Indirect Adaptive Fuzzy Control Research Articles

Indirect adaptive fuzzy finite time synergetic control for power systems

Introduction. Budget constraints in a world ravenous for electrical power have led utility companies to operate generating stations with full power and sometimes at the limit of stability. In such drastic conditions the occurrence of any contingency or disturbance may lead to a critical situation starting with poorly damped oscillations followed by loss of synchronism and power system instability. In the past decades, the utilization of supplementary excitation control signals for improving power system stability has received much attention. Power system stabilizers (PSS) are used to generate supplementary control signals for the excitation system in order to damp low-frequency oscillations caused by load disturbances or short-circuit faults. Problem. Adaptive power system stabilizers have been proposed to adequately deal with a wide range of operating conditions, but they suffer from the major drawback of requiring parameter model identification, state observation and on-line feedback gain computation. Power systems are nonlinear systems, with configurations and parameters that fluctuate with time that which require a fully nonlinear model and an adaptive control scheme for a practical operating environment. A new nonlinear adaptive fuzzy approach based on synergetic control theory which has been developed for nonlinear power system stabilizers to overcome above mentioned problems. Aim. Synergetic control theory has been successfully applied in the design of power system stabilizers is a most promising robust control technique relying on the same principle of invariance found in sliding mode control, but without its chattering drawback. In most of its applications, synergetic control law was designed based on an asymptotic stability analysis and the system trajectories evolve to a specified attractor reaching the equilibrium in an infinite time. In this paper an indirect finite time adaptive fuzzy synergetic power system stabilizer for damping local and inter-area modes of oscillations for power systems is presented. Methodology. The proposed controller design is based on an adaptive fuzzy control combining a synergetic control theory with a finite-time attractor and Lyapunov synthesis. Enhancing existing adaptive fuzzy synergetic power system stabilizer, where fuzzy systems are used to approximate unknown system dynamics and robust synergetic control for only providing asymptotic stability of the closed-loop system, the proposed technique procures finite time convergence property in the derivation of the continuous synergetic control law. Analytical proofs for finite time convergence are presented confirming that the proposed adaptive scheme can guarantee that system signals are bounded and finite time stability obtained. Results. The performance of the proposed stabilizer is evaluated for a single machine infinite bus system and for a multi machine power system under different type of disturbances. Simulation results are compared to those obtained with a conventional adaptive fuzzy synergetic controller.

MIMO fuzzy adaptive control systems based on fuzzy semi-tensor product

<abstract><p>Based on fuzzy semi-tensor product (STP) algorithms and fuzzy relation matrix (FRM) models, the design of an adaptive fuzzy controller was proposed in this paper for the multivariable nonlinear systems with uncertainty. The controlled multi-input-and-multi-output (MIMO) plants were expressed and processed first by FRM models and fuzzy STP operations, and then the indirect adaptive fuzzy control laws were designed. The tracking property of the FRM models was proved for the control objective of MIMO systems. The effectiveness of the novel matrix expression was verified by simulations of the tracking control on a two-link rigid robot manipulator. Results in simulation tests show that the proposed design of adaptive FRM control is efficient for nonlinear multivariables. Therefore, the proposed indirect fuzzy adaptive controllers can be extended to general matrix expression for MIMO nonlinear systems with fuzzy STP algorithms and FRM models and online approximate unknown parameters, according to required accuracy.</p></abstract>

Indirect adaptive fuzzy control with a new control input transformation

In this paper, a novel indirect adaptive fuzzy controller is proposed for nonlinear systems with a low-pass filter. Firstly, the system is transformed into a simple form with only two unknown nonlinear functions by the filter. Since the states of converted system are difficult to calculate, a state observer is used to estimate them. Secondly, a new control law is designed based on the adaptive laws for estimating the parameters of the controller and two fuzzy systems for approximating the nonlinear functions of the transformed system. Then, the stability and convergence of the closed-loop system are proved in the Lyapunov sense. Finally, simulation results obtained for a practical example show the feasibility, effectiveness of the designed scheme.

