Hybrid Adaptive Fuzzy Control Research Articles

Hybrid adaptive bionic fuzzy control method for MIMO systems with dead-zone input

In this research, a hybrid adaptive bionic fuzzy control strategy is developed for a class of complicated nonlinear multiple-input-multiple-output (MIMO) systems with dead-zone input. The first component of the bionic adaptive controller is a general phrase for tunning system parameters depending on the present state, and the second component is a trend-based compensation for adjusting the system parameters. This technique makes the system more intelligent and boosts its anti-interference capabilities. The stability and convergence are analyzed using the Lyapunov synthetic method, and thus the parameter restrictions of the MIMO system are provided. Finally, the strong anti-interference of the system is verified by the simulations.

Robust control of large-scale nonlinear systems by a hybrid adaptive fuzzy observer design with input saturation

In this paper a robust hybrid observer-based hybrid adaptive fuzzy controller for large-scale multiple-input multiple-output (MIMO) systems with affine and nonaffine form in the presence of input saturation is presented. Based on an arbitrary control gain matrix, a hybrid control scheme is designed to avoid the possible singularity problem. The proposed control method is very simple, effective and robust. The states of the system and the mathematical model of the system under control do not need to be well known. An observer-based adaptive fuzzy system based on strictly positive real (SPR) theory is applied to approximate the system states, but it does not require SPR conditions to be known identified. A robust control term is utilized to compensate the both uncertainties and observer errors and attenuate the residual error to the desired level. Finally, to display the applicability and efficiency of the proposed control method, a MIMO affine system and a MIMO nonaffine system with input saturation are applied in simulations.

Observer-based hybrid adaptive fuzzy control for affine and nonaffine uncertain nonlinear systems

This paper presents a novel observer-based hybrid adaptive fuzzy controller for affine and nonaffine nonlinear systems with external disturbance. The suggested design is so easy and does not need a mathematical model for system under control and also it is very simple, efficient and robust. Based on the adaptive method and the system states observer, an observer-based adaptive fuzzy method is proposed to control an uncertain nonlinear system. Also, a supervisory controller term is employed to attenuate the residual error to a desired level and compensate the both uncertainties and observer errors. Although proposed control method needs the uncertainties to be bounded, it does not need this bound to be identified. Stability of the proposed method is shown based on Lyapunov theory and also the strictly positive real condition if all the implicated signals are uniformly bounded. Finally, in our simulation studies, to demonstrate the usefulness and efficiency of the suggested technique, an uncertain nonlinear system is employed.

Development of an Hybrid Adaptive Neuro Fuzzy Controller for Surface Roughness (SR) prediction of Mild Steel during Turning

Development of an Hybrid Adaptive Neuro Fuzzy Controller for Surface Roughness (SR) prediction of Mild Steel during Turning

Hybrid adaptive fuzzy control for uncertain MIMO nonlinear systems with unknown dead-zones

In this paper, a hybrid adaptive fuzzy control approach is proposed for a class of uncertain multiple-input and multiple-output (MIMO) strict-feedback nonlinear systems with unknown dead-zones. Fuzzy logic systems are utilized to approximate the unknown nonlinear functions, and a serial-parallel estimation model is established. Based on the adaptive backstepping dynamic surface control (DSC) technique, and utilizing the bounds of the dead-zone slopes and the prediction errors between the system model and the serial-parallel estimation model, a new robust adaptive fuzzy backstepping controller with the composed parameters adaptive laws is developed. The proposed control method can not only overcome the problem of “explosion of complexity” inherently existing in traditional backstepping design methods, but also improve the control performance. It is proved that all the signals of the closed-loop systems are bounded and the system output can follow the given bounded reference signal. A numerical example and simulation comparisons with the existing control methods are provided to show the effectiveness of the proposed approach.

