Operator-based nonlinear feedback control design using robust right coprime factorization

In this note, robust stabilization and tracking performance of operator based nonlinear feedback control systems are studied by using robust right coprime factorization. Specifically, a new condition of robust right coprime factorization of nonlinear systems with unknown bounded perturbations is derived. Using the new condition, a broader class of nonlinear plants can be controlled robustly. When the spaces of the nonlinear plant output and the reference input are different, a space change filter is designed, and in this case this note considers tracking controller design using the exponential iteration theorem

Tracking of perturbed nonlinear plants using robust right coprime factorization approach

This paper deals with a plant output tracking design problem of perturbed nonlinear plants by using a robust right coprime factorization approach. An interesting control system design scheme, which was given by G. Chen and Z. Han, uses robustness of the right coprime factorization for robust stability of the closed-loop system with perturbation. Unfortunately, robust right coprime factorization cannot easily improve tracking performance of the control system except for simple cases. In this paper, a nonlinear operator-based design method for nonlinear plant output to track a reference input is given. Examples are presented to support the theoretical analysis.</p

オペレータリロンニモトヅクアツデンアクチュエータヲモチイタヘイバンコウゾウブツノヒセンケイシンドウセイギョニカンスルケンキュウ

博士（学術）東京農工大

Decentralized Robust Capacity Control of Job Shop Systems with Reconfigurable Machine Tools

Manufacturing companies are confronted with various challenges from the perspective of customers individual requirements concerning variations of types of products, quantities and delivery dates. This renders the manufacturing process to be more dynamic and complex, which may result in bottlenecks and unbalanced capacity distributions. To cope with these problems, capacity adjustment is an effective approach to balance capacity and load for short or medium term fluctuations on the operational layer. Particularly, new technologies and algorithms need to be developed for the implementation of capacity adjustment. Reconfigurable machine tools (RMTs) and operator-based robust right coprime factorization (RRCF) provide an opportunity for a new capacity control strategy. Therefore, the main purpose of the research is to develop an effective machinery-oriented capacity control strategy by incorporating RMTs and RRCF for a job shop system to deal with volatile customer demands

Speed Control Based on ESO for the Pitching Axis of Satellite Cameras

The pitching axis is the main axis of a satellite camera and is used to control the pitch posture of satellite cameras. A control strategy based on extended state observer (ESO) is designed to obtain a fast response speed and highly accurate pitching axis control system and eliminate disturbances during the adjustment of pitch posture. First, a sufficient condition of stabilization for ESO is obtained by analyzing the steady-state error of the system under step input. Parameter tuning and disturbance compensation are performed by ESO. Second, the ESO of speed loop is designed by the speed equation of the pitching axis of satellite cameras. The ESO parameters are obtained by pole assignment. By ESO, the original state variable observes the motor angular speed and the extended state variable observes the load torque. Therefore, the external load disturbances of the control system are estimated in real time. Finally, simulation experiments are performed for the system on the cases of nonload starting, adding external disturbances on the system suddenly, and the load of system changing suddenly. Simulation results show that the control strategy based on ESO has better stability, adaptability, and robustness than the PI control strategy

Optimal Control of a PEM Fuel Cell for the Inputs Minimization

The trajectory tracking problem of a proton exchange membrane (PEM) fuel cell is considered. To solve this problem, an optimal controller is proposed. The optimal technique has the objective that the system states should reach the desired trajectories while the inputs are minimized. The proposed controller uses the Hamilton-Jacobi-Bellman method where its Riccati equation is considered as an adaptive function. The effectiveness of the proposed technique is verified by two simulations