Francis F. Assadian

By Francis Assadian, Kevin Mallon and Brian Walker

During the lifetime of an energy storage system, its health deteriorates from use due to irreversible internal changes to the system. This degradation results in decreased capacity and efficiency of the battery or capacitor. This chapter reviews empirical aging models for lithium-ion battery and ultracapacitor energy storage systems. It will explore how operating conditions like large currents, high temperature, or deep discharge cycles impact the health of the energy storage system. After reviewing aging models, this chapter will then show how these models can be used in vehicle energy management control systems to reduce energy storage system aging. This includes both aging-aware control and control of hybrid energy storage systems (systems that include both a battery and an ultracapacitor).

Part of the book: Energy Storage Devices

By Ehsan Arasteh and Francis Assadian

This chapter discusses control design of three different brake-by-wire actuators. The brakes studied include an Electro-Hydraulic brake with pressure modulation for wheel slip control, and two different Electro-Mechanical Brake configurations that directly use electric motors to control the movement of the caliper for wheel slip control. After modeling the actuators with the use of bond graphs, a cascaded control architecture is used to control these active systems. Individual controllers are designed using Youla robust control design method. Then, a feed-forward disturbance rejection is designed and added to the loops and its effectiveness is analyzed. Finally, a one-wheel model is used to compare these brake-by-wire systems in terms of stopping distance and actuator efforts.

Part of the book: Advanced Applications of Hydrogen and Engineering Systems in the Automotive Industry

By Rongfei Li and Francis F. Assadian

The use of robotic technology has drastically increased in manufacturing in the twenty-first century. But by utilizing their sensory cues, humans still outperform machines, especially in the micro scale manufacturing, which requires high-precision robot manipulators. These sensory cues naturally compensate for high level of uncertainties that exist in the manufacturing environment. Uncertainties in performing manufacturing tasks may come from measurement noise, model inaccuracy, joint compliance (e.g., elasticity) etc. Although advanced metrology sensors and high-precision microprocessors, which are utilized in nowadays robots, have compensated for many structural and dynamic errors in robot positioning, but a well-designed control algorithm still works as a comparable and cheaper alternative to reduce uncertainties in automated manufacturing. Our work illustrates that a multi-robot control system can reduce various uncertainties to a great amount.

Part of the book: Production Engineering and Robust Control

By Francis Assadian and George Martin

This chapter summarizes controller methodologies for lateral control of a standard passenger vehicle, with front-wheel steering and fixed rear wheels. Comparisons are made to elucidate benefits and drawbacks of the application of a model predictive controller. Select widely used system models are described, and a general problem formulation for the model predictive controller is given. Two publicly available examples of this application are reviewed and compared, and the results are discussed.

Part of the book: Model Predictive Control

By Rongfei Li and Francis F. Assadian

Image-based visual servoing (IBVS) methods have been well developed and used in many applications, especially in pose (position and orientation) alignment. However, most research papers focused on developing control solutions when 3D point features can be detected inside the field of view. This work proposes an innovative feedforward-feedback adaptive control algorithm structure with the Youla parameterization method. A designed feature estimation loop ensures stable and fast motion control when point features are outside the field of view. As 3D point features move inside the field of view, the IBVS feedback loop preserves the precision of the pose at the end of the control period. Also, an adaptive controller is developed in the feedback loop to stabilize the system in the entire range of operations. The nonlinear camera and robot manipulator model is linearized and decoupled online by an adaptive algorithm. The adaptive controller is then computed based on the linearized model evaluated at current linearized point. The proposed solution is robust and easy to implement in different industrial robotic systems. Various scenarios are used in simulations to validate the effectiveness and robust performance of the proposed controller.

Part of the book: Latest Adaptive Control Systems [Working title]