Showing papers in "Intelligent Control and Automation in 2011"
TL;DR: The methods used in the design of ABS systems are reviewed, the main difficulties are highlighted and the more recent developments in their control techniques are summarized.
Abstract: Many different control methods for ABS systems have been developed. These methods differ in their theoretical basis and performance under the changes of road conditions. The present review is a part of research project entitled “Intelligent Antilock Brake System Design for Road-Surfaces of Saudi Arabia” In the present paper we review the methods used in the design of ABS systems. We highlight the main difficulties and summarize the more recent developments in their control techniques. Intelligent control systems like fuzzy control can be used in ABS control to emulate the qualitative aspects of human knowledge with several advantages such as robustness, universal approximation theorem and rule-based algorithms.
90 citations
TL;DR: A Proportional Integral Derivative (PID) controller is designed and attached to electrohydraulic servo actuator system to control its angular position and the optimal values of the feedback gains can be obtained within 10 generations.
Abstract: Electrohydraulic servosystem have been used in industry in a wide number of applications. Its dynamics are highly nonlinear and also have large extent of model uncertainties and external disturbances. In order to increase the reliability, controllability and utilizing the superior speed of response achievable from electrohydraulic systems, further research is required to develop a control software has the ability of overcoming the problems of system nonlinearities. In This paper, a Proportional Integral Derivative (PID) controller is designed and attached to electrohydraulic servo actuator system to control its angular position. The PID parameters are optimized by the Genetic Algorithm (GA). The controller is verified on the state space model of servovalve attached to a rotary actuator by SIMULINK program. The appropriate specifications of the GA for the rotary position control of an actuator system are presented. It is found that the optimal values of the feedback gains can be obtained within 10 generations, which corresponds to about 200 experiments. A new fitness function was implemented to optimize the feedback gains and its efficiency was verified for control such nonlinear servosystem.
83 citations
TL;DR: Simulation study of linear quadratic controllers for a system with an inverted pendulum on a mobile robot shows that both LQR and LQG are capable to control this system successfully.
Abstract: The objective of this paper is to design linear quadratic controllers for a system with an inverted pendulum on a mobile robot. To this goal, it has to be determined which control strategy delivers better performance with respect to pendulum’s angle and the robot’s position. The inverted pendulum represents a challenging control problem, since it continually moves toward an uncontrolled state. Simulation study has been done in MATLAB Simulink environment shows that both LQR and LQG are capable to control this system successfully. The result shows, however, that LQR produced better response compared to a LQG strategy.
48 citations
TL;DR: In this paper, the authors presented fabrication and installation of a solar panel mount with a dual-axis solar tracking controller to maximize the capture of the rays from the sun for conversion into electricity.
Abstract: The recent decades have seen the increase in demand for reliable and clean form of electricity derived from renewable energy sources. One such example is solar power. The challenge remains to maximize the capture of the rays from the sun for conversion into electricity. This paper presents fabrication and installation of a solar panel mount with a dual-axis solar tracking controller. This is done so that rays from the sun fall perpendicularly unto the solar panels to maximize the capture of the rays by pointing the solar panels towards the sun and following its path across the sky. Thus electricity and efficiency increased.
47 citations
TL;DR: Particle Swarm Optimization is found to perform better than the Genetic Algorithm, as the former carries out both global and local searches simultaneously, whereas the latter concentrates mainly on the global search.
Abstract: The performances of Particle Swarm Optimization and Genetic Algorithm have been compared to develop a methodology for concurrent and integrated design of mechanical structure and controller of a 2-dof robotic manipulator solving tracking problems. The proposed design scheme optimizes various parameters belonging to different domains (that is, link geometry, mass distribution, moment of inertia, control gains) concurrently to design manipulator, which can track some given paths accurately with a minimum power consumption. The main strength of this study lies with the design of an integrated scheme to solve the above problem. Both real-coded Genetic Algorithm and Particle Swarm Optimization are used to solve this complex optimization problem. Four approaches have been developed and their performances are compared. Particle Swarm Optimization is found to perform better than the Genetic Algorithm, as the former carries out both global and local searches simultaneously, whereas the latter concentrates mainly on the global search. Controllers with adaptive gain values have shown better performance compared to the conventional ones, as expected.
32 citations
TL;DR: An adaptive fuzzy sliding mode control (AFSMC) scheme is derived for robotic systems and has the learning ability to generate the fuzzy control actions and adaptively compensates for the uncertainties.
