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In this study, a structure of a five degree of freedom flywheel energy storage system (FESS) is introduced. A nonlinear model of active magnetic bearing (AMB) system in the FESS is obtained by Lagrange's equation. In this model, the current in each coil is treated as a state variable and the control input is the voltage applied to each coil, this approach offers more advantages than current control input approach. PID controllers with decentralized structure are proposed to control the nonlinear multiple-input multiple-output (MIMO) system. Dynamic behavior of the flywheel in magnetic bearings and performance of the controller is discussed in simulation results.

As a branch of the Hydraulic Hybrid Vehicle (HHV) technology, Series Hydraulic Hybrid Vehicle (SHHV) has been an important research object of institutions and automotive manufacturers all over the world. With the flexibility of engine management and regenerative braking characteristics SHHV is expected to be a short-term solution to develop a higher efficiency, cleaner, and safer transportation. In this work, the function and parameter determination of key components for SHHV are discussed. Based on the analytical analysis, the suitable set of component parameters is selected. The model of SHHV is implemented via Simulink/MATLAB mostly based on SimScape toolbox. The proposed model can be used as a development tool to quickly simulate the real hybrid system when it allows applying different parameter sets and in various conditions. The performance of the system is evaluated through some specific cases and the capability of braking energy recovery of the system is also investigated. Simulation results indicate that for a 2.5 ton truck case, more than 86% of braking energy can be captured and more than 72% of that energy can be returned to the kinetic energy of vehicle motion.

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Two active noise control (ANC) algorithms for internal combustion engine exhaust systems are developed and their performances are compared in various experiments. The first controller is based on the filtered-x least mean square (FXLMS) algorithm with feedback neutralization, while the second is a fixed controller with a gain-scheduled active control technique for broadband attenuation with thermal effects. Both control algorithms are implemented on a digital signal processing (DSP) platform. Experiments are carried out to evaluate the attenuation performance of the proposed active noise control systems for an engine exhaust system. The results of the experiments indicate that both the adaptive controller and the gain-scheduled controller effectively suppress the noise of engine exhaust systems. The experimental comparison and analysis of the proposed controllers are also described.

The main objective of this investigation is to improve the tracking accuracy of a piezo-actuated positioning stage using an iterative learning control. First, to compensate for the tracking error of the piezo-actuated positioning stage that is caused by nonlinear hysteresis, the dynamics of the hysteresis is modeled using the Bouc-Wen model. The particle swarm optimization (PSO) is used to determine the parameters of the inverse-hysteresis model. Second, the design of an iterative learning control is presented. Based on the simulation, the appropriate value of the learning rate is determined. Finally, the efficacy of the approach is demonstrated to achieve high accuracy positioning via the real-time experiments. The experimental result of the piezo-actuated positioning stage is measured by the laser interferometer (HP-5529A). The experimental results show that the iterative learning control can compensate the hysteresis-caused tracking error and the positional accuracy of better than 100 nano-meter is readily achieved.