Bursting solutions for cubic autocatalysis in a continuous stirred tank reactor with recycle and time delay

1991 ◽  
Vol 95 (8) ◽  
pp. 2939-2941 ◽  
Author(s):  
Parkash Badola ◽  
P. Rajani ◽  
V. Ravi Kumar ◽  
B. D. Kulkarni
Keyword(s):  
Time Delay ◽  
Stirred Tank ◽  
Continuous Stirred Tank Reactor ◽  
Stirred Tank Reactor ◽  
Tank Reactor ◽  
Continuous Stirred Tank ◽  
Cubic Autocatalysis

scholarly journals Adaptive Neural Control and Modeling for Continuous Stirred Tank Reactor with Delays and Full State Constraints

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Dongjuan Li ◽  
Dongxing Wang ◽  
Ying Gao
Keyword(s):  
Time Delay ◽  
State Constraints ◽  
Stirred Tank ◽  
Time Varying ◽  
Continuous Stirred Tank Reactor ◽  
Stirred Tank Reactor ◽  
Tank Reactor ◽  
Full State ◽  
Continuous Stirred Tank ◽  
Full State Constraints

In this paper, an adaptive neural network control method is described to stabilize a continuous stirred tank reactor (CSTR) subject to unknown time-varying delays and full state constraints. The unknown time delay and state constraints problem of the concentration in the reactor seriously affect the input-output ratio and stability of the entire system. Therefore, the design difficulty of this control scheme is how to debar the effect of time delay in CSTR systems. To deal with time-varying delays, Lyapunov–Krasovskii functionals (LKFs) are utilized in the adaptive controller design. The convergence of the tracking error to a small compact set without violating the constraints can be identified by the time-varying logarithm barrier Lyapunov function (LBLF). Finally, the simulation results on CSTR are shown to reveal the validity of the developed control strategy.

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