Abstract: In this article, an adaptive fuzzy sliding mode control (AFSMC) scheme is derived for robotic systems. In the AFSMC design, the sliding mode control (SMC) concept is combined with fuzzy control strategy to obtain a model-free fuzzy sliding mode control. The equivalent controller has been substituted for by a fuzzy system and the uncertainties are estimated on-line. The approach of the AFSMC has the learning ability to generate the fuzzy control actions and adaptively compensates for the uncertainties. Despite the high nonlinearity and coupling effects, the control input of the proposed control algorithm has been decoupled leading to a simplified control mechanism for robotic systems. Simulations have been carried out on a two link planar robot. Results show the effectiveness of the proposed control system.
29 citations
TL;DR: Two widely used controllers “PID and Fuzzy” have been implemented in the same FPGA card to examine the performance of the proposed system and showed the superiority of FBuzzy-PID.
Abstract: The design of intelligent control systems has become an area of intense research interest. The development of an effective methodology for the design of such control systems undoubtedly requires the synthesis of many concepts from artificial intelligence. The most commonly used controller in the industry field is the proportional-plus-integral-plus-derivative (PID) controller. Fuzzy logic controller (FLC) provides an alternative to PID controller, especially when the available system models are inexact or unavailable. Also rapid advances in digital technologies have given designers the option of implementing controllers using Field Programmable Gate Array (FPGA) which depends on parallel programming. This method has many advantages over classical microprocessors. In this research, A model of the fuzzy PID control system is implemented in real time with a Xilinx FPGA (Spartan-3A, Xilinx Company, 2007). It is introduced to maintain a constant speed to when the load varies.,The model of a DC motor is considered as a second order system with load variation as a an example for complex model systems. For comparison purpose, two widely used controllers “PID and Fuzzy” have been implemented in the same FPGA card to examine the performance of the proposed system. These controllers have been tested using Matlab/Simulink program under speed and load variation conditions. The controllers were implemented to run the motor as real time application under speed and load variation conditions and showed the superiority of Fuzzy-PID.
28 citations
TL;DR: In this article, the state memory feedback controller is designed to ensure finite-time boundedness and boundedness of switched linear systems with time-varying delay and exogenous disturbances.
Abstract: Finite-time boundedness and H∞ finite-time boundedness of switched linear systems with time-varying delay and exogenous disturbances are addressed. Based on average dwell time (ADT) and free-weight matrix technologies, sufficient conditions which can ensure finite-time boundedness and H∞ finite-time boundedness are given. And then in virtue of the results on finite-time boundedness, the state memory feedback controller is designed to H∞ finite-time stabilize a time-delay switched system. These conditions are given in terms of LMIs and are delay-dependent. An example is given to illustrate the efficiency of the proposed method.
27 citations
TL;DR: In this article, an adaptive fuzzy control algorithm is proposed for trajectory tracking of an n-DOF robot manipulator subjected to parametric uncertainty and it is advantageous compared to the conventional nonlinear saturation controller.
Abstract: In this paper, an adaptive fuzzy control algorithm is proposed for trajectory tracking of an n-DOF robot manipulator subjected to parametric uncertainty and it is advantageous compared to the conventional nonlinear saturation controller The asymptotic stability of the proposed controller has been derived based on Lyapunaov energy function The design procedure is straightforward due to its simple fuzzy rules and control strategies The simulation results show that the present control strategy effectively reduces the control effort with negligible chattering in control torque signals in comparison to the existing nonlinear saturation controller
20 citations
TL;DR: In this paper, a nonlinear large disturbance attenuation controller and parameter updating law of turbine speed governor system are designed using backstepping method, which not only considers transmission line parameter uncer-tainty, but also attenuated the influences of large external disturbances on system output.
Abstract: Considering generator rotor and valve by external disturbances for turbine regulating system, the nonlinear large disturbance attenuation controller and parameter updating law of turbine speed governor system are designed using backstepping method. The controller not only considers transmission line parameter uncer-tainty, and has attenuated the influences of large external disturbances on system output. The nonlinear con-troller does not have the sensitivity to the influences of external disturbances, but also has strong robustness for system parameters variation, which is because of the transmission line uncertainty being considered in internal disturbances. The simulation results show that the control effect of the large disturbance attenuation controller more advantages by comparing with the control performance of conventional nonlinear robust controller.
18 citations
TL;DR: In this paper, a reduced model-based controller has been developed, which requires only the information of space robot base velocity and link parameters, and the flexible link is modeled as Euler Bernoulli beam.
