Naser Mehrabi

Optimal disturbance rejection control design for Electric Power Steering systems

Nowadays many automobile manufacturers are switching to Electric Power Steering (EPS) for its advantages on performance and cost. In this paper, a mathematical model of a column type EPS system is established, and its state-space expression is constructed. Then three different control methods are implemented and performance, robustness and disturbance rejection properties of the EPS control systems are investigated. The controllers are tested via simulation and results show a modified Linear Quadratic Gaussian (LQG) controller can track the characteristic curve well and effectively attenuate external disturbances.

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 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.

Design and Evaluation of an Observer-based Disturbance Rejection Controller for Electric Power Steering Systems

The goal of this paper is to develop an observer-based disturbance rejection electric power steering (EPS) controller to provide steering assistance and improve the driver’s steering feel. For the purpose of control design, a control-oriented model of a vehicle with a column-assist EPS system is developed and verified against a high-fidelity multibody dynamics model of the vehicle. The high-fidelity model is used to mimic vehicle dynamics to study controller performance in realistic driving conditions. Then, a linear quadratic Gaussian approach is used to design an EPS optimal controller, in which a Kalman filter estimates the unmeasured steering system’s states and external disturbance. A new formulation for the linear quadratic regulator objective function is proposed to take advantages of the known information about the system dynamics to attenuate the disturbance and magnify the driver’s torque. Finally, the EPS controller is applied to the high-fidelity vehicle model in a software-in-the-loop simulation to evaluate its robustness and performance under realistic conditions. The results show that the proposed controller can effectively reduce the disturbance induced in the steering rack, and simultaneously magnify the driver’s steering torque by use of a bi-linear EPS characteristic curve. Then, to show the disturbance rejection properties of this EPS controller, its performance is compared with H2/H∞ and PID control designs using time and frequency domain analysis.

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

Steering Feel Improvement for Different Driver Types Using Model-Based Control

A realistic driver model can support the development of new steering technologies by reducing the time-consuming trial and error process of designing products. A neuromuscular driver model, by offering physiologically realistic steering maneuvers can provide insights into the task performance and energy consumption of the driver, including fatigue and muscle co-contraction. Here, two muscles are used in a simplified neuromuscular driver model. To study the effect of driver’s characteristics such as age, gender and physical ability on steering, the muscle parameters are adjusted to represent a particular population. Then, this modified driver model is used to to tune the Electric Power Steering (EPS) assist curves for that particular population.Copyright