Reza Sharif Razavian

Design and Evaluation of a Real-time Fuel-optimal Control System for Series Hybrid Electric Vehicles

Razavian, R. S., Taghavipour, A., Azad, N. L., & McPhee, J. (2012). Design and evaluation of a real-time fuel-optimal control system for series hybrid electric vehicles. International Journal of Electric and Hybrid Vehicles, 4(3), 260. Final version published by Inderscience Publishers, and available at: https://doi.org/10.1504/IJEHV.2012.050501

On real-time optimal control of a series Hybrid Electric Vehicle with an ultra-capacitor

To design a supervisory plan for a Hybrid Electric Vehicle (HEV), different methods are presented in the literature. In many of these controllers, there are a few parameters that must be tuned according to future driving conditions. To address this issue, a novel feedback controller is introduced in this paper that serves as the supervisory plan of a series HEV, and does not require the exact knowledge of the drive cycle. To find this controller, first a mathematical model of the hybrid drivetrain is developed, then Pontryagins Minimum Principle is applied assuming that the drive cycle is known in advance. Based on the mechanism of the optimal control, a set of mathematical rules is extracted, and an optimal feedback controller is designed. It is also shown that a priori knowledge of the future drive cycle is not required; it is possible to tune the controller parameters, knowing only the cruise time and the available negative energy during braking.

A Physics-Based Musculoskeletal Driver Model to Study Steering Tasks

Realistic driver models can play an important role in developing new driver assistance technologies. A realistic driver model can reduce the time-consuming trial-and-error process of designing and testing products, and thereby reduce the vehicles development time and cost. A realistic model should provide both driver path planning and arm motions that are physiologically possible. The interaction forces between a drivers hand and steering wheel can influence control performance and steering feel. The aim of this work is to develop a comprehensive yet practical model of the driver and vehicle. Consequently, a neuromuscular driver model in conjunction with a high-fidelity vehicle model is developed to learn and understand more about the drivers performance and preferences, and their effect on vehicle control and stability. This driver model can provide insights into task performance and energy consumption of the driver, including fatigue and cocontraction dynamics of a steering task. In addition, this driver model in conjunction with a high-fidelity steering model can be used to develop new steering technologies such as electric power steering.

A battery hardware-in-the-loop setup for concurrent design and evaluation of real-time optimal HEV power management controllers

Razavian, R. S., Azad, N. L., & McPhee, J. (2013). A battery hardware-in-the-loop setup for concurrent design and evaluation of real-time optimal HEV power management controllers. International Journal of Electric and Hybrid Vehicles, 5(3), 177. Final version published by Inderscience Publishers, and available at: https://doi.org/10.1504/IJEHV.2013.057604

A model-based approach to predict muscle synergies using optimization: application to feedback control.

This paper presents a new model-based method to define muscle synergies. Unlike the conventional factorization approach, which extracts synergies from electromyographic data, the proposed method employs a biomechanical model and formally defines the synergies as the solution of an optimal control problem. As a result, the number of required synergies is directly related to the dimensions of the operational space. The estimated synergies are posture-dependent, which correlate well with the results of standard factorization methods. Two examples are used to showcase this method: a two-dimensional forearm model, and a three-dimensional driver arm model. It has been shown here that the synergies need to be task-specific (i.e., they are defined for the specific operational spaces: the elbow angle and the steering wheel angle in the two systems). This functional definition of synergies results in a low-dimensional control space, in which every force in the operational space is accurately created by a unique combination of synergies. As such, there is no need for extra criteria (e.g., minimizing effort) in the process of motion control. This approach is motivated by the need for fast and bio-plausible feedback control of musculoskeletal systems, and can have important implications in engineering, motor control, and biomechanics.