Abstract: Model based control schemes use the inverse dynamics of the robot arm to produce the main torque component necessary for trajectory tracking. For model-based controller one is required to know the model parameters accurately. This is a very difficult task especially if the manipulator is flexible. So a reduced model based controller has been developed, which requires only the information of space robot base velocity and link parameters. The flexible link is modeled as Euler Bernoulli beam. To simplify the analysis we have considered Jacobian of rigid manipulator. Bond graph modeling is used to model the dynamics of the system and to devise the control strategy. The scheme has been verified using simulation for two links flexible space manipulator.
TL;DR: Remotely control applications over a wide area had been commonly used in the industries today, but with the rise of the technology, Ethernet module will be used in order to achieve the remote control system.
Abstract: Remotely control applications over a wide area had been commonly used in the industries today. One of the common applications requires remote control and monitoring is inverter fed induction drive system. Drive system has various types of controller, in order to perform some actions such as control the speed, forward and reverse turning direction of the motor. This approach can be done by Programmable Logic Controller (PLC), and with the rise of the technology, Ethernet module will be used in order to achieve the remote control system. Plus the PLC today can be controlled not only using its original software, but 3rd party software as well, such as LabVIEW. LabVIEW is a human machine interfaces design software that is user friendly. It can be easily communicate with different hardware.
TL;DR: A unified control-oriented modeling approach is proposed to deal with the kinematics, linear and angular momentum, contact constraints and dynamics of a free-flying space robot interacting with a target satellite, which combines the dynamics of both systems in one structure along with holonomic and nonholonomic constraints in a single framework.
Abstract: In this paper a unified control-oriented modeling approach is proposed to deal with the kinematics, linear and angular momentum, contact constraints and dynamics of a free-flying space robot interacting with a target satellite. This developed approach combines the dynamics of both systems in one structure along with holonomic and nonholonomic constraints in a single framework. Furthermore, this modeling allows consid-ering the generalized contact forces between the space robot end-effecter and the target satellite as internal forces rather than external forces. As a result of this approach, linear and angular momentum will form holonomic and nonholonomic constraints, respectively. Meanwhile, restricting the motion of the space robot end-effector on the surface of the target satellite will impose geometric constraints. The proposed momentum of the combined system under consideration is a generalization of the momentum model of a free-flying space robot. Based on this unified model, three reduced models are developed. The first reduced dynamics can be considered as a generalization of a free-flying robot without contact with a target satellite. In this re-duced model it is found that the Jacobian and inertia matrices can be considered as an extension of those of a free-flying space robot. Since control of the base attitude rather than its translation is preferred in certain cases, a second reduced model is obtained by eliminating the base linear motion dynamics. For the purpose of the controller development, a third reduced-order dynamical model is then obtained by finding a common solution of all constraints using the concept of orthogonal projection matrices. The objective of this approach is to design a controller to track motion trajectory while regulating the force interaction between the space robot and the target satellite. Many space missions can benefit from such a modeling system, for example, autonomous docking of satellites, rescuing satellites, and satellite servicing, where it is vital to limit the con-tact force during the robotic operation. Moreover, Inverse dynamics and adaptive inverse dynamics control-lers are designed to achieve the control objectives. Both controllers are found to be effective to meet the specifications and to overcome the un-actuation of the target satellite. Finally, simulation is demonstrated by to verify the analytical results.
TL;DR: The goal of this paper is to introduce a new neural network architecture called Sigmoid Diagonal Recurrent Neural Network (SDRNN) to be used in the adaptive control of nonlinear dynamical systems by adding a sigmoid weight victor in the hidden layer neurons to adapt of the shape of the sigmoids function.
Abstract: The goal of this paper is to introduce a new neural network architecture called Sigmoid Diagonal Recurrent Neural Network (SDRNN) to be used in the adaptive control of nonlinear dynamical systems. This is done by adding a sigmoid weight victor in the hidden layer neurons to adapt of the shape of the sigmoid function making their outputs not restricted to the sigmoid function output. Also, we introduce a dynamic back propagation learning algorithm to train the new proposed network parameters. The simulation results showed that the (SDRNN) is more efficient and accurate than the DRNN in both the identification and adaptive control of nonlinear dynamical systems.
TL;DR: In this article, a robust PD-type FLC is driven for a class of MIMO second order nonlin- ear systems with application to robotic manipulators, where the controller is decoupled and the procedure is simplified leading to a computationally efficient FLC.