Maximum power point tracking in photovoltaic systems using indirect adaptive fuzzy robust controller

Maximum power point tracking in photovoltaic systems using indirect adaptive fuzzy robust controller

Direct Adaptive Fuzzy Control Scheme With Guaranteed Tracking Performances for Uncertain Canonical Nonlinear Systems

In our recent work, we propose an indirect adaptive fuzzy control scheme for uncertain unparametrizable nonlinear systems, which ensures that the number of adaptive laws does not increase with the number of fuzzy rules, and the time derivative of the chosen Lyapunov function is negative semidefinite. However, the scheme involves a class of high-order smooth functions and their time derivatives, which can make the controller structure become sophisticated especially when the relative degree of system is quite large. To overcome this problem, in the article, we propose a new direct adaptive fuzzy control scheme based on a class of reduced-order smooth functions. With the scheme, no partial-derivative term is involved in controller and virtual controllers, only one adaptive law is used regardless of the increase of fuzzy rules, and also the time derivative of Lyapunov function can be ensured negative semidefinite. It is proved that all closed-loop signals are bounded, and the output tracking error converges to a prescribed interval asymptotically. The transient tracking performance and robustness of the proposed control scheme are also considered. The effectiveness of the obtained results is illustrated by two practical control systems.

Combined indirect & direct adaptive fuzzy decentralised control of large-scale systems with strong interconnection and its application

To achieve better adaptation properties, the proposed controller is a weighted combination of indirect and direct adaptive fuzzy controls such that both fuzzy descriptions and control rules can be incorporated under a united controller design at the same time. By using fuzzy systems and robust control technique, the interconnections between subsystems are extended to general unknown nonlinear functions. No a priori knowledge of lower and upper bounds on lumped uncertainties is required to construct each local controller. The resulting closed-loop large-scale system is proved to be asymptotically stable. The controller design is applicable to an automated highway system, and simulation results confirm its practical usefulness.

Designing of a Control Approach for Uncertain Fractional Order Systems with Indirect Adaptive Fuzzy Controller and Frational Order Sliding Mode

Designing of a Control Approach for Uncertain Fractional Order Systems with Indirect Adaptive Fuzzy Controller and Frational Order Sliding Mode

Optimal fractional-order adaptive fuzzy control on inverted pendulum model

This paper shows the ability of Fractional-Order (FO) dynamics in adaptation laws of indirect adaptive fuzzy logic control (AFLC). The parameters of the FO-adaptive laws are optimized using Particle Swarm Optimization algorithm without the presence of the disturbance. The optimal FO-AFLC (OFO-AFLC) is introduced in this paper to overcome the drawbacks of AFLC. The complexity in AFLC is due to using the projection algorithm to keep the adapted parameters bounded. Also the AFLC uses a complex control to guarantee the error convergence. It has proven that the OFO-AFLC can guarantee the error convergence without using neither the projection algorithm nor complex control. The applicability and efficiency of the proposed method is compared to the ordinary AFLC, and demonstrated through simulations made on inverted pendulum model. Furthermore, simulation results show that the control signal in OFO-AFLC is smoother with less oscillations.

Indirect Adaptive Fuzzy Control for Nonaffine Nonlinear Pure-Feedback Systems

In this article, an indirect adaptive fuzzy control scheme is proposed for single-input, single-output (SISO) nonaffine nonlinear systems in a pure-feedback form. In this scheme, the unknown nonlinear functions are approximated by Takagi–Sugeno (T–S) fuzzy logic systems with an state observer. The adaptive laws are developed to adjust the parameters of the fuzzy systems. The stability of the closed-loop system is proved with all state variables being uniformly bounded in the Lyapunov sense and the tracking error is confirmed to zero asymptotically. Finally, simulation results obtained for two practical examples show the feasibility and effectiveness of the proposed scheme.

Synchronization Adaptive Fuzzy Gain Scheduling PID Controller for a Class of MIMO Nonlinear Systems

This paper presents a new Synchronization Adaptive Fuzzy Gain Scheduling PID controller (SAFGS-PIDc) for a class of Multiple-input multiple-output (MIMO) nonlinear systems with uncertainties. To achieve better adaptation properties, weighting factor is adapted to sum together the adaptive fuzzy control scheme and Fuzzy Gain Scheduler PID control (FGS-PIDc) method, such that both controllers can be incorporated at the same time. The FGS adjusts online the parameters of the conventional PID controller, and an Indirect Adaptive Fuzzy control (IAFc) scheme that uses feedback error function as inputs is constructed. In addition, each subsystem of MIMO system is able to adaptively compensate for uncertainties and external disturbance. Also, a robust control term is designed that aims to provide added robustness in the presence of uncertainties. The proposed scheme can overcome the controller singularity problem. While the proposed controller scheme requires the uncertainties to be bounded, it does not require this bound to be known. Thus, this control law can be used for the systems that the system’s models are quite unknown. The proposed method guarantees the stability of the closed-loop system based on Lyapunov theory. Finally, simulation studies demonstrate the usefulness and effectiveness of the proposed technique for controlling nonlinear.