Design of a hybrid adaptive fuzzy and neural network controller for nonlinear laser alloying process

The laser surface alloying quality may vary significantly with respect to the process parameters variation. Hence, a feedback control system is required to monitor the operating parameters for yielding a good quality control. Since this multi-input and multi-output system has nonlinear coupling and time varying dynamic characteristics, it is very difficult to establish an accurate process model for designing model-based controller. Hence a hybrid model-free adaptive fuzzy and neural network controller HAFNC, which combines an adaptive rule with fuzzy and neural network control, is employed in this study to overcome the difficulty. It has on-line learning ability for responding to the system's nonlinear and time-varying behaviors. Since this model-free controller has simple control structure and small number of control parameters, it is easy to implement. Two HAFNC controllers are designed for tuning the laser power and the traverse velocity simultaneously to tackle the absorptivity and geometrical variations of the work pieces. Simulation results based on semi-experimental system dynamic model and parameters show that the good surface lapping performance is achieved by using this intelligent control strategy.

Harmony search algorithm and Lyapunov theory based hybrid adaptive fuzzy controller for temperature control of air heater system with transport-delay

The present paper proposes systematic designs of stable adaptive fuzzy logic controllers (AFLCs), employing hybridizations of Lyapunov strategy based approach (LSBA) and a contemporary stochastic optimization technique, for controlling the temperature of a thermal process in an air-heater system with transport-delay and in the presence of disturbances. Harmony search (HS) algorithm has been considered here as a candidate stochastic optimization technique utilized in conjunction with the Lyapunov theory to develop the hybrid models in this work. The objective of this work is to design stable adaptive fuzzy controllers which can provide high degree of automation, guarantee asymptotic stability and also achieve satisfactory transient performance by simultaneous adaptations of both the fuzzy controller structure and its free parameters. The results obtained from real-life experiments aptly demonstrate the usefulness of the proposed approach.

Robust decentralized hybrid adaptive output feedback fuzzy control for a class of large-scale MIMO nonlinear systems and its application to AHS

This paper presents a novel observer-based decentralized hybrid adaptive fuzzy control scheme for a class of large-scale continuous-time multiple-input multiple-output (MIMO) uncertain nonlinear systems whose state variables are unmeasurable. The scheme integrates fuzzy logic systems, state observers, and strictly positive real conditions to deal with three issues in the control of a large-scale MIMO uncertain nonlinear system: algorithm design, controller singularity, and transient response. Then, the design of the hybrid adaptive fuzzy controller is extended to address a general large-scale uncertain nonlinear system. It is shown that the resultant closed-loop large-scale system keeps asymptotically stable and the tracking error converges to zero. The better characteristics of our scheme are demonstrated by simulations.

GA and Lyapunov theory-based hybrid adaptive fuzzy controller for non-linear systems

In this present article, a new hybrid methodology for designing stable adaptive fuzzy logic controllers (AFLCs) for a class of non-linear system is proposed. The proposed design strategy exploits the features of genetic algorithm (GA)-based stochastic evolutionary global search technique and Lyapunov theory-based local adaptation scheme. The objective is to develop a methodology for designing AFLCs with optimised free parameters and guaranteed closed-loop stability. Simultaneously, the proposed method introduces automation in the design process. The stand-alone Lyapunov theory-based design, GA-based design and proposed hybrid GA–Lyapunov design methodologies are implemented for two benchmark non-linear plants in simulation case studies with different reference signals and one experimental case study. The results demonstrate that the hybrid design methodology outperforms the other control strategies on the whole.

Hybrid adaptive fuzzy and neural network controller for the molten steel level control in strip casting processes

The molten steel level control of strip casting process has the properties of nonlinear uncertainty and time-varying characteristics, hence, it is difficult to establish an accurate process model for designing a model-based controller to monitor the strip quality. This study develops a hybrid model-free adaptive fuzzy and neural network controller (HAFNC) which combines an adaptive rule with fuzzy and neural network control to overcome the difficulty. The proposed control strategy has online learning ability for responding to the system's nonlinear and time-varying behaviors during the molten steel level control. Since this model-free controller has simple control structure and small number of control parameters, it is easy to implement. Numerical results based on semi-experimental system dynamic model and parameters are executed to show the control performance of the proposed intelligent controller.

