Adaptive PID Controller Using RLS for SISO Stable and Unstable Systems

Fahmy, Rania A.; Badr, Ragia I.; Rahman, Farouk A.

doi:10.1155/2014/507142

Cited by 21 publications

(20 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this section, an adaptive switch controller is proposed for the nonlinear MIMO system (1). e controller consists of the neural network APID and neural network IAC approach.…”

Section: Adaptive Switch Controller Structurementioning

confidence: 99%

“…For instance, the proportional integrate derivative (PID) controller has been widely spread in various applications until these days due to its simple structure and easy implementation [1,2]. A challenging problem in designing a PID controller is to find its appropriate gain values [1]. For nonlinear systems, PID controller performance decreases under a great deal of tuning efforts of constant gains to get local stability.…”

Section: Introductionmentioning

confidence: 99%

“…In [4][5][6][7], a PID controller-based neural network is proposed with self-learning and adaptive ability for nonlinear systems. In [1], the author proposed an adaptive PID controller using the recursive least square algorithm for single-input single-output stable and unstable systems. However, in [3] and in [8], respectively, a PID controller based, firstly, on the wavelet neural network is applied to the AC motor and, secondly, on radial basis function is applied to quadrator.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Neural Adaptive PID and Neural Indirect Adaptive Control Switch Controller for Nonlinear MIMO Systems

Slama

Errachdi

Benrejeb

2019

Mathematical Problems in Engineering

View full text Add to dashboard Cite

This paper proposes an adaptive switch controller (ASC) design for the nonlinear multi-input multi-output system (MIMO). In fact, the proposed method is an online switch between the neural network adaptive PID (APID) controller and the neural network indirect adaptive controller (IAC). According to the design of the neural network IAC scheme, the adaptation law has been developed by the gradient descent (GD) method. However, the adaptive PID controller is built based on the neural network combining the PID control and explicit neural structure. The strategy of training consists of online tuning of the neural controller weights using the backpropagation algorithm to select the suitable combination of PID gains such that the error between the reference signal and the actual system output converges to zero. The stability and tracking performance of the neural network ASC, the neural network APID, and the neural network IAC are analyzed and evaluated by the Lyapunov function. Then, the controller results are compared between APID, IAC, and ASC, in this paper, applying to a nonlinear system. From simulations, the proposed adaptive switch controller has better effects both on response time and on tracking performance with smallest MSE.

show abstract

“…In this section, an adaptive switch controller is proposed for the nonlinear MIMO system (1). e controller consists of the neural network APID and neural network IAC approach.…”

Section: Adaptive Switch Controller Structurementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Neural Adaptive PID and Neural Indirect Adaptive Control Switch Controller for Nonlinear MIMO Systems

Slama

Errachdi

Benrejeb

2019

Mathematical Problems in Engineering

View full text Add to dashboard Cite

show abstract

“…However, they have some drawbacks. Indeed, parameters tuning these controllers are important to ensure their effectiveness [10]. In this sense, different methods were proposed to optimize the PI parameters such as Ghazali et al improved in [11] the performance of an upper-limb prosthetic using a fuzzy-PID controller.…”

Section: Introductionmentioning

confidence: 99%

“…In the literature, some researchers show that the hand writing on the plane (x, y) can be modeled only from two forearm muscle activities, named "Abductor Pollicis Longus" and "Extensor Capri Ulnaris." e first assures vertical movement, and the second assures horizontal displacement [10,13,14].…”

Section: Introductionmentioning

confidence: 99%

Adaptive Control Design for Human Handwriting Process Based on Electromyography Signals

2020

View full text Add to dashboard Cite

The most used control approaches of hand prosthesis are based on the forearm muscle activities, named ElectroMyoGraphy signal (EMG). In this sense, researchers modeled the hand writing on the plane only from two EMG signals. Based on this analysis, we can consider the hand as a robot with two arms (two degrees of freedom) moving on (x, y) plane. However, these signals are very sensitive to many disturbances and are generally unpredictable in time, type, and level. Based on forearm EMG signals, this work aims to propose an adaptive hand-robot control design to generate handwriting. As a first step, we develop the application of the classic proportional integral structure (PI). The PI controller was applied to generate different essays of handwritten graphic traces in one-writer case and multiwriter case. Both cases have presented unsatisfactory results in generating cursive letters and forms. Indeed, we propose, as a second approach, an adaptive PI controller with varying Integral Ki gain, according to EMG signals, in order to deal with operation changes.

show abstract

Direct tuning method of gain‐scheduled controllers with the sparse polynomials function

Yahagi

Kajiwara

2021

Asian Journal of Control

View full text Add to dashboard Cite

In industry, gain‐scheduled proportional‐integral‐derivative (PID) control is performed for nonlinear systems using a look‐up table (LUT) that is easy to understand. Compared with the fixed PID, there are many more parameters of the scheduler, and it takes a lot of time to tune them. Also, the ROM storage area increases. To address these problems, in this paper, we propose a gain‐scheduled control law using the sparse polynomial functions and a direct parameter tuning method without system identification. The polynomial functions are used instead of LUT to reduce the ROM area. For direct tuning, data‐driven control is formulated so that it can be applied to the gain‐scheduled control, and the optimal parameters are obtained by the LASSO regression, with which the small contributing parameters of the scheduler become zero, and a sparse controller is obtained. The effectiveness of this method was examined by simulation for two types of nonlinear systems. As a result, it was revealed that a sparse controller with a low calculation cost and a reduced ROM area can be directly obtained without knowing the characteristics of the controlled object for a large number of control parameters of the gain scheduler.

show abstract

Adaptive PID Controller Using RLS for SISO Stable and Unstable Systems

Cited by 21 publications

References 10 publications

Neural Adaptive PID and Neural Indirect Adaptive Control Switch Controller for Nonlinear MIMO Systems

Neural Adaptive PID and Neural Indirect Adaptive Control Switch Controller for Nonlinear MIMO Systems

Adaptive Control Design for Human Handwriting Process Based on Electromyography Signals

Direct tuning method of gain‐scheduled controllers with the sparse polynomials function

Contact Info

Product

Resources

About