Open Access. Powered by Scholars. Published by Universities.®
Articles 1 - 5 of 5
Full-Text Articles in Engineering
Integration Of A Statcom And Battery Energy Storage, Zhiping Yang, Shen Chen, Lin Zhang, Stan Atcitty, Mariesa Crow
Integration Of A Statcom And Battery Energy Storage, Zhiping Yang, Shen Chen, Lin Zhang, Stan Atcitty, Mariesa Crow
Electrical and Computer Engineering Faculty Research & Creative Works
The integration of an energy storage system, such as battery energy storage (BESS), into a FACTS device can provide dynamic decentralized active power capabilities and much needed flexibility for mitigating transmission level power flow problems. This paper introduces an integrated StatCom/BESS for the improvement of dynamic and transient stability and transmission capability; compare the performance of the different FACTS/BESS combinations, and provide experimental verification of the proposed controls on a scaled StatCom/BESS system
Dual Heuristic Programming Excitation Neurocontrol For Generators In A Multimachine Power System, Ganesh K. Venayagamoorthy, Donald C. Wunsch, Ronald G. Harley
Dual Heuristic Programming Excitation Neurocontrol For Generators In A Multimachine Power System, Ganesh K. Venayagamoorthy, Donald C. Wunsch, Ronald G. Harley
Electrical and Computer Engineering Faculty Research & Creative Works
The design of optimal neurocontrollers that replace the conventional automatic voltage regulators for excitation control of turbogenerators in a multimachine power system is presented in this paper. The neurocontroller design is based on dual heuristic programming (DHP), a powerful adaptive critic technique. The feedback variables are completely based on local measurements from the generators. Simulations on a three-machine power system demonstrate that DHP based neurocontrol is much more effective than the conventional PID control for improving dynamic performance and stability of the power grid under small and large disturbances. This paper also shows how to design optimal multiple neurocontrollers for …
Robust Control Of Input Limited Smart Structural Systems, Sridhar Sana, Vittal S. Rao
Robust Control Of Input Limited Smart Structural Systems, Sridhar Sana, Vittal S. Rao
Electrical and Computer Engineering Faculty Research & Creative Works
Integration of controllers with smart structural systems require the controllers to consume less power and to be small in hardware size. These requirements pose as limits on the control input and the order of the controllers. Use of reduced order model of the plant in the controller design can cause spill over problems in the closed-loop system due to possible excitation of the unmodeled dynamics. In this paper, we present a method to design output feedback robust controllers for smart structures in the presence of control input limits considering unmodeled dynamics as additive uncertainty in the design. The performance requirements …
Robust State Dependent Riccati Equation Based Guidance Laws, S. N. Balakrishnan, Ming Xin
Robust State Dependent Riccati Equation Based Guidance Laws, S. N. Balakrishnan, Ming Xin
Mechanical and Aerospace Engineering Faculty Research & Creative Works
A robust state dependent Riccati equation based guidance/control is investigated in this study. In order to have a better design tool in terms of required interceptor accelerations, the target intercept geometry is formulated in a set of polar coordinates. With this formulation, we formulate a cost function with state dependent weights. In this study, we investigate the effects of such cost functions on the levels of interceptor accelerations. We also synthesize a neural network based extra controller to achieve the robustness in the presence of the target acceleration. In this manner, we will not need target acceleration estimation explicitly in …
Robust State Dependent Riccati Equation Based Robot Manipulator Control, Ming Xin, S. N. Balakrishnan, Zhongwu Huang
Robust State Dependent Riccati Equation Based Robot Manipulator Control, Ming Xin, S. N. Balakrishnan, Zhongwu Huang
Mechanical and Aerospace Engineering Faculty Research & Creative Works
We present a new optimal control approach to robust control of robot manipulators in the framework of state dependent Riccati equation (SDRE) technique. To treat this highly nonlinear control system, we formulate it as a nonlinear optimal regulator problem. SDRE technique was used to synthesize an optimal controller to this class of robot control problem. We also synthesize a neural network based extra controller to achieve the robustness in the presence of the parameter uncertainties. A typical two-link robot position control problem was studied to show the effectiveness of SDRE approach and robust extra control design to robotic application.