Hybrid Adaptive Fuzzy Control Based on the Biological Adaptation Strategies

For a class of uncertain nonlinear systems, this paper presents a method based on biological adaptation strategies of hybrid adaptive fuzzy control method. The method in the controller design, the ecological niche theory function as a consequent of the fuzzy rule fuzzy system, is established based on the biological adaptation of the adaptive controller. The controller design embodies the biological adaptability to environment, but also embodies the biological development and utilization of environmental capacity. Through an example, the controller and the controller compared to conventional, has better control effect, but also has good disturbance resistance

Real-time implementation of adaptive fuzzy hysteresis-band current control technique for shunt active power filter

Optimising the performance of power system networks using conventional methods is quite difficult because of the complex nature of systems that are highly non-linear and non-stationary. In this study a hybrid adaptive fuzzy hysteresis current controller for shunt active power filter (SAPF) is proposed. The conventional adaptive hysteresis concept is hybridised with fuzzy logic controller (FLC), which facilitates discarding of uncertainty in the system. In fact, conventional proportional-integral (PI) controllers for shunt active filter are based on a linearised model that fails to react under transient events. On the other side, FLC has widened its applicability to many engineering fields and offers satisfactory results for a wide variety of operating conditions. It helps in fulfilling the need for perfection, such as stability and robustness for every system. All this motivated to adopt FLC for SAPF applications. By incorporating an adaptive fuzzy hysteresis band, active power filter (APF) gains outstanding compensation ability under steady-state and transient conditions. To validate the proposed approach, the system is implemented on a real-time digital simulator and adequate results are reported for its verification.

The Hybrid Adaptive Control of T‐S Fuzzy System Based on Niche

Based on the niche characteristics, a hybrid adaptive fuzzy control method with the function of continuous supervisory control is proposed in this paper. Considering the close degree of Niche as the consequent of adaptive T‐S fuzzy control system, the hybrid control law is designed by tracking, continuous supervisory, and adaptive compensation. Adaptive compensator is used in the controller to compensate the approximation error of fuzzy logic system and the effect of the external disturbance. The adaptive law of consequent parameters, which is achieved in this paper, embodies system adaptability as biological individual. It is proved that all signals in the closed‐loop system are bounded and tracking error converges to zero by Lyapunov stability theory. The effectiveness of the approach is demonstrated by the simulation results.

A Survey Of Fuzzy Control For Stabilized Platforms

This paper focusses on the application of fuzzy control techniques (fuzzy type-1 and type-2) and their hybrid forms (Hybrid adaptive fuzzy controller and fuzzy-PID controller) in the area of stabilized platforms. It represents an attempt to cover the basic principles and concepts of fuzzy control in stabilization and position control, with an outline of a number of recent applications used in advanced control of stabilized platform. Overall, in this survey we will make some comparisons with the classical control techniques such us PID control to demonstrate the advantages and disadvantages of the application of fuzzy control techniques.

Formula omitted] tracking-based decentralized hybrid adaptive output feedback fuzzy control for a class of large-scale nonlinear systems

This paper proposes a novel H∞ tracking-based decentralized hybrid adaptive fuzzy controller via output feedback for a class of large-scale uncertain nonlinear systems. To achieve better adaptation properties, the proposed controller is a weighted combination of indirect and direct adaptive fuzzy controls (IAFC and DAFC) such that both fuzzy descriptions and control rules can be incorporated at the same time. After proper filtering of the observation error dynamics, the developed observer-based feedback and adaptation mechanisms become implementable indeed. By using fuzzy inference systems, a state observer and H∞ tracking technique, the decentralized combined indirect and direct adaptive fuzzy control algorithm is developed based upon a united H∞ tracking controller design for the modification of the previous work of Huang et al. The resultant closed-loop systems are guaranteed to be stable and a good H∞ tracking performance is obtained. Simulation results of interrelated inverted pendulums substantiate that the proposed CAFC system stands out as being better properties than typical IAFC and DAFC systems in that the tracking performance is much better while the control effort is even smaller.

