Temperature Control Of A Water Bath Process: A Comparative Study Between Neuro-Control, A Self-Tuning Adaptive Control, A Generalised Predictive Control, And A Conventional Feedback Control Approach

1991 ◽  
pp. 14-33
Author(s):  
Marzuki Khalid ◽  
Rubiyah Yusof ◽  
Sigeru Omatu
Keyword(s):  
Neural Networks ◽  
Adaptive Control ◽  
Feedback Control ◽  
Predictive Control ◽  
Temperature Control ◽  
Water Bath ◽  
The Other ◽  
Control Technique ◽  
Control Approach ◽  
Self Tuning

Currently, neural networks are being used to solve problem related to control. One way to determine the reliability of the neuro-control technique is to test it on a variety of realistic problems, and to compare directly with existing traditional control technique, to see whether it works well and where it needs further refinement. In this article, we compare the neuro-control approach to a self-tuning adaptive control approach, a generalised predictive control approach, and a conventional feedback control approach on a real-time process control system. The neuro-control scheme consists of a backpropagation through time utility where two neural networks are trained one as an emulator, and the other as a controller. The four systems are compared conceptually and through experimental studies on the same single-input single-output water bath temperature control process. Comparisons, where applicable, are made with respect to methodology, system tracking performance, speed of adaptation, disturbance rejection, effect of long time-delay, and noise rejection. The results show that the neural network controller performs very well and offers encouraging advantages in many aspects over the other three controllers.

scholarly journals A novel self-tuning fractional order PID control based on optimal model reference adaptive system

2019 ◽  
Vol 10 (1) ◽  
pp. 230
Author(s):  
Mohamed Abdelbar Shamseldin ◽  
Mohamed Sallam ◽  
Abdel Halim Bassiuny ◽  
A. M. Abdel Ghany
Keyword(s):  
Fractional Order ◽  
Load Frequency Control ◽  
The Other ◽  
Control Technique ◽  
Order System ◽  
Optimal Model ◽  
Model Reference ◽  
Self Tuning ◽  
Fractional Order Pid ◽  
Model Reference Adaptive

<span>This paper presents a novel self-tuning fractional order PID (FOPID) control based on optimal Model Reference Adaptive Control (MRAC). The proposed control technique has subjected to a third order system case study (power system load frequency control). The model reference describes the requirements of designer. It can be first or second order system. The parameters of MRAC have obtained using the harmony search (HS) optimization technique to achieve the optimal performance. Sometimes, the tuning of the five parameters of FOPID control online at same moment consumes more calculation time and more processing. So, this study proposes three methods for self-tuning FOPID control. The first method has been implemented to tune the two integral and derivative parameters only and the rest of parameters are fixed. The second method has been designed to adjust the proportional, integral derivative parameters while the other fractional parameters are constant. The last method has developed to adjust the five parameters of FOPID control simultaneously. The simulation results illustrate that the third method of self-tuning FOPID control can accommodate the sudden disturbance compared to other techniques. Also, it can absorb the system uncertainty better than the other control techniques.</span>

Adaptive Control Scheme for Grid Tied SPV System with DSTATCOM Capabilities: Review

2021 ◽  
Vol 23 (07) ◽  
pp. 678-689
Author(s):  
Bilal Ahmad Ganie ◽  
◽  
Dr. (Mrs.) Lini Mathew ◽  
Keyword(s):  
Adaptive Control ◽  
Stage Structure ◽  
Voltage Regulation ◽  
Control Technique ◽  
Voltage Source ◽  
Voltage Source Converter ◽  
Current Harmonics ◽  
Control Approach ◽  
Photovoltaic Array ◽  
Static Compensator

This study provides an adaptive control approach of VSC (voltage source converter) coupled with SPV (solar photovoltaic array), in a 3P3W (three-phase three-wire) system with three single-phase non-linear loads having Distributed Static Compensator (DSTATCOM) abilities using P and O (perturb & observe) methodology. The adaptive control technique converges quickly and has a low mean square error. For the correction of power factor and zero voltage regulation modes, the system is studied and simulated. The system’s great efficacy at high voltages is due to its one-stage structure. Grid current harmonics are significantly below the IEEE-519 norm. The suggested system is modeled and simulated with the available sim power system toolbox in MATLAB/Simulink, and the system’s behavior under different loads and environmental circumstances is confirmed.

scholarly journals Stability of fractional order fuzzy cellular neural networks with distributed delays via hybrid feedback controllers

2021 ◽  
Author(s):  
Ajendra Sing ◽  
Jitendra Nath Rai
Keyword(s):  
Neural Networks ◽  
Feedback Control ◽  
Fractional Order ◽  
Sufficient Conditions ◽  
Cellular Neural Networks ◽  
Control Technique ◽  
Fuzzy Cellular Neural Networks ◽  
Feedback Controllers ◽  
Lyapunov Approach ◽  
Analysis Techniques