Abstract: The performance of any fuzzy logic controller (FLC) is greatly dependent on its inference rules. In most cases, the closed-loop control performance and stability are enhanced if more rules are added to the rule base of the FLC. However, a large set of rules requires more on-line computational time and more parameters need to be adjusted. In this paper, a robust PD-type FLC is driven for a class of MIMO second order nonlin- ear systems with application to robotic manipulators. The rule base consists of only four rules per each de- gree of freedom (DOF). The approach implements fuzzy partition to the state variables based on Lyapunov synthesis. The resulting control law is stable and able to exploit the dynamic variables of the system in a lin- guistic manner. The presented methodology enables the designer to systematically derive the rule base of the control. Furthermore, the controller is decoupled and the procedure is simplified leading to a computationally efficient FLC. The methodology is model free approach and does not require any information about the sys- tem nonlinearities, uncertainties, time varying parameters, etc. Here, we present experimental results for the following controllers: the conventional PD controller, computed torque controller (CTC), sliding mode con- troller (SMC) and the proposed FLC. The four controllers are tested and compared with respect to ease of design, implementation, and performance of the closed-loop system. Results show that the proposed FLC has outperformed the other controllers.
TL;DR: A probabilistic fuzzy approach is proposed for mobile-robot reactive navigation using range sensors using range sensor data to create an integrated reactive navigation control system with good real-time performance under uncertainty.
Abstract: In this paper, a probabilistic fuzzy approach is proposed for mobile-robot reactive navigation using range sensors. The primary motivation is an integrated reactive navigation control system with good real-time performance under uncertainty. To accomplish this aim, a probabilistic fuzzy logic system (PFLS) is introduced to range measurement and reactive navigation in local environments. PFLS is first adopted to handle the fuzzy and stochastic uncertainties in range sensors and to provide more precise distance information in unknown environments. Consequently these sensor data are sent to a probabilistic fuzzy rule-based inference system with reactive behaviors for local navigation. The feasibility and effectiveness of the proposed approach are verified by simulation and the experiments on a real mobile robot.
TL;DR: In this paper, a new controller is proposed by using backstepping method for the trajectory tracking problem of nonholonomic dynamic mobile robots with non-holonomic constraints under the condition that there is a distance between the mass center and the geometrical center, and the distance is unknown.
Abstract: In this paper, a new controller is proposed by using backstepping method for the trajectory tracking problem of nonholonomic dynamic mobile robots with nonholonomic constraints under the condition that there is a distance between the mass center and the geometrical center and the distance is unknown. And an adaptive feedback controller is also proposed for the case that some kinematic parameters and dynamic parameters are uncertain. The asymptotical stability of the control system is proved with Lyapunov stability theory. The simulation results show the effectiveness of the proposed controller. The comparison with the previous methods is made to show the effectiveness of the method in this article.
TL;DR: In this article, a sequential linearized model based predictive controller is designed using the DMC algorithm to control the temperature of a batch MMA polymerization process using the mechanistic model of the polymerization, a parametric transfer function is derived to relate the reactor temperature to the power of the heaters.
Abstract: A sequential linearized model based predictive controller is designed using the DMC algorithm to control the temperature of a batch MMA polymerization process. Using the mechanistic model of the polymerization, a parametric transfer function is derived to relate the reactor temperature to the power of the heaters. Then, a multiple model predictive control approach is taken in to track a desired temperature trajectory.The coefficients of the multiple transfer functions are calculated along the selected temperature trajectory by sequential linearization and the model is validated experimentally. The controller performance is studied on a small scale batch reactor.
TL;DR: In this paper, the authors considered the distributed consensus problem of high-order multi-agent systems with nonlinear dynamics subject to external disturbances and derived sufficient conditions under which the consensus can be achieved with a prescribed norm bound.
Abstract: This paper deals with the distributed consensus problem of high-order multi-agent systems with nonlinear dynamics subject to external disturbances. The network topology is assumed to be a fixed undirected graph. Some sufficient conditions are derived, under which the consensus can be achieved with a prescribed norm bound. It is shown that the parameter matrix in the consensus algorithm can be designed by solving two linear matrix inequalities (LMIs). In particular, if the nonzero eigenvalues of the laplacian matrix ac-cording to the network topology are identical, the parameter matrix in the consensus algorithm can be de-signed by solving one LMI. A numerical example is given to illustrate the proposed results.
