A PID controller based on CMAC and Kalman filter

Author(s):  
Honglei Wei ◽  
Changyou Guo
Keyword(s):  
2021 ◽  
Vol 11 (15) ◽  
pp. 6693
Author(s):  
Sagar Gupta ◽  
Abhaya Pal Singh ◽  
Dipankar Deb ◽  
Stepan Ozana

Robotic manipulators have been widely used in industries, mainly to move tools into different specific positions. Thus, it has become necessary to have accurate knowledge about the tool position using forward kinematics after accessing the angular locations of limbs. This paper presents a simulation study in which an encoder attached to the limbs gathers information about the angular positions. The measured angles are applied to the Kalman Filter (KF) and its variants for state estimation. This work focuses on the use of fractional order controllers with a Two Degree of Freedom Serial Flexible Links (2DSFL) and Two Degree of Freedom Serial Flexible Joint (2DSFJ) and undertakes simulations with noise and a square wave as input. The fractional order controllers fit better with the system properties than integer order controllers. The KF and its variants use an unknown and assumed process and measurement noise matrices to predict the actual data. An optimisation problem is proposed to achieve reasonable estimations with the updated covariance matrices.


Author(s):  
Meiyin Zhu ◽  
Xi Wang ◽  
Shubo Yang ◽  
Huairong Chen ◽  
Keqiang Miao ◽  
...  

Abstract Flight Environment Simulation Volume (FESV) is the most important subsystem of Altitude Ground Test Facilities (AGTF). Its control precision of temperature and pressure determines the level of test ability of AGTF. However, in practice, the sensor hysteresis and noise may greatly affect the control precision of FESV. To improve the control performance of FESV in practice, a new control structure of two degree-of-freedom (DOF) μ synthesis control with the extended Kalman filter (EKF) considering actuators and sensors uncertainty is proposed, which constitutes a core support part of the paper. For the problem of sensors de-noising, an EKF is devised to provide a credible feedback signal to the two DOF μ controller. Aiming at the problem of reference command’s rapid change, one freedom feed forward is adopted, while another freedom output feedback is used to ensure good servo tracking as well as disturbance and noise rejection; furthermore to overcome the overshoot problem and acquire dynamic tuning, an integral is introduced in inner loop; additionally, two performance weighting functions are designed to achieve robustness and control energy limit considering the uncertainties in system. In order to verify the effectiveness of the designed two DOF μ synthesis controller with EKF, we suppose a typical engine test condition with Zoom-Climb and Mach Dash and consider time delay and Gaussian noise in the sensors. The simulation results show that the designed two DOF μ synthesis controller with EKF has good servo tracking and noise rejection performance and the relative steady-state and transient errors of temperature and pressure are both less than 0.1% and 0.2% respectively. Additionally, we validate the robust performance of the designed two DOF μ controller with EKF by using the upper bound value of the uncertainty parameters. Furthermore, to verify the advantage of the designed two DOF μ controller with EKF, we compare its control results with those of without EKF and μ controller without considering sensor time delay. The comparison results show that the designed two DOF μ controller with EKF provides better performance. Finally, to verify the advantage of μ synthesis controller, we designed a PID controller and compare the simulation result with μ controller, the comparison result show that the designed μ controller is better than PID controller.


2018 ◽  
Vol 154 ◽  
pp. 03002
Author(s):  
Barlian Henryranu Prasetio ◽  
Wijaya Kurniawan

This research implements self-balancing robot using 3 algorithms. There are PID Controller, Ensemble Kalman Filter and Feed-Forward Control system. The PID controller function is as a robot equilibrium control system. The Kalman Ensemble algorithm is used to reduce noise measurement of accelerometer and gyroscope sensors. The PID controller and Ensemble Kalman filter were implemented on self-balancing robot in previous research. In this paper, we added the Feed-Forward controller that serves to detect disturbance derived from the unevenness of the ground. Disturbance is detected using 2 proximity sensors. Base on test results that the system can detect disturbance with an average delay of 2.15 seconds at Kff optimal value is 2.92. Feed-Forward effects result in self-balancing robots increasing power so that the pitch of the robot changes to anticipation of disturbance.


2016 ◽  
Vol 39 (12) ◽  
pp. 1785-1797 ◽  
Author(s):  
Feng Pan ◽  
Lu Liu ◽  
Dingyu Xue

In this paper, we used a Qball-X4 quad-rotor unmanned aerial vehicle (UAV) which was developed by the Quanser Company as the experimental platform. First, a fundamental mathematical model of the Qball-X4 quad-rotor UAV was built and a simulation model was set up based on the proposed mathematical model; then, a double closed-loop optimal proportional–integral–derivative (PID) controller based on integral of time multiplied by absolute error (ITAE) indices was designed according to the model structure. In consideration of the possible system error and data delay, we designed a corresponding Kalman filter, which can estimate the target trajectory and be put before the proposed PID controller to ensure their validity. Finally, simulation results of the system with presented PID controller and Kalman filter were shown to verify their effectiveness.


Author(s):  
Seta Yuliawan ◽  
Oyas Wahyunggoro ◽  
Nurman Setiawan

A proportional–integral–derivative (PID) controller is a type of control system that is most widely applied in industrial world. Various tuning models have been developed to obtain optimal performance in PID control. However, the methods are designed under ideal circumstances. This means that the control system which has been built will not work optimally when noise exists. Noise can come from electrical vibrations, inference of electronic components, or other noise sources. Thus, it is necessary to design PID control system that can work optimally without being disturbed by noise. In this research, Kalman filter was used to improve the performance of PID controllers. The application of Kalman filter was used to reduce the noise of the input signal so that it could generate output signal which is in accordance with the expected output. Simulation result showed that the PID performance with Kalman filter was more optimal than the ordinary one to minimize the existing noise. The resulting speed of DC motor with Kalman filter had a lower overshoot than PID control without Kalman filter. This method resulted lower integral of absolute error (IAE) than ordinary PID controls. The IAE value for the PID controller with the Kalman filter was 25.4, the PID controller with the observer was 31.0, while the IAE value in the ordinary controller was only 60.9.


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