A model-based control method applicable to unstable, non-minimum-phase, nonlinear processes

2004 ◽  
Author(s):  
C. Panjapornpon ◽  
M. Soroush ◽  
W.D. Seider
Keyword(s):  
Control Method ◽  
Minimum Phase ◽  
Nonlinear Processes ◽  
Model Based Control ◽  
Model Based

Model Based Temperature Control in RTP Yielding ±0.1 °C accuracy on A 1000 °C, 2 second, 100 °C/s Spike Anneal

1998 ◽  
Vol 525 ◽  
Author(s):  
Peter Vandenabeele ◽  
Wayne Renken
Keyword(s):  
Differential Equations ◽  
Temperature Control ◽  
Control Method ◽  
Nonlinear Differential Equations ◽  
System Response ◽  
Model Based Control ◽  
First Order ◽  
Model Based ◽  
Measured System ◽  
Spike Anneal

ABSTRACTA Model Based Control method is presented for accurate control of RTP systems. The model uses 4 states: lamp filament temperature, wafer temperature, quartz temperature and TC temperature. A set of 4 first order, nonlinear differential equations describes the model. Feedback is achieved by updating the model, based on a comparison between actual (measured) system response and modeled system response.

Model-Based Controller Design for Unstable, Non-Minimum-Phase, Nonlinear Processes

2006 ◽  
Vol 45 (8) ◽  
pp. 2758-2768 ◽  
Author(s):  
Chanin Panjapornpon ◽  
Masoud Soroush ◽  
Warren D. Seider
Keyword(s):  
Controller Design ◽  
Minimum Phase ◽  
Nonlinear Processes ◽  
Model Based

scholarly journals Control of functional electrical stimulation for restoration of motor function

2017 ◽  
Vol 30 (3) ◽  
pp. 295-312
Author(s):  
Dejan Popovic
Keyword(s):  
Biological Control ◽  
Electrical Stimulation ◽  
Functional Electrical Stimulation ◽  
Control Method ◽  
Level Structure ◽  
Open Loop ◽  
Model Based Control ◽  
Model Based ◽  
Assistive Systems ◽  
Artificial Control

An injury or disease of the central nervous system (CNS) results in significant limitations in the communication with the environment (e.g., mobility, reaching and grasping). Functional electrical stimulation (FES) externally activates the muscles; thus, can restore several motor functions and reduce other health related problems. This review discusses the major bottleneck in current FES which prevents the wider use and better outcome of the treatment. We present a control method that we continually enhance during more than 30 years in the research and development of assistive systems. The presented control has a multi-level structure where upper levels use finite state control and the lower level implements model based control. We also discuss possible communication channels between the user and the controller of the FES. The artificial controller can be seen as the replica of the biological control. The principle of replication is used to minimize the problems which come from the interplay of biological and artificial control in FES. The biological control relies on an extensive network of neurons sending the output signals to the muscles. The network is being trained though many the trial and error processes in the early childhood, but staying open to changes throughout the life to satisfy the particular needs. The network considers the nonlinear and time variable properties of the motor system and provides adaptation in time and space. The presented artificial control method implements the same strategy but relies on machine classification, heuristics, and simulation of model-based control. The motivation for writing this review comes from the fact that many control algorithms have been presented in the literature by the authors who do not have much experience in rehabilitation engineering and had never tested the operations with patients. Almost all of the FES devices available implement only open-loop, sensory triggered preprogrammed sequences of stimulation. The suggestion is that the improvements in the FES devices need better controllers which consider the overall status of the potential user, various effects that stimulation has on afferent and efferent systems, reflexive responses to the FES and direct responses to the FES by non-stimulated sensory-motor systems, and the greater integration of the biological control.

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