Parameter Adaptation Law Research Articles

Adaptive decoupling synchronous control of dissimilar redundant actuation system for large civil aircraft

In order to meet the increasing reliability requirements of actuation system for large civil aircraft, the novel distributed dissimilar redundant actuation system composed of one hydraulic actuator (HA) and one electro-hydrostatic actuator (EHA) has been applied to the design of advanced aircraft. This configuration can greatly improve the system reliability and effectively avoid potential common-mode/common-cause (CM/CC) failure. However, this actuation configuration can exhibit force fighting problem between HA and EHA due to their different driving mechanisms and rigid coupling when they operate in the active/active mode, which may even cause damage to the control surface. To resolve this problem, an adaptive decoupling synchronous controller (ADSC) is proposed in this study. The coupling effect between HA and EHA is taken into account, and an adaptive decoupling controller is designed to eliminate the coupling term. Parameter adaption law is designed for the parametric uncertainties. In addition, a feed-forward compensator is proposed to compensate for the difference between HA and EHA by accelerating the dynamic response of EHA. Finally, the comparative simulation results indicate that the proposed ADSC controller has high speed/high robustness performances and can effectively reduce the force fighting between HA and EHA.

Output torque tracking control of direct-drive rotary torque motor with dynamic friction compensation

Accurate output torque tracking control problem of Direct-Drive Rotary (DDR) torque motor is concerned in this paper. An adaptive robust torque control algorithm with LuGre model based dynamic friction compensation is proposed to improve the torque tracking accuracy. Adaptive robust control (ARC) backstepping design technique and Lyapunov stability theorem are employed to design the controller to deal with both structured and unstructured uncertainties in a practical control system. In the ARC algorithm, discontinuous projection based parameter adaptive law is utilized to estimate unknown system parameters, and robust control law ensures the robustness of closed-loop control system. In order to further improve the torque tracking control performance, LuGre model based dynamic friction compensation is designed to estimate and compensate friction torque in the practical torque motor system. The mathematically design procedure of the proposed algorithm is developed. Finally, the proposed algorithm is implemented on a practical DDR torque motor control system. Experimental results with three compared control algorithms validate that the proposed algorithm can effectively improve the torque tracking performance and LuGre model based dynamic friction compensation is also necessary for high-accuracy torque tracking control.

Application of V-Belt Continuously Variable Transmission System Using Hybrid Recurrent Laguerre Orthogonal Polynomials Neural Network Control System and Modified Particle Swarm Optimization

Because the V-belt continuously variable transmission (CVT) system spurred by permanent magnet synchronous motor (PMSM) has unknown nonlinear and time-varying properties, the better control performance design for the linear control design is a time consuming procedure. In order to conquer difficulties for design of the linear controllers, the hybrid recurrent Laguerre orthogonal polynomials neural network (NN) control system, which has online learning ability to react to unknown nonlinear and time-varying characteristics, is developed for controlling PMSM servo-driven V-belt CVT system with the lumped nonlinear load disturbances. The hybrid recurrent Laguerre orthogonal polynomials NN control system consists of an inspector control, a recurrent Laguerre orthogonal polynomials NN control with adaptation law, and a recouped control with estimation law. Moreover, the adaptation law of online parameters in the recurrent Laguerre orthogonal polynomials NN is originated from Lyapunov stability theorem. Additionally, two varied learning rates of the parameters by means of modified particle swarm optimization (PSO) are posed in order to achieve better convergence. At last, comparative studies shown by experimental results are illustrated in order to verify the effectiveness of the proposed control scheme.

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.

Intelligent dynamic sliding-mode neural control using recurrent perturbation fuzzy neural networks

In this paper, a recurrent perturbation fuzzy neural network (RPFNN) is used to online approximate an unknown nonlinear term in the system dynamics. A sine–cosine perturbed membership function is used to handle rule uncertainties when it is hard to exactly determine the grade of the value of fuzzy sets. Unlike type-2 fuzzy sets use an extra type reduction operation to find the output, the proposed RPFNN does not require heavy computational loading. Meanwhile, this paper proposes an intelligent dynamic sliding-mode neural control (IDSNC) system which is composed of a neural controller and an exponential compensator. The neural controller is designed as the main controller via dynamic sliding-mode approach, and the exponential compensator is designed to obtain