Indirect adaptive fuzzy control of nonlinear descriptor systems

This paper focuses on indirect adaptive fuzzy control of nonlinear descriptor systems described by both uncertain algebraic and differential equations aiming to guarantee asymptotic tracking of a regular and impulse-free descriptor reference model. The proposed controller exploits the universal approximation capability of Takagi–Sugeno–Kang (TSK) fuzzy models for the identification of the unknown system dynamics. More specifically, it is assumed that only the system order is known while all the dynamical equations of the system are completely unknown. In the proposed method, the asymptotic tracking of the reference model is guaranteed by suitable adaptation laws for the parameters of the TSK fuzzy model. Simulation results are presented to demonstrate the effectiveness of the proposed method.

Indirect adaptive fuzzy control of non-linear systems using fuzzy supervisory term

Indirect adaptive fuzzy control of non-linear systems using fuzzy supervisory term

Indirect adaptive fuzzy control of non-linear systems using fuzzy supervisory term

This paper focuses on the indirect adaptive fuzzy control of Single Input Single Output (SISO) non-linear systems with unknown linearities. The proposed adaptive fuzzy controller is based on feedback linearisation. Its parameters are updated online according to some adaptive laws such as tracking error-based method, composite tracking and modelling error-based approach as well as filtered composite tracking and modelling error-based approach. However, the approximation error introduced into feedback loop increases the difficulty to guarantee the stability of the closed-loop control system. To solve this problem a supervisory term should be added to the control law. In this paper, a fuzzy supervisory control is proposed to overcome the problem of chattering phenomena introduced by the classical supervisory term. Theoretical and simulation results prove the effectiveness of the proposed approaches. Faster and improved tracking is obtained by using second and third aforementioned adaptive laws combined with fuzzy supervisory control.

Indirect Adaptive Fuzzy Control of SISO Nonlinear Systems With Input–Output Nonlinear Relationship

In this paper, a design method of indirect adaptive fuzzy controller is proposed for a class of single-input single-output (SISO) nonlinear systems with input–output nonlinear relationship. In this method, fuzzy systems are utilized to approximate unknown nonlinear functions, and a state observer is designed to estimate the unmeasured state. Under the given constraint conditions of the system parameters, the adaptive law is given. Meanwhile, the stability of the closed-loop system is proven with all state variables being uniformly bounded in the Lyapunov sense. Moreover, the convergence of the fuzzy control system is analyzed. The simulation results show the feasibility, effectiveness, and universality of the designed method.

Indirect adaptive fuzzy fault-tolerant tracking control for MIMO nonlinear systems with actuator and sensor failures

In this paper, an active fuzzy fault tolerant tracking control (AFFTTC) scheme is developed for a class of multi-input multi-output (MIMO) unknown nonlinear systems in the presence of unknown actuator faults, sensor failures and external disturbance. The developed control scheme deals with four kinds of faults for both sensors and actuators. The bias, drift, and loss of accuracy additive faults are considered along with the loss of effectiveness multiplicative fault. A fuzzy adaptive controller based on back-stepping design is developed to deal with actuator failures and unknown system dynamics. However, an additional robust control term is added to deal with sensor faults, approximation errors, and external disturbances. Lyapunov theory is used to prove the stability of the closed loop system. Numerical simulations on a quadrotor are presented to show the effectiveness of the proposed approach.

