Simulation of an expert model-based adaptive controller

1990 ◽  
Vol 20 (4) ◽  
pp. 81-87
Author(s):  
Mark S. Ma
Keyword(s):  
Adaptive Controller ◽  
Model Based ◽  
Expert Model

Model-based control of the substrate concentration in an aeration basin using the wastewater level

1998 ◽  
Vol 37 (12) ◽  
pp. 335-342 ◽  
Author(s):  
Jacek Czeczot
Keyword(s):  
Substrate Concentration ◽  
Consumption Rate ◽  
Least Square Method ◽  
Estimation Procedure ◽  
Adaptive Controller ◽  
Open Loop ◽  
Least Square ◽  
Substrate Consumption ◽  
Model Based ◽  
Effluent Flow Rate

This paper deals with the minimal-cost control of the modified activated sludge process with varying level of wastewater in the aerator tank. The model-based adaptive controller of the effluent substrate concentration, basing on the substrate consumption rate and manipulating the effluent flow rate outcoming from the aerator tank, is proposed and its performance is compared with conventional PI controller and open loop behavior. Since the substrate consumption rate is not measurable on-line, the estimation procedure on the basis of the least-square method is suggested. Finally, it is proved that cooperation of the DO concentration controller with the adaptive controller of the effluent substrate concentration allows the process to be operated at minimum costs (low consumption of aeration energy).

Characteristic model-based adaptive control for cryogenic wind tunnels

2020 ◽  
pp. 014233122096065
Author(s):  
Chenhui Yu ◽  
Fei Liao ◽  
Haibo Ji ◽  
Wenhua Wu
Keyword(s):  
Reynolds Number ◽  
Wind Tunnel ◽  
Flow Field ◽  
Control Problem ◽  
Mimo System ◽  
Adaptive Controller ◽  
Characteristic Model ◽  
Model Based ◽  
Field Control ◽  
Cryogenic Wind Tunnel

With the increasing requirement of Reynolds number simulation in wind tunnel tests, the cryogenic wind tunnel is considered as a feasible method to realize high Reynolds number. Characteristic model-based adaptive controller design method is introduced to flow field control problem of the cryogenic wind tunnel. A class of nonlinear multi-input multi-output (MIMO) system is given for theoretical research that is related to flow field control of the cryogenic wind tunnel. The characteristic model in the form of second-order time-varying difference equations is provided to represent the system. A characteristic model-based adaptive controller is also designed correspondingly. The stability analysis of the closed loop system composed of the characteristic model or the exact discrete-time model and the proposed controller is investigated respectively. Numerical simulation is presented to illustrate the effectiveness of this control method. The modeling and control problem based on characteristic model method for a class of MIMO system are studied and first applied to the cryogenic wind tunnel control field.

scholarly journals Data-Driven Model-Free Adaptive Control of Z-Source Inverters

Sensors ◽  
2021 ◽  
Vol 21 (22) ◽  
pp. 7438
Author(s):  
Yasin Asadi ◽  
Amirhossein Ahmadi ◽  
Sasan Mohammadi ◽  
Ali Moradi Amani ◽  
Mousa Marzband ◽  
...  
Keyword(s):  
State Feedback ◽  
Adaptive Controller ◽  
Green Energy ◽  
Data Driven ◽  
Control Action ◽  
Linear Quadratic ◽  
Model Based ◽  
Model Free ◽  
Quadratic Integral ◽  
Lyapunov Sense

The universal paradigm shift towards green energy has accelerated the development of modern algorithms and technologies, among them converters such as Z-Source Inverters (ZSI) are playing an important role. ZSIs are single-stage inverters which are capable of performing both buck and boost operations through an impedance network that enables the shoot-through state. Despite all advantages, these inverters are associated with the non-minimum phase feature imposing heavy restrictions on their closed-loop response. Moreover, uncertainties such as parameter perturbation, unmodeled dynamics, and load disturbances may degrade their performance or even lead to instability, especially when model-based controllers are applied. To tackle these issues, a data-driven model-free adaptive controller is proposed in this paper which guarantees stability and the desired performance of the inverter in the presence of uncertainties. It performs the control action in two steps: First, a model of the system is updated using the current input and output signals of the system. Based on this updated model, the control action is re-tuned to achieve the desired performance. The convergence and stability of the proposed control system are proved in the Lyapunov sense. Experiments corroborate the effectiveness and superiority of the presented method over model-based controllers including PI, state feedback, and optimal robust linear quadratic integral controllers in terms of various metrics.

Characteristic model–based adaptive controller with discrete extended state observer for servo systems

2017 ◽  
Vol 231 (4) ◽  
pp. 259-270 ◽  
Author(s):  
Xiang Wang ◽  
Yifei Wu ◽  
Enze Zhang ◽  
Jian Guo ◽  
Qingwei Chen
Keyword(s):  
Discrete Time ◽  
Adaptive Controller ◽  
State Observer ◽  
Lyapunov Theory ◽  
Extended State Observer ◽  
Recursive Least Squares ◽  
Extended State ◽  
Servo Systems ◽  
Characteristic Model ◽  
Model Based

Inertia variations and torque disturbances, most often considered as two of the major uncertainties in servo systems, highly affect the control performance. This article presents a characteristic model–based adaptive controller in the presence of large-range load inertia variations. A discrete-time characteristic model of the servo system, which has more advantages in describing time-varying dynamics, is established. The parameters of characteristic model are identified by a recursive least squares algorithm. To restrain the identification error and load torque disturbances, a discrete extended state observer is newly designed for the discrete-time system. Both the convergence of discrete extended state observer and the stability of closed-loop system are verified by the Lyapunov theory. Finally, simulation and experimental results demonstrate that the proposed controller provides better performance than the fuzzy proportional integral controller in terms of adaptability and robustness.

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