scholarly journals Kalman State Estimation and LQR Assisted Adaptive Control Of a Variable Loaded Servo System

2019 ◽  
Vol 9 (3) ◽  
pp. 4125-4130 ◽  
Author(s):  
O. Aydogdu ◽  
M. L. Levent
Keyword(s):  
Adaptive Control ◽  
Control Method ◽  
Controller Design ◽  
Servo System ◽  
Adaptive Controller ◽  
Variable Load ◽  
Performance Measurements ◽  
Load Effect ◽  
Lqr Control ◽  
Control Work

This study actualized a new hybrid adaptive controller design to increase the control performance of a variable loaded time-varying system. A structure in which LQR and adaptive control work together is proposed. At first, a Kalman filter was designed to estimate the states of the system and used with the LQR control method which is one of the optimal control servo system techniques in constant initial load. Then, for the variable loaded servo (VLS) system, the Lyapunov based adaptive control was added to the LQR control method which was inadequate due to the constant gain parameters. Thus, it was aimed to eliminate the variable load effects and increase the stability of the system. In order to show the effectiveness of the proposed method, a Quanser servo module was used in Matlab-Simulink environment. It is seen from the experimental results and performance measurements that the proposed method increases the system performance and stability by minimizing noise, variable load effect and steady-state error.

scholarly journals State Estimation with Reduced-Order Observer and Adaptive-LQR Control of Time Varying Linear System

2020 ◽  
Vol 26 (2) ◽  
pp. 24-31
Author(s):  
Omer Aydogdu ◽  
Mehmet Latif Levent
Keyword(s):  
Optimal Control ◽  
Linear System ◽  
State Estimation ◽  
Servo System ◽  
Variable Load ◽  
Time Varying ◽  
Load Effect ◽  
Reduced Order ◽  
Lqr Control ◽  
Reduced Order Observer

In this study, a new controller design was created to increase the control performance of a variable loaded time varying linear system. For this purpose, a state estimation with reduced order observer and adaptive-LQR (Linear–Quadratic Regulator) control structure was offered. Initially, to estimate the states of the system, a reduced-order observer was designed and used with LQR control method that is one of the optimal control techniques in the servo system with initial load. Subsequently, a Lyapunov-based adaptation mechanism was added to the LQR control to provide optimal control for varying loads as a new approach in design. Thus, it was aimed to eliminate the variable load effects and to increase the stability of the system. In order to demonstrate the effectiveness of the proposed method, a variable loaded rotary servo system was modelled as a time-varying linear system and used in simulations in Matlab-Simulink environment. Based on the simulation results and performance measurements, it was observed that the proposed method increases the system performance and stability by minimizing variable load effect.

Lyapunov Based Adaptive Control Method for Hydraulic Servo Drive

2016 ◽  
Author(s):  
Hamid Roozbahani ◽  
Konstantin Frumkin ◽  
Heikki Handroos
Keyword(s):  
Adaptive Control ◽  
Control Algorithm ◽  
Environmental Changes ◽  
Control Method ◽  
Servo System ◽  
Adaptive Controller ◽  
Servo Drive ◽  
Hydraulic Servo ◽  
Hydraulic Servo System ◽  
Adaptive Control Algorithm

Adaptive control systems are one of the most significant research directions of modern control theory. It is well known that every mechanical appliance’s behavior noticeably depends on environmental changes, functioning-mode parameter changes and changes in technical characteristics of internal functional devices. An adaptive controller involved in control process allows reducing an influence of such changes. In spite of this such type of control methods is applied seldom due to specifics of a controller designing. The work presented in this paper shows the design process of the adaptive controller built by Lyapunov’s function method for a hydraulic servo system. The modeling of the hydraulic servo system were conducting with MATLAB® software including Simulink® and Symbolic Math Toolbox™. In this study, the Jacobi matrix linearization of the object’s mathematical model and derivation of the suitable reference models based on Newton’s characteristic polynomial were applied. In addition, an intelligent adaptive control algorithm and system model including its nonlinearities was developed to solve Lyapunov’s equation. Developed algorithm works properly and considered plant is met requirement of functioning with. The results shows that the developed adaptive control algorithm increases system performance in use devices significantly and might be used for correction of system’s behavior and dynamics.

An Adaptively Controlled Electrohydraulic Servo-Mechanism: Part 1: Adaptive Controller Design

1987 ◽  
Vol 201 (3) ◽  
pp. 175-180 ◽  
Author(s):  
K A Edge ◽  
K R A Figueredo
Keyword(s):  
Adaptive Control ◽  
Controller Design ◽  
Control Design ◽  
Adaptive Controller ◽  
Adaptive Control System ◽  
Sampled Data ◽  
Robust Controller ◽  
Servo Mechanism ◽  
Control Scheme ◽  
Model Reference Adaptive

A systematic model reference adaptive control design scheme is presented. The control scheme is developed and analysed within the framework of a sampled data system with a parameter adaptive algorithm designed on the basis of hyper stability theory. A number of supervisory functions are used to supplement the basic adaptive control system in order to enhance robust controller action.

scholarly journals Deep submergence rescue vehicle docking based on parameter adaptive control with acoustic and visual guidance

2020 ◽  
Vol 17 (2) ◽  
pp. 172988142091995 ◽  
Author(s):  
Yushan Sun ◽  
Xiangrui Ran ◽  
Jian Cao ◽  
Yueming Li
Keyword(s):  
Adaptive Control ◽  
Reinforcement Learning ◽  
Attitude Determination ◽  
Control Method ◽  
Adaptive Controller ◽  
Control Effect ◽  
Visual Guidance ◽  
Docking Method ◽  
Parameter Adaptive