Decentralized adaptive output feedback fuzzy controller for a class of large-scale nonlinear systems

In the previous work of Huang et al., a coordinated decentralized hybrid adaptive output feedback fuzzy control scheme of large-scale nonlinear systems is obtained predicated upon this prerequisite assumption that the local controllers can share the a priori information about their individual reference models. In this note, we concentrate in the absence of the coordination assumption on developing a classical decentralized combined indirect and direct adaptive fuzzy controller for a class of uncertain large-scale nonlinear systems. The output feedback and adaptation mechanisms proposed for each subsystem hinges just upon its individual output, regardless of any other output reference. Neither the famous strictly positive real (SPR) condition nor a high-gain observer (HGO) is required to realize the overall output feedback algorithm. The tracking errors of the closed-loop large-scale system are shown to converge to tunable neighborhoods of the origin. Simulation results on correlated inverted pendulums verify the validity of the decentralized controller modification.

Coordinated decentralised hybrid adaptive output feedback fuzzy control for a class of large-scale non-linear systems with strong interconnections

A novel decentralised hybrid adaptive fuzzy control (HAFC) scheme is developed for a class of large-scale uncertain non-linear systems in this paper. The local controllers are assumed to share the prior information about their individual reference models such that the interconnections between subsystems can be extended to arbitrary non-linearities. The presented controller is a weighted combination of both indirect and direct adaptive fuzzy controllers, and a weighting factor, which is adjusted by the tradeoff between plant knowledge and control knowledge, is adopted to total up both the control efforts. Under the proposed overall control algorithm, the input gains are relaxed from typical positive constants to non-linear functions. The tracking errors of the closed-loop large-scale system are guaranteed to converge to tunable neighbourhoods of the origin. Simulation results substantiate that the proposed decentralised HAFC systems take on smaller control efforts as well as better tracking performance.

Robust adaptive fuzzy semi‐decentralized control for a class of large‐scale nonlinear systems using input–output linearization concept

AbstractStable direct and indirect adaptive fuzzy controllers based on input–output linearization concept are presented for a class of interconnected nonlinear systems with unknown nonlinear subsystems and interconnections. The interconnected nonlinear systems are represented not only in the canonical forms as in Yousef et al. (Int. J. Robust Nonlinear Control 2006; 16: 687–708) but also in the general forms. Hybrid adaptive fuzzy robust tracking control schemes that are based on a combination of an H∞ tracking theory and fuzzy control design are developed. In the proposed control schemes, all the states and signals are bounded and an H∞ tracking control performance is guaranteed without imposing any constraints or assumptions about the interconnections. Extensive simulation on the tracking of a two‐link rigid robot manipulator and a numerical example verify the effectiveness of the proposed algorithms. Copyright © 2009 John Wiley & Sons, Ltd.

Adaptive fuzzy decentralized control for a class of large-scale nonlinear systems.

In this paper, direct and indirect adaptive output-feedback fuzzy decentralized controllers for a class of uncertain large-scale nonlinear systems are developed. The proposed controllers do not need the availability of the state variables. By designing the state observer, the adaptive fuzzy systems, which are used to model the unknown functions, can be constructed using the state estimations, and a new hybrid adaptive fuzzy control methodology is proposed by combining the adaptive fuzzy systems with H infinity control and the sliding mode control techniques. Based on Lyapunov stability theorem, the stability of the closed-loop systems can be verified. Moreover, the proposed overall control schemes guarantee that all the signals involved are bounded and achieve the H infinity-tracking performance. To demonstrate the effectiveness of the proposed methods, simulation results are illustrated in this paper.

Observer-based adaptive fuzzy control for SISO nonlinear systems

The observer-based indirect and direct adaptive fuzzy controllers are developed for a class of SISO uncertain nonlinear systems. The proposed approaches do not need the availability of the state variables. By designing the state observer, the adaptive fuzzy systems, which are used to model the unknown functions, can be constructed using the state estimations. Thus, a new hybrid adaptive fuzzy control method is proposed by combining the above adaptive fuzzy system with the H ∞ control technique. Based on Lyapunov stability theorem, the proposed adaptive fuzzy control system can guarantee the stability of the whole closed-loop systems and obtain good tracking performance as well. The proposed methods are applied to an inverted pendulum system and a chaotic system and achieve satisfactory simulation results.