Fuzzy Internal Model Control Research Articles

Robust output regulation for unknown nonlinear strict‐feedback systems with input delay

AbstractThis article investigates the robust output regulation problem for unknown nonlinear strict‐feedback systems with input delay. Nonlinear fuzzy internal model and adaptive fuzzy backstepping control are integrated to obtain a feedforward‐feedback composite controller. As a distinctive feature, Pade approximation technique is introduced to deal with the input delay of nonlinear output regulation problem for the first time. The process of control strategy can be divided into two steps. In the first step, a novel nonlinear fuzzy internal model is designed to get the feedforward compensator. In the second step, an adaptive fuzzy backstepping control scheme is proposed to get the feedback controller, which can guarantee semiglobally uniformly ultimately bounded of the closed‐loop system. Simulation results of an interconnected power system show the validity of the theoretical results.

Fuzzy rule-based auto-tuned internal model controller for real-time experimentation on temperature and level processes

Recently, internal model control (IMC) technique has been widely employed for various industrial close-loop control applications. Rewarding feature of IMC controller is that we need to tune only one parameter λ (close-loop time constant) for achieving the desired close-loop response. But, finding an appropriate value of λ is not an easy task. From the basic behaviour of IMC-based close-loop responses, it is found that when the process variable is moving very fast towards the desired value, relatively larger value of λ (smooth control) is required to reduce the possible oscillations. In contrary, smaller value of λ (tight control) is preferred when the process response is quickly moving away from the set point to restrict its further deviation. Hence, to mitigate the limitation of conventional IMC tuning with a fixed λ, a fuzzy rule-based auto-tuning scheme is proposed here for IMC-proportional integral derivative (IMC-PID) controller and its performance is validated through real-time experimentation on temperature and level control loops.

Two fuzzy internal model control methods for nonlinear uncertain systems

PurposeThe purpose of this paper is to use the internal model control to deal with nonlinear stable systems affected by parametric uncertainties.Design/methodology/approachThe dynamics of a considered system are approximated by a Takagi-Sugeno fuzzy model. The parameters of the fuzzy rules premises are determined manually. However, the parameters of the fuzzy rules conclusions are updated using the descent gradient method under inequality constraints in order to ensure the stability of each local model. In fact, without making these constraints the training algorithm can procure one or several unstable local models even if the desired accuracy in the training step is achieved. The considered robust control approach is the internal model. It is synthesized based on the Takagi-Sugeno fuzzy model. Two control strategies are considered. The first one is based on the parallel distribution compensation principle. It consists in associating an internal model control for each local model. However, for the second strategy, the control law is computed based on the global Takagi-Sugeno fuzzy model.FindingsAccording to the simulation results, the stability of all local models is obtained and the proposed fuzzy internal model control approaches ensure robustness against parametric uncertainties.Originality/valueThis paper introduces a method for the identification of fuzzy model parameters ensuring the stability of all local models. Using the resulting fuzzy model, two fuzzy internal model control designs are presented.

Adaptive Fuzzy Hysteresis Internal Model Tracking Control of Piezoelectric Actuators With Nanoscale Application

In this paper, a novel Takagi–Sugeno (T–S) fuzzy-system-based model is proposed for hysteresis in piezoelectric actuators. The antecedent and consequent structures of the developed fuzzy hysteresis model (FHM) can be identified online through uniform partition approach and recursive least squares (RLS) algorithm, respectively. With respect to the controller design, the inverse of FHM is used to develop a fuzzy internal model (FIM) controller. Decreasing the hysteresis effect, the FIM controller has a good performance of high-speed trajectory tracking. To achieve nanometer-scale tracking precision, the novel fuzzy adaptive internal model (FAIM) controller is uniquely developed. Based on real-time input and output data to update FHM, the FAIM controller is capable of compensating for the hysteresis effect of the piezoelectric actuator in real time. Finally, the experimental results for two cases are shown: the first is with 50 Hz and the other with multiple-frequency (50 + 25 Hz) sinusoidal trajectories tracking that demonstrate the efficiency of the proposed controllers. Especially, being 0.32% of the maximum desired displacement, the maximum error of 50-Hz sinusoidal tracking is greatly reduced to 6 nm. This result clearly indicates the nanometer-scale tracking performance of the novel FAIM controller.

Comparative analysis of modern control and AI-based control for maintaining constant ambient temperature

The objective of this paper is to study various control techniques in order to maintain constant ambient temperature by controlling the position of gas solenoid valve and expansion valve that control the volume of gas flow to the burner and volume of cool air flow into the room. In order to control the room temperature conventional P, PI, PID, model reference control (MRC), fuzzy logic control (FLC) and internal model control (IMC) are designed and implemented. The comparative study of these controllers is presented in order to identify the suitable controller.