Abstract This article studies the Global Mittag-Leffler stability of fractional order fuzzy cellular neural networks via hybrid feedback controllers. Based on hybrid feedback control technique Lyapunov approach, and some novel analysis techniques of fractional calculation, some sufficient conditions are obtained to guarantee the Global Mittag-lefflers stability. Finally, two simulation example are given to illustrate the effectiveness of the proposed method.

scholarly journals Model Predictive Control of Robotic Grinding Based on Deep Belief Network

Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-12
Author(s):  
Shouyan Chen ◽  
Tie Zhang ◽  
Yanbiao Zou ◽  
Meng Xiao
Keyword(s):  
Feedback Control ◽  
Model Predictive Control ◽  
Predictive Control ◽  
Model Simulation ◽  
Deep Belief Network ◽  
Model Parameters ◽  
Feed Forward ◽  
Belief Network ◽  
Control Approach ◽  
Robotic Grinding

Considering the influence of rigid-flexible dynamics on robotic grinding process, a model predictive control approach based on deep belief network (DBN) is proposed to control robotic grinding deformation. The rigid-flexible coupling dynamics of robotic grinding is first established, on the basis of which a robotic grinding prediction model is constructed to predict the change of robotic grinding status and perform feed-forward control. A rolling optimization formula derived from the energy function is also established to optimize control output in real time and perform feedback control. As the accurately model parameters are hard to obtain, a deep belief network is constructed to obtain the parameters of robotic grinding predictive model. Simulation and experimental results indicate that the proposed model predictive control approach can predict abrupt change of robotic grinding status caused by deformation and perform a feed-forward and feedback based combination control, reducing control overflow and system oscillation caused by inaccurate feedback control.

scholarly journals Handling Model and Implementation Uncertainties via an Adaptive Discrete Sliding Mode Controller Design

2016 ◽  
Author(s):  
Mohammad Reza Amini ◽  
Mahdi Shahbakhti ◽  
Selina Pan ◽  
J. Karl Hedrick
Keyword(s):  
Adaptive Control ◽  
Controller Design ◽  
Sliding Mode ◽  
Electronic Control Unit ◽  
Control Technique ◽  
Propagation Mechanism ◽  
Sliding Mode Controller ◽  
Model Uncertainties ◽  
Control Approach ◽  
The Impact

Analog-to-digital conversion (ADC) and uncertainties in modeling the plant dynamics are the main sources of imprecisions in the design cycle of model-based controllers. These implementation and model uncertainties should be addressed in the early stages of the controller design, otherwise they could lead to failure in the controller performance and consequently increase the time and cost required for completing the controller verification and validation (V&V) with more iterative loops. In this paper, a new control approach is developed based on a nonlinear discrete sliding mode controller (DSMC) formulation to mitigate the ADC imprecisions and model uncertainties. To this end, a DSMC design is developed against implementation imprecisions by incorporating the knowledge of ADC uncertainties on control inputs via an online uncertainty prediction and propagation mechanism. Next, a generic online adaptive law will be derived to compensate for the impact of an unknown parameter in the controller equations that is assumed to represent the model uncertainty. The final proposed controller is an integrated adaptive DSMC with robustness to implementation and model uncertainties that includes (i) an online ADC uncertainty mechanism, and (ii) an online adaptation law. The proposed adaptive control approach is evaluated on a nonlinear automotive engine control problem in real-time using a processor-in-the-loop (PIL) setup with an actual electronic control unit (ECU). The results reveal that the proposed adaptive control technique removes the uncertainty in the model fast, and significantly improves the robustness of the controllers to ADC imprecisions. This provides up to 60% improvement in the performance of the controller under implementation and model uncertainties compared to a baseline DSMC, in which there are no incorporated ADC imprecisions.

scholarly journals Synchronization of Discontinuous Neural Networks with Delays via Adaptive Control

2013 ◽  
Vol 2013 ◽  
pp. 1-9 ◽  
Author(s):  
Xinsong Yang ◽  
Jinde Cao
Keyword(s):  
Neural Networks ◽  
Adaptive Control ◽  
Control Technique ◽  
Activation Functions ◽  
Complete Synchronization ◽  
Delayed Neural Networks ◽  
Discontinuous Activation ◽  
Discontinuous Activations ◽  
Discontinuous Activation Functions ◽  
Continuous Neural Networks

The drive-response synchronization of delayed neural networks with discontinuous activation functions is investigated via adaptive control. The synchronization of this paper means that the synchronization error approaches to zero for almost all time as time goes to infinity. The discontinuous activation functions are assumed to be monotone increasing which can be unbounded. Due to the mild condition on the discontinuous activations, adaptive control technique is utilized to control the response system. Under the framework of Filippov solution, by using Lyapunov function and chain rule of differential inclusion, rigorous proofs are given to show that adaptive control can realize complete synchronization of the considered model. The results of this paper are also applicable to continuous neural networks, since continuous function is a special case of discontinuous function. Numerical simulations verify the effectiveness of the theoretical results. Moreover, when there are parameter mismatches between drive and response neural networks with discontinuous activations, numerical example is also presented to demonstrate the complete synchronization by using discontinuous adaptive control.

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