Predictive Simulation of Reaching Moving Targets Using Nonlinear Model Predictive Control

This article investigates the application of optimal feedback control to trajectory planning in voluntary human arm movements. A nonlinear model predictive controller (NMPC) with a finite prediction horizon was used as the optimal feedback controller to predict the hand trajectory planning and execution of planar reaching tasks. The NMPC is completely predictive, and motion tracking or electromyography data are not required to obtain the limb trajectories. To present this concept, a two degree of freedom musculoskeletal planar arm model actuated by three pairs of antagonist muscles was used to simulate the human arm dynamics. This study is based on the assumption that the nervous system minimizes the muscular effort during goal-directed movements. The effects of prediction horizon length on the trajectory, velocity profile, and muscle activities of a reaching task are presented. The NMPC predictions of the hand trajectory to reach fixed and moving targets are in good agreement with the trajectories found by dynamic optimization and those from experiments. However, the hand velocity and muscle activations predicted by NMPC did not agree as well with experiments or with those found from dynamic optimization.

Steering disturbance rejection using a physics-based neuromusculoskeletal driver model

The aim of this work is to develop a comprehensive yet practical driver model to be used in studying driver–vehicle interactions. Drivers interact with their vehicle and the road through the steering wheel. This interaction forms a closed-loop coupled human–machine system, which influences the drivers steering feel and control performance. A hierarchical approach is proposed here to capture the complexity of the drivers neuromuscular dynamics and the central nervous system in the coordination of the drivers upper extremity activities, especially in the presence of external disturbance. The proposed motor control framework has three layers: the first (or the path planning) plans a desired vehicle trajectory and the required steering angles to perform the desired trajectory; the second (or the musculoskeletal controller) actuates the musculoskeletal arm to rotate the steering wheel accordingly; and the final layer ensures the precision control and disturbance rejection of the motor control units. The physics-based driver model presented here can also provide insights into vehicle control in relaxed and tensed driving conditions, which are simulated by adjusting the driver model parameters such as cognition delay and muscle co-contraction dynamics.

A Neuronal Model of Central Pattern Generator to Account for Natural Motion Variation

We have developed a simple mathematical model of the human motor control system, which can generate periodic motions in a musculoskeletal arm. Our motor control model is based on the idea of a central pattern generator (CPG), in which a small population of neurons generates periodic limb motion. The CPG model produces the motion based on a simple descending command—the desired frequency of motion. Furthermore, the CPG model is implemented by a spiking neuron model; as a result of the stochasticity in the neuron activities, the motion exhibits a certain level of variation similar to real human motion. Finally, because of the simple structure of the CPG model, it can generate the sophisticated muscle excitation commands much faster than optimization-based methods.

A THREE-DIMENSIONAL MUSCULOSKELETAL DRIVER MODEL TO STUDY STEERING TASKS

Realistic driver models can play an important role in developing new driver assistance technologies. A realistic driver model can reduce the time-consuming trial and error process of designing and testing products, and thereby reduce the vehicle’s development time and cost. A realistic model should provide both driver path planning and arm motions that are physiologically possible. The interaction between a driver’s hand and steering wheel can influence control performance and steering feel.The aim of this work is to develop a comprehensive yet practical model of the driver and vehicle. Consequently, a neuro-muscular driver model in conjunction with a high-fidelity vehicle model is developed to learn and understand more about the driver’s performance and preferences, and their effect on vehicle control and stability. This driver model can provide insights into task performance and energy consumption of the driver, including fatigue and co-contraction dynamics of a steering task. In addition, this driver model in conjunction with a high-fidelity steering model can be used to develop new steering technologies such as Electric Power Steering.Copyright

Minimization of Muscle Fatigue as the Criterion to Solve Muscle Forces-Sharing Problem

The application of functional electrical stimulation (FES) to muscles quickly fatigues them. Our research goal is to determine the optimal control of FES signals that delay the fatigue for as long as possible. In this research we have used a physiology-based mathematical model of muscle fatigue, to study the behaviour of a musculoskeletal system during a prolonged exercise. To solve the redundant problem of muscle force sharing, we have used a time-dependent fatigue minimization objective instead of the usual activation-based minimization criteria. Our results showed that muscle co-activation, as seen in natural human motion, does not necessarily minimize muscle fatigue.Copyright