The Design of Combustion Control System Based on Fuzzy Immune Internal Model Controller

The controlled object of boiler combustion system in power plant is a complex system with nonlinear, timing change, large lagging and multi-variable coupling, and does not have precise mathematical model, so it is difficult to obtain the satisfactory control effect adopting the traditional PID control. Advanced control strategies are adopted to improve the performance of the boiler combustion control system, and it has been more and more the concern of the majority of electricity production enterprises. Internal model control is a very practical control method, and its main characteristic is simple structure, intuitive design and few online adjustment parameter, and easy adjustment policy. And it is especially particularly significant to improve the control effect of large delay system. The internal model control system is used in power station boiler combustion system, it can effectively solve the large delay, large inertia and other shortcomings, but there is the contradiction between the fast response and robustness in internal model control system. The fuzzy immune control has advantages, such as, fast response, fast stable and good robust, etc. The fuzzy immune control is introduced into internal model control system, this paper designs fuzzy immune internal model controller, which integrates speed and robustness of the internal model control. The fuzzy immune internal model control is applied to combustion control system, and we compare it with ordinary internal model control method. The simulation result shows that fuzzy immune internal model control can greatly improve the characteristics of the control system with time delay. And this effectiveness of the fuzzy immune internal model controller has been verified.

Application of practical fuzzy arithmetic to fuzzy internal model control

This paper applies a new fuzzy arithmetic of interval calculus and fuzzy quantities to automatic control. Practical results are obtained which overcome those based on the extension principle or α-cuts. The proposed approach is based on a different representation of fuzzy numbers, though most common arithmetic operators cannot be directly applied for designing a fuzzy controller due to the unjustified overestimation effect. To avoid this phenomenon, a procedure based on an “exact” resolution calculus is proposed, whose solutions allow creating a fuzzy internal model control scheme. The validity of the new method is illustrated by a real-time educational engineering application on classical control design: a coupled tanks system.

Application of Fuzzy Internal Model Control in Grinding System

According to the complex characteristics of grinding process, a method of fuzzy internal model control (FIMC) is put forward. By adjusting the filter parameters t in internal model control on line, the problem that the control performance is poor in the condition of internal model mismatching, and through simulation, it shows that this method has short adjusting time, small overshoot, and also is feasible and effective.

Adaptive fuzzy internal model control of thermal heating process

An adaptive fuzzy internal model control (IMC) scheme is proposed to control nonlinear systems with inherent time delay. A Takagi-Sugeno (TS) fuzzy modeling with adaptive tuner is used to obtain the plant model. TS model is obtained using experimental data of step responses at different input levels and the model accuracy is improved using the normalized least mean squares (nLMS) based online adaptive tuner. Adaptive inverse of the plant is established using adaptive inverse control scheme on TS model with adaptive tuner. The scheme is implemented on a laboratory scale heating process. The experimental results indicate that the heating process output tracks the reference signal.

Wireless LANs: an advanced control system for efficient power saving

This paper proposes a novel radio modem architecture for wireless telecommunication networks. The main focus is to reduce the power consumption, which is a crucial requirement for portable devices. In particular, the paper describes a new control architecture which is in charge of properly adapting the transmission power to the link's condition by controlling the transmission bit rate and the capacity request scheme. By using fuzzy logic and internal model control, the scheme manages to adapt the bit rate, granting at the same time the reduction of the power consumption and the satisfaction of the Quality of Service requirements.

MIMO fuzzy internal model control

Model-based controllers are now beginning to gain widespread acceptance in industry. However, the majority of these controllers are based on linear models and performance in controlling the non-linear processes common in the chemical industry is sub-optimal. The use of a non-linear model could yield significant improvements in control performance. In this study a relational model from a fuzzy input space to a crisp output space is constructed by applying a least-squares identification technique to past process data. This model is termed a crisp-consequent fuzzy relational model (ccFRM) and is capable of giving an accurate representation of a non-linear system. A novel inversion method is presented which allows the ccFRM to be inverted and used within the well-known IMC structure. This new controller is termed a fuzzy internal model controller (FIMC) and test results are presented showing the FIMC performing both servo and regulatory action on a multi-variable simulated pH system. This process is extremely non-linear and exhibits severe interaction effects and is consequently a very difficult system to control. The simulation is introduced in detail, as are the tests carried out, and the performance of the FIMC in these tests is found to be encouraging.

Fuzzy adaptive internal model control

In this paper, a fuzzy adaptive internal model controller (FAIMC) for open-loop stable plants is presented. The control scheme consists of two parts: a fuzzy dynamic model and a model-based fuzzy controller. A fuzzy dynamic model, which serves as the internal model of the FAIMC is identified online by using the input and output measurement of the plant. Based on the identified fuzzy model, the fuzzy controller is designed to minimize an H/sub 2/ performance objective. This FAIMC scheme has been successfully applied to control the flow rate in a laboratory-scale process control unit from Bytronic. The experimental results demonstrate that this class of control scheme is appropriate for control of time-varying stable plants with time delay. The control system is also shown to possess satisfactory robust performance.