problem within the sampling time is too expensive or simply infeasible, or because the computer code implementing the numerical solver causes software certification concerns,especially in safety critical applications.