Temperature trajectory control of cryogenic wind tunnel with robust L1 adaptive control

2017 ◽  
Vol 40 (13) ◽  
pp. 3675-3689 ◽  
Author(s):  
Rusong Zhu ◽  
Guofu Yin ◽  
Gengsheng Tang ◽  
Hai Wang ◽  
Shuangxi Zhang
Keyword(s):  
Adaptive Control ◽  
Wind Tunnel ◽  
Temperature Control ◽  
Adaptive Controller ◽  
Linear Temperature ◽  
L1 Adaptive Control ◽  
State Dependent ◽  
Non Linear ◽  
Linear State ◽  
Cryogenic Wind Tunnel

Temperature control in a cryogenic wind tunnel is the key to realizing finely controlled Reynolds number close to true flight. This study deploys the L1 adaptive control methodology to ensure the total temperature profile of the cryogenic wind tunnel tracks a specified reference trajectory. After introducing a non-linear model of a cryogenic wind tunnel and a linear temperature model, a linear–quadratic–Gaussian (LQG) controller is implemented as the baseline controller. The L1 adaptive controller with piecewise constant adaptive law is used as an augmentation to the baseline controller to cancel the matched and unmatched uncertainties within the actuator’s bandwidth. By introducing two modifications to the standard L1 adaptive controller, which are the transportation delay modelling in the state predictor and the non-linear state dependent filter, the L1 adaptive controller improves the performance of the baseline controller in the presence of uncertainties in temperature control, guaranteeing proper stability and delay margin. The simulation results and analysis demonstrate the effectiveness of the proposed control architecture. The main contribution of this paper lies in the first applications of L1 adaptive control to the wind tunnel control problem and the non-linear state dependent filter in L1 adaptive control structure.

scholarly journals Temperature control of cryogenic wind tunnel with a modified L1 adaptive output feedback control

2018 ◽  
Vol 51 (9-10) ◽  
pp. 498-513 ◽  
Author(s):  
Rusong Zhu ◽  
Guofu Yin ◽  
Zhenhua Chen ◽  
Shuangxi Zhang ◽  
Zili Guo
Keyword(s):  
Adaptive Control ◽  
Temperature Control ◽  
Output Feedback ◽  
Feedforward Control ◽  
Adaptive Controller ◽  
Linear Quadratic ◽  
Linear Quadratic Gaussian ◽  
L1 Adaptive Control ◽  
Adaptive Law ◽  
Cryogenic Wind Tunnel

Background: Temperature is one of the main variables need to be regulated in cryogenic wind tunnel to realize the true flight Reynolds number. A new control methodology based on L1 output feedback adaptive control is deployed in the temperature control. Methods: This design is composed of three parts: linear quadratic Gaussian baseline control, L1 adaptive control and nonlinear feedforward control. A linear quadratic Gaussian controller is implemented as the baseline controller to provide the basic robustness of temperature control. A L1 output feedback adaptive controller with a modified piecewise constant adaptive law is deployed as an augmentation for the baseline controller to cancel the uncertainties within the actuator’s bandwidth. The modified adaptive law can guarantee better steady-state tracking performance compared with the standard adaptive law. A global nonlinear optimization process is carried out to obtain a suboptimal filter design for the L1 controller to maximize the performance index. The nonlinear feedforward control is to cancel the coupling effects in control of the tunnel. Results: With these design techniques, the augmented L1 adaptive controller improves the performance of the baseline controller in the presence of uncertainties of dynamics. The simulation results and analysis demonstrate the effectiveness of the proposed control architecture. Conclusion: The modification of adaptive law plus the global nonlinear optimization of the filter in the L1 adaptive control architecture helps the controller achieve good control performance and acceptable robustness for the temperature control over a wide range of operations.

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.

lBest-HS algorithm based concurrent L1 adaptive control for non-Linear systems

2020 ◽  
Vol 96 ◽  
pp. 390-414
Author(s):  
Roshni Maiti ◽  
Kaushik Das Sharma ◽  
Gautam Sarkar
Keyword(s):  
Adaptive Control ◽  
Linear Systems ◽  
L1 Adaptive Control ◽  
Non Linear ◽  
Non Linear Systems

scholarly journals A L1 Adaptive Control Scheme for UAV Carrier Landing Using Nonlinear Dynamic Inversion

2019 ◽  
Vol 2019 ◽  
pp. 1-8 ◽  
Author(s):  
Ke Lu ◽  
Chunsheng Liu
Keyword(s):  
Adaptive Control ◽  
Nonlinear Dynamic ◽  
Adaptive Controller ◽  
Dynamic Inversion ◽  
Model Inversion ◽  
L1 Adaptive Control ◽  
Control Scheme ◽  
Glide Slope ◽  
Adaptive Control Scheme ◽  
Nonlinear Dynamic Inversion

This paper presents a L1 adaptive controller augmenting a dynamic inversion controller for UAV (unmanned aerial vehicle) carrier landing. A three axis and a power compensator NDI (nonlinear dynamic inversion) controller serves as the baseline controller for this architecture. The inner-loop command inputs are roll-rate, pitch-rate, yaw-rate, and thrust commands. The outer-loop command inputs come from the guidance law to correct the glide slope. However, imperfect model inversion and nonaccurate aerodynamic data may cause degradation of performance and may lead to the failure of the carrier landing. The L1 adaptive controller is designed as augmentation controller to account for matched and unmatched system uncertainties. The performance of the controller is examined through a Monte Carlo simulation which shows the effectiveness of the developed L1 adaptive control scheme based on nonlinear dynamic inversion.

Adaptive Control for Uncertain Hysteretic Systems

2013 ◽  
Vol 136 (1) ◽  
Author(s):  
Xiaotian Zou ◽  
Jie Luo ◽  
Chengyu Cao
Keyword(s):  
Adaptive Control ◽  
Linear Time ◽  
Adaptive Controller ◽  
Time Varying ◽  
Performance Bounds ◽  
L1 Adaptive Control ◽  
Hysteretic Systems ◽  
Time Varying Parameters ◽  
L1 Adaptive Controller ◽  
Uniformly Bounded

This paper presents an approach to use the L1 adaptive controller for a class of uncertain systems in the presence of unknown Preisach-type hysteresis in input, unknown time-varying parameters, and unknown time-varying disturbances. The hysteresis operator can be transformed into an equivalent linear time-varying (LTV) system with uncertainties, which means that the effect of the hysteresis can be considered as general uncertainties to the system. Without constructing the inverse hysteresis function, the L1 adaptive control is used to handle the uncertainties introduced by the hysteresis, as well as system dynamics. The adaptive controller presented in this paper ensures uniformly bounded transient and tracking performance for uncertain hysteretic systems. The performance bounds can be systematically improved by increasing the adaptation rate. Simulation results with Preisach-type hysteresis are provided to verify the theoretical findings.

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