In view of the difficulties in the attitude determination of wrecked submarine and the automatic attitude matching of deep submergence rescue vehicles during the docking and guidance of a submarine rescue vehicle, this study proposes a docking method based on parameter adaptive control with acoustic and visual guidance. This study omits the process of obtaining the information of the wrecked submarine in advance, thus saving considerable detection time and improving rescue efficiency. A parameter adaptive controller based on reinforcement learning is designed. The S-plane and proportional integral derivative controllers are trained through reinforcement learning to obtain the control parameters in the improvement of the environmental adaptability and anti-current ability of deep submarine rescue vehicles. The effectiveness of the proposed method is proved by simulation and pool tests. The comparison experiment shows that the parameter adaptive controller based on reinforcement learning has better control effect, accuracy, and stability than the untrained control method.

Managing driving disturbances in lateral vehicle dynamics via adaptive integrated chassis control

2020 ◽  
pp. 146441932097737
Author(s):  
Narjes Ahmadian ◽  
Alireza Khosravi ◽  
Pouria Sarhadi
Keyword(s):  
Adaptive Control ◽  
Control Method ◽  
Linear Time ◽  
Hierarchical Control ◽  
Control Level ◽  
Adaptive Controller ◽  
Stability Control ◽  
Control Laws ◽  
Mimo Model ◽  
Yaw Moment

This paper presents a vehicle stability control method based on a multi-input multi-output (MIMO) model reference adaptive control (MRAC) strategy as an advanced driver assistance system (ADAS) to enhance the handling and yaw stability of the vehicle lateral dynamics. The corrective yaw moment and additive steering angle are generated using direct yaw moment control (DYC) and active front steering (AFS) at the upper control level in the hierarchical control algorithm. A nonlinear term is added to the conventional adaptive control laws to handle parametric uncertainties and disturbances. The desired yaw moment generated by the upper-level controller is converted to the brake forces and is distributed to the rear wheels by an optimal procedure at the lower-level. The major contribution of this study is the introduction of a nonlinear integrated adaptive control method based on a constraint optimization algorithm. To verify the effectiveness of the proposed control strategy, the nonlinear integrated adaptive controller, and linear time-varying MRAC are designed and used for comparison. Simulation results are performed for the J-turn and double lane change (DLC) manoeuvres at high speeds and low tyre-road friction coefficients. The desired performance of the proposed controller exhibited significant improvement compared to the conventional MRAC in terms of yaw rate tracking and handling of sideslip limitation.

The Model-free Adaptive Composite Control Method for Permanent Magnet Linear Motor

2015 ◽  
Vol 7 (2) ◽  
pp. 30-44
Author(s):  
Rongmin Cao ◽  
Su Zhong ◽  
Shizhen Liu
Keyword(s):  
Permanent Magnet ◽  
Control Method ◽  
Controller Design ◽  
Adaptive Controller ◽  
Linear Motor ◽  
Modeling And Control ◽  
Composite Control ◽  
Model Free ◽  
Permanent Magnet Linear Motor ◽  
Force Ripple

A composite control method based on the model-free adaptive control is applied to the position or speed control of the linear motor. The model-free adaptive controller (MFAC) broke through the classical PID controller design of linear framework, is a kind of new controller, it' structure is adaptive and a kind of integration of modeling and control method. The composite control method includes an adaptive feedforward compensator which is designed to eliminate or suppress the effects of inherent force ripple for a permanent magnet linear motor (PMLM). Simulation results show that compared with PID control, the proposed composite control algorithm is more effective for the strong coupling of nonlinear system and difficult to realize stable control. And the response performance of the system is realized.

A new control method of flexible-joint manipulator with harmonic drive

2020 ◽  
Vol 234 (9) ◽  
pp. 1868-1883
Author(s):  
Guocai Yang ◽  
Yechao Liu ◽  
Minghe Jin
Keyword(s):  
Control Method ◽  
Controller Design ◽  
Joint Stiffness ◽  
Adaptive Controller ◽  
Harmonic Drive ◽  
Flexible Joint ◽  
Assignment Method ◽  
Uncertain Dynamics ◽  
Tracking Controller ◽  
Flexible Joint Manipulator

Considerable elasticity and nonlinear friction caused by harmonic transmission challenge the performance of flexible-joint manipulators. The uncertain dynamics of manipulator and the inadequate measurable states also limit the controller design. A new control method is proposed to address these problems, achieving the precise motion control of the flexible-joint manipulator. The method consists of three cascaded controllers: an adaptive controller, a torque-tracking controller, and a motor controller. The adaptive controller was adopted to generate the desired torque ensuring the robustness for uncertain dynamics. The torque-tracking controller derived the position compensation for motor control according to the torque error. As the elastic torque is under control, the vibration caused by harmonic drive can be eliminated. The motor was controlled based on poles-assignment method and friction compensation. The Kalman observer based on the Brownian motion model observed both velocity and the high-order derivatives of torque sensing. The stability of the control method was strictly proved. Calibration was performed on each joint to obtain the required joint stiffness and motor friction parameters. The control method was verified on a single joint and the frequency response of the system was obtained. The results show that the controller has good performance. The controller was realized on the self-developed seven-degree-of-freedom manipulator. The results reveal that the controller has high-precision tracking performance.

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