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.

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.

A longitudinal trajectory tracking method with L1 adaptive control for hypersonic reentry vehicles

2019 ◽  
Vol 42 (3) ◽  
pp. 386-403
Author(s):  
GenSen Han ◽  
Jun Zhou ◽  
JianGuo Guo ◽  
Qing Lu
Keyword(s):  
Adaptive Control ◽  
Trajectory Tracking ◽  
Control Method ◽  
Linear Time ◽  
Adaptive Controller ◽  
Terminal State ◽  
Linear Quadratic ◽  
L1 Adaptive Control ◽  
Reentry Vehicles ◽  
Lqr Controller

This paper presents a longitudinal trajectory tracking scheme with [Formula: see text] adaptive control for hypersonic reentry vehicles (HRVs). A linear time-varying (LTV) multiple input multiple output (MIMO) model, in which influences of lateral states, earth rotation, and linearization are considered as model uncertainties, is derived based on state and input errors of longitudinal model. The normalization of error model is used to reduce differences of magnitude orders in state and input matrix elements which may affect the stability of [Formula: see text] adaptive controller. In order to achieve an accurate tracking performance, a linear quadratic regulator (LQR) controller is employed as the baseline controller, augmented with an [Formula: see text] adaptive controller to attenuate the matched and unmatched uncertainties. Based on the augmented controller, the optimization process is executed with the estimate of uncertainties at the same time. The simulation results of LQR controller, [Formula: see text] augmentation controller and robust [Formula: see text] controller show that the [Formula: see text] adaptive control method can reduce the terminal and integral of squared state errors validly. Terminal state errors in all simulation scenarios are less than 2.5m/s, 1e-3 and 10m, respectively, which reflects its effectiveness in increasing robustness of baseline controller.

Multivariable Direct Adaptive Control of Thermal Mixing Processes

1985 ◽  
Vol 107 (4) ◽  
pp. 278-283 ◽  
Author(s):  
Qiusheng Zhang ◽  
Masayoshi Tomizuka
Keyword(s):  
Adaptive Control ◽  
Adaptive Controller ◽  
Linear Quadratic ◽  
Mixing Processes ◽  
Linear Quadratic Optimal Control ◽  
Control Approach ◽  
Thermal Mixing ◽  
Direct Adaptive Control ◽  
Integral Action ◽  
Quadratic Optimal Control

Multivariable direct adaptive control is tested on a nonlinear thermal mixing process and is compared with state space based nonadaptive controllers. The linear quadratic optimal control approach is used to design two nonadaptive controllers: one without integral action (ordinary LQ) and the other with integral action (LQI). The operating point is changed over a wide region in the experiment. The adaptive controller is verified to perform most consistently under the tested conditions.

scholarly journals Adaptive Control of a Four-Dimensional Hyperchaotic System

2019 ◽  
pp. 1-17
Author(s):  
Xuxia Li ◽  
Xinghua Fan ◽  
Jiuli Yin ◽  
Ying Zhang ◽  
Xiangxiang Lv
Keyword(s):  
Adaptive Control ◽  
Feedback Control ◽  
Control Method ◽  
Adaptive Controller ◽  
Linear Feedback ◽  
Hyperchaotic System ◽  
Linear Feedback Control ◽  
Adaptive Law ◽  
Unstable Equilibrium Point ◽  
Lyapunov Exponent Spectrum

This paper concerns the application of adaptive control method in a four-dimensional hyperchaoticsystem. Firstly, we carry out a systematic dynamic analysis including the properties of equilibriumpoint, stability, dissipation, Lyapunov exponent spectrum, and bifurcation. Both the existenceof two positive Lyapunov exponents and the Lyapunov dimension value show the hyperchaotic property of the system. Based on Lyapunov stability theorem, we then construct an adaptive controller and the adaptive law to suppress hyperchaos to the origin, which is an unstable equilibrium point under a certain parameter set. The effectiveness of the adaptive control is veried by theoretical analysis and numerical simulation. We nally brie y demonstrate the control eciency of self-linear feedback control and misaligned feedback control. For the fourdimensional hyperchaotic system, the adaptive control outperforms them from the view of control speed.

scholarly journals Temperature Control of Stirred Tank Heater using Optimal Control Technique

2020 ◽  
Author(s):  
Mustefa Jibril ◽  
Messay Tadese ◽  
Eliyas Alemayehu
Keyword(s):  
Optimal Control ◽  
Temperature Control ◽  
Open Loop ◽  
Loop System ◽  
Stirred Tank ◽  
Control Technique ◽  
Closed Loop System ◽  
Linear Quadratic ◽  
Linear Quadratic Gaussian ◽  
Quadratic Integral

This paper presents the application of optimal control problem in modeling of stirred tank heater temperature control. The analysis of the open loop system shows that the system is not efficient without a controller. Linear Quadratic Gaussian (LQG) and Linear Quadratic Integral (LQI) controllers are used to increase the performance of the system. Comparison of the closed loop system with the proposed controllers have been done with Matlab/Simulink Toolbox and a promising results have been analyzed.

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.

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