Design and experimental evaluation of feedforward controller integrating filtered-x LMS algorithm with applications to electro-hydraulic force control systems

Author(s):  
Yu Tang ◽  
Zhencai Zhu ◽  
Gang Shen

The control purpose of an electro-hydraulic force control (EHFC) system is to real time replicate the force exerted on a structure in laboratory so as to simulate loads that cannot otherwise be generated naturally. In contrast to electro-hydraulic position control system, the tracking performance of EHFC system is always limited. To enhance the force replication accuracy of EHFC systems, a feedforward inverse controller integrating filtered-x LMS adaptive algorithm is presented in this paper. The proposed controller comprises a feedforward inverse controller and an adaptive controller. The feedforward inverse controller working as an inner loop is firstly established by directly cascading the designed parametric inverse transfer function to the EHFC system with proportional integral controller and the inverse transfer function is obtained with the implementation of system identification and zero magnitude error tracking technology. Then, the adaptive controller employing the filtered-x LMS algorithm acting as an outer loop is further combined with the feedforward controller to deal with the error occurred in the inverse model design procedure. Therefore, the proposed controller is an easy-to-implement strategy and can effectively enhance the force replication performance for both phase delay errors and amplitude mismatch errors. Finally, a series of experiments are carried out on a real EHFC test rig by means of xPC target technology, and the experimental results indicate that the proposed controller has a relatively better tracking accuracy compared with the proportional integral controller and the feedforward controller. It is also worth noting that the proposed controller can also be extended to other servo control systems where high accuracy tracking performance is required.

Author(s):  
Benyamin Haghniaz Jahromi ◽  
Seyed Mohammad Taghi Almodarresi ◽  
Pooya Hajebi

: Networked control systems (NCSs) are used to control industrial and medical plants via data communication networks. These systems have many wide applications in broad range of area such as remote surgery, industrial and space sciences. Two important challenging problems in these systems are stochastic time delays and packet dropouts. Classic proportional-integral controllers due to their simple inherent design and implementation have many applications in controlling industrial and medical plants. However these simple controllers do not have high performance in NCS because of communication networks induced time-varying delays and so this causes instability in NCS. In this paper an adaptive proportional-integral controller is proposed using online estimation of network time delay technique in node application layer. The coefficients of this new controller change according to the values of estimated time delays online. Therefore, the proposed controller causes stability in NCS loop. The performance of proposed method is simulated for a DC motor that can be used in remote surgery. The simulation results show the proposed controller is better at least about 1000 times according to IAE performance index rather than classic proportional-integral controller. Also the results of practical implementation show that the proposed controller causes the stability of NCS.


Author(s):  
Hichem Othmani ◽  
D. Mezghani ◽  
A. Mami

In this article, we have set up a vector control law of induction machine where we tried different type of speed controllers. Our control strategy is of type Field Orientated Control (FOC). In this structure we designed a Fuzzy Gain-Scheduling Proportional–Integral (Pi) controller to obtain best result regarding the speed of induction machine. At the beginning we designed a Pi controller with fixed parameters. We came up to these parameters by identifying the transfer function of this controller to that of Broïda (second order transfer function). Then we designed a fuzzy logic (FL) controller. Based on simulation results, we highlight the performances of each controller. To improve the speed behaviour of the induction machine, we have designend a controller called “Fuzzy Gain-Scheduling Proportional–Integral controller” (FGS-PI controller) which inherited the pros of the aforementioned controllers. The simulation result of this controller will strengthen its performances.


Author(s):  
Chau Minh Thuyen

<p>This paper aims to design a control method using an adaptive controller for Hybrid Active Power Filter. The controller of designed method includes a traditional discrete Proportional Integral controller and a neural regulator. The neural regulator is used to estimate the nonlinear model of Hybrid Active Power Filter and predict an output value in the future to adjust the parameters of the traditional discrete Proportional Integral controller according to the change of load. Compared to the control method using a conventional Proportional Integral controller, the proposed controller shows the advantages of smaller compensation error and smaller total harmonic distortion and able to online control very well. The simulations have verified the effectiveness of proposed controller.</p><p> </p>


2018 ◽  
Vol 41 (4) ◽  
pp. 1088-1100
Author(s):  
Yimin Zhou

In this paper, an adaptive proportional-integral controller based on a fuzzy relational model is developed for the purpose of fault tolerant control in a nonlinear, information-poor system. First, the methods of fault tolerant control are briefly introduced. An air-cooling subsystem in a heating, ventilating and air-conditioning system is used as an example to describe the fuzzy relational modelling procedure. Then a proportional-integral (PI) observer is established for fault identification and a PI-based adaptive controller is designed for fault tolerance with varied parameter adjusting. By introducing the fault estimation, an adaptive mechanism is adopted to update the parameter selection in the control scheme. Sensor noise is also considered in the method and simulation experiments are performed to verify the effectiveness of the proposed scheme.


Author(s):  
Viyils Sangregorio-Soto ◽  
Claudia L. Garzon-Castro ◽  
Gianfranco Mazzanti ◽  
Manuel Figueredo ◽  
John A. Cortes-Romero

Sign in / Sign up

Export Citation Format

Share Document