The role of the supporting metal oxide on the noble metal particle size was systematically investigated for the flame spray pyrolysis process. The materials were produced at fixed process conditions such as resident t...
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The role of the supporting metal oxide on the noble metal particle size was systematically investigated for the flame spray pyrolysis process. The materials were produced at fixed process conditions such as resident time of the particles in the flame, energy input and maximum temperature. The materials exhibited different noble metal particle sizes, having the same surface area of the support and metal loading. The mechanisms of metal particle formation in the flame and the effect of the metal oxide support were presented for the controlled production of a desired metal particle size. With these concepts, the properties of the noble metal on the metal oxide support and the interaction with the support can be tailored for specific applications.
The present paper contributes to the issues of batch process modelling and monitoring by proposing a time-varying state space (TVSS) model for the evaporative sugar crystallization industrial process. The study is foc...
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The present paper contributes to the issues of batch process modelling and monitoring by proposing a time-varying state space (TVSS) model for the evaporative sugar crystallization industrial process. The study is focused on issues of on-line detection of changes in crystallization process operation, the early warning of process malfunctions and potential production failures; problems that have not been directly addressed by existing statistical monitoring schemes. The TVSS methodology is compared with current state-of-the-art techniques and the results obtained demonstrate the superior performance of the TVSS approach to successfully detect abnormal events and periods of bad operation.
We present a stabilizing scheduled output feedback Model Predictive control (MPC) algorithm for constrained nonlinear systems with large operating regions. We design a set of local output feedback predictive controlle...
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We present a stabilizing scheduled output feedback Model Predictive control (MPC) algorithm for constrained nonlinear systems with large operating regions. We design a set of local output feedback predictive controlle...
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We present a stabilizing scheduled output feedback Model Predictive control (MPC) algorithm for constrained nonlinear systems with large operating regions. We design a set of local output feedback predictive controllers with their estimated regions of stability covering the desired operating region, and implement them as a single scheduled output feedback MPC which on-line switches between the set of local controllers and achieves nonliilear transitions with guaranteed stability. This algorithm provides a general framework for scheduled output feedback MPC design.
The paper presents a new deterministic framework for assessing constrained control loop performance. The proposed dynamic performance index is based on the dynamic operating work of Uztiirk and Georgakis (2002). It fo...
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The paper presents a new deterministic framework for assessing constrained control loop performance. The proposed dynamic performance index is based on the dynamic operating work of Uztürk and Georgakis (2002). I...
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The paper presents a new deterministic framework for assessing constrained control loop performance. The proposed dynamic performance index is based on the dynamic operating work of Uztürk and Georgakis (2002). It focuses on the time related characteristics of controllers' response to set-point changes or steptype disturbances. It explicitly takes into account the existence of constraints on manipulated variables. These constraints include minimum and maximum values as well as an upper limit on the rate of change of the input variables.
A robust adaptive control design is considered for a class of bilinear plants with unmodeled high-order dynamics and bounded disturbances. A basic optimal control law is first introduced by the generalized minimum var...
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Using identification of neural networks, a new robust iterative learning control algorithm is proposed in the paper. Combined with feedback control in real time, the neural network is employed to identify the nonlinea...
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Force control of a robot manipulator is important for the robot to perform physical interaction with its manipulated objects as well as its environment. Usually, the environmental dynamics is unknown and during intera...
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Force control of a robot manipulator is important for the robot to perform physical interaction with its manipulated objects as well as its environment. Usually, the environmental dynamics is unknown and during interactions the environmental dynamics will influence the robot's control loop. In this research, based on the fact that the transfer functions from the robot's control torque to the environmental reaction force is biproper, a novel 2 degree of freedom adaptive control approach is presented and is applied for the explicit force control of the robot manipulator. In this approach, both force feedback and feedforward controllers are involved in the robot's control system, the feedback control is set as constant while the feedforward controller is adjusted adaptively online to approach the inverse of the force control transfer function. Using this approach, exact force response without any loop delay can be realized. Computer simulations show the effectiveness of this control approach
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