TL;DR: In this article, an adaptive on-line neural network compensator (ANNC) was used for the position control of a pneumatic gantry robot. But the advantage of neural networks is that they do not require a mathematical model of the system to be controlled.
Abstract: Considerable research has been conducted on the control of pneumatic systems. However, nonlinearities continue to limit their performance. To compensate, advanced nonlinear and adaptive control strategies can be used. But the more successful advanced strategies typically need a mathematical model of the system to be controlled. The advantage of neural networks is that they do not require a model. This paper reports on a study whose objective is to explore the potential of a novel adaptive on-line neural network compensator (ANNC) for the position control of a pneumatic gantry robot. It was found that by combining ANNC with a traditional PID controller, tracking performance could be improved on the order of 45% to 70%. This level of performance was achieved after careful tuning of both the ANNC and PID components. The paper sets out to document the ANNC algorithm, the adopted tuning procedure, and presents experimental results that illustrate the adaptive nature of NN and confirms the performance achievable with ANNC. A major contribution is demonstration that tuning of ANNC requires no more effort than the tuning of PID.
TL;DR: In this paper, the performance of instantaneous real active and reactive current (id-iq) control strategy for extracting reference currents of shunt active filters under balanced, un-balanced and balanced non-sinusoidal conditions is analyzed.
Abstract: The main objective of this paper is to develop Fuzzy controller to analyse the performance of instantaneous real active and reactive current (id-iq) control strategy for extracting reference currents of shunt active filters under balanced, un-balanced and balanced non-sinusoidal conditions. When the supply voltages are balanced and sinusoidal, the all control strategies are converge to the same compensation characteristics; However, the supply voltages are distorted and/or un-balanced sinusoidal, these control strategies result in different degrees of compensation in harmonics. The p-q control strategy unable to yield an adequate solution when source voltages are not ideal. Extensive simulations are carried out with Fuzzy controller for id-iq control strategy under different main voltages. The 3-ph 4-wire shunt active filter (SHAF) system is also implemented on a Real Time Digital Simulator (RTDS Hardware) to further verify its effectiveness. The detailed simulation and RTDS Hardware results are included.
TL;DR: In this article, a new approach for a class of optimal control problems governed by Volterra integral equations is proposed, which is based on linear combination property of intervals, and Discretization method is also applied to convert the new problems to the discrete time problem.
Abstract: In this paper, we propose a new approach for a class of optimal control problems governed by Volterra integral equations which is based on linear combination property of intervals. We convert the nonlinear terms in constraints of problem to the corresponding linear terms. Discretization method is also applied to convert the new problems to the discrete-time problem. In addition, some numerical examples are presented to illustrate the effectiveness of the proposed approach.
TL;DR: A class of closed-loop supply chain system consisting of one manufacturer and one supplier is designed, in which re-distribution, remanufacturing and reuse are considered synthetically, and a non-linear dynamic pricing model is established.
Abstract: A class of closed-loop supply chain system consisting of one manufacturer and one supplier is designed, in which re-distribution, remanufacturing and reuse are considered synthetically. The manufacturer is in charge of recollecting and re-disposal the used products. Demands of ultimate products and collecting quantity of used products are described as the function of prices and reference prices. A non-linear dynamic pricing model for this closed-loop supply chain is established. A numerical example is designed, and the results of this example verified the model’s validity to price for the operation of closed-loop supply chain system.
TL;DR: A motor learning system for the self-balancing two-wheeled robot has been built using the RBF neural networks as the actor and evaluation function approximator and the simulation experiments showed that the proposed motorlearning system achieved a better learning effect.
Abstract: A novel motor learning method is present based on the cooperation of the cerebellum and basal ganglia for the behavior learning of agent. The motor learning method derives from the principle of CNS and operant learning mechanism and it depends on the interactions between the basal ganglia and cerebellum. The whole learning system is composed of evaluation mechanism, action selection mechanism, tropism mechanism. The learning signals come from not only the Inferior Olive but also the Substantia Nigra in the beginning. The speed of learning is increased as well as the failure time is reduced with the cerebellum as a supervisor. Convergence can be guaranteed in the sense of entropy. With the proposed motor learning method, a motor learning system for the self-balancing two-wheeled robot has been built using the RBF neural networks as the actor and evaluation function approximator. The simulation experiments showed that the proposed motor learning system achieved a better learning effect, so the motor learning based on the coordination of cerebellum and basal ganglia is effective.