Indirect adaptive type-2 bionic fuzzy control

In this study, an indirect adaptive type-2 bionic fuzzy control method is proposed for a class of nonlinear systems. By regarding the niche of each species in an ecosystem as the antecedent, the fuzzy system with biological characteristics is constructed based on the niche “ecostate-ecorole” theory. In the actual system, we design a fuzzy control system using a type-2 bionic fuzzy system and provide both the adaptive law and constraint conditions of the system parameters. The stability of the closed-loop system is proved with all the state variables uniformly bounded in the Lyapunov sense. Additionally, the convergence of the bionic fuzzy control system is analyzed. Finally, the simulation results obtained for a permanent magnet direct current motor demonstrate the effectiveness and superiority of the designed method.

Stable indirect adaptive interval type-2 fuzzy sliding-based control and synchronization of two different chaotic systems

An indirect approach to adaptive interval type-2 fuzzy sliding mode control is proposed for the stable synchronization of two different chaotic nonlinear systems with different initial conditions under the presence of uncertainties involving process noises and external disturbances. The indirect model-based approach to adaptation is promoted here as a more suitable strategy for the fast changes that occurs in chaotic systems. In other words, the usual direct adaptive strategies may be too slow to respond to the inherently fast changing dynamics of chaotic systems. Using Lyapunov analysis, the sliding mode approach illustrates the asymptotic convergence of synchronization error to zero as well as good robustness against external disturbances. The interval type-2 structure aims to remedy the undesirable chattering phenomenon that is common in most conventional sliding mode control applications. It also provides a more effective equivalent model in the indirect approach, which leads to improved handling of the chaotic variations and uncertainties. Two numerical pairs of chaotic systems, i.e. the Lorenz and Chen’s systems and the Rössler system and modified Chua’s circuit are considered. In particular, in comparison with its type-1 fuzzy counterpart, the control effort is reduced by an average of 26.25% and 17.4% for the synchronization of the two corresponding systems, respectively. Furthermore, the integral of squared error is also improved by an average of 27.2% and 25.33%. This is while convergence time is reduced to less than 0.5s and 1.5s.

Voltage Regulation System of Doubly Salient Electromagnetic Generator Based on Indirect Adaptive Fuzzy Control

In this paper, a voltage regulation system based on indirect adaptive fuzzy (IAF) control for doubly salient electromagnetic generator (DSEG) is proposed to improve the robustness of the system and reduce output voltage ripple. The operating principle of IAF controller is described in detail subsequently and the stability condition of the system is deduced by Lyapunov function based on the mathematical model and working principle of the generator. Moreover, the selection method of parameters and control strategy are determined with the adaptive fuzzy control law. Finally, a field-circuit coupling simulation model of voltage regulation system for DSEG using IAF controller is constructed by finite element analysis technique and transient solver of control circuit. The simulation results show that DSEG using IAF controller has good static characteristics, high robustness, and low output voltage ripple.

Adaptive fuzzy PD control with stable H∞ tracking guarantee

For indirect adaptive fuzzy H∞ tracking control (AFHC) of perturbed uncertain nonlinear systems, sliding-mode control (SMC) compensation usually has to be applied to ensure stability and H∞ robustness of the closed-loop system. We prove that indirect AFHC without SMC compensation is sufficient to guarantee stable H∞ tracking under given initial conditions and parameter constraints. The control structure only includes an indirect adaptive fuzzy control term and a proportional derivative (PD) control term. A certainty equivalent control law is slightly modified such that both a lumped perturbation and adaptive laws are independent of the PD control term. This modification is significant since it not only plays a key role in stability analysis, but also alleviates some drawbacks of existing AFHC approaches for practical applications. An illustrative example has been provided to verify correctness of the theoretical result.

Indirect Adaptive Control for Discrete-Time Nonlinear Systems based on T-S Fuzzy Model

The main goal of this paper is to present an indirect adaptive fuzzy control of discrete-time non affine nonlinear systems with parametric variations. The synthesis of the state feedback control law is based on the Takagi-Sugeno (T-S) fuzzy models developed by a local description of the considered system. In the first step, the model parameters locally estimated by the fuzzy model are adjusted using gradient method. In the second step, the local control gain based on pole placement is computed. After that, the global state feedback control law is applied to the nonlinear system. Based on the Lyapunov stability theory, the asymptotic stability of the proposed state feedback adaptive fuzzy control method is studied to ensure the global stability of the system. To illustrate the performance of the proposed controller, inverted pendulum and two links robot manipulator arm are presented.