TL;DR: It is concluded that primary function of proximal- to-distal or distal-to-proximal joint sequence is to flatten the trajectory of the fingertip or body centre of mass.
Abstract: This paper discusses on the role of joint temporal sequence while moving a two-dimensional arm from an initial position to targets into the fingertip workspace in humans. For this purpose, we proposed a general monotonic model of joint asymmetric displacement. Optimization consisted in minimizing least square dis-placement of either fingertip or arm centre of mass from arm initial position to four targets located into fin-gertip workspace, i.e. contralaterally and ipsilaterally. Except for 60° ipsilateral target, results of the simula-tion presented in all cases temporal sequences of the shoulder, the elbow and the wrist. We concluded that primary function of proximal-to-distal or distal-to-proximal joint sequence is to flatten the trajectory of the fingertip or body centre of mass.
TL;DR: By the new BHF device combined with the hardware and the software system, the BHF can be regulated accurately variation with the predefined BHF profile in deep drawing process.
Abstract: Blank holder force (BHF) control is used to prevent wrinkles of sheet metal in deep drawing process. Based on a novel conception of BHF control technique driven by servo-motor, a new BHF device with six-bar linkage mechanism has been designed and manufactured. Whole control system of the new BHF technique was developed, and the basic structure of the hardware configuration of the system was given. Software analysis, implementation and division of the functional modules have been done. Also, the control software in data acquisition and processing module has been developed in the relevant technology of the BHF control system for the requirements of real-time, stability and accuracy. By the new BHF device combined with the hardware and the software system, the BHF can be regulated accurately variation with the predefined BHF profile in deep drawing process.
TL;DR: The some properties of the fuzzy R-solution of the control linear fuzzy differential differential inclusions are shown and the time-optimal problems for it are researched.
Abstract: In the present paper, we show the some properties of the fuzzy R-solution of the control linear fuzzy differential inclusions and research the time-optimal problems for it.
TL;DR: In this article, the performance of servo drive control is evaluated using the Prony analysis, where two important dynamic parameters of closed loop behavior, damping and frequency, are estimated by the prony method.
Abstract: The Prony Analysis is already used in different fields of science and industries. The described new approach intends assessing the performance of Servo Drive Control. The basic approach is, that two important dynamic parameters of closed loop behavior, damping and frequency, are estimated by the Prony method. Hence analyzing a control loop in this way leads to a statement concerning the quality of control and allows comparing different parameter sets. The paper presents results achieved by using this method on a test rig.
TL;DR: In this article, an improvement of the existing nominal characteristic trajectory following (NCTF) controller is described as a practical control method for a two-mass rotary point-to-point positioning systems.
Abstract: This paper describes an improvement of the existing nominal characteristic trajectory following (NCTF) as a practical control method for a two-mass rotary point-to-point (PTP) positioning systems. Generally, the NCTF controller consists of a nominal characteristic trajectory (NCT) and a PI compensator. A notch filter is added as a part of the compensator to eliminate the vibration due to the mechanical resonance of the plant. The objective of the NCTF controller is to make the object motion follow the NCT and end at its origin. The NCTF controller is designed based on a simple open-loop experiment of the object. The parameters identification and an exact model of the plant are not necessary for controller design. The performance response of improved NCTF controller is evaluated and discussed based on results of simulation. The effect of the design parameters on the robustness of the NCTF controller to inertia and friction variations is evaluated and compared with conventional PID controller. The results show that the improved NCTF controller has a better positioning performance and is much more robust than the PID controller.
TL;DR: In this article, an adaptive neural fuzzy (NF) controller is developed for active vibration suppression in flexible structures, and a recurrent identification network (RIN) is developed to adaptively identify system dynamics of the plant.
Abstract: An adaptive neural fuzzy (NF) controller is developed in this paper for active vibration suppression in flexible structures. A recurrent identification network (RIN) is developed to adaptively identify system dynamics of the plant. A novel recurrent training (RT) technique is suggested to train the RIN so as to optimize nonlinear input-output mapping and to enhance convergence. The effectiveness of the developed controller and the related techniques has been verified experimentally corresponding to different control scenarios. Test results show that the proposed RIN can effectively recognize the time-varying dynamics of the plant. The RT-based hybrid training technique can improve the adaptive capability of the control system to accommodate different system conditions and enhance the training convergence. The developed NF controller is a robust and stable vibration suppression system, and it outperforms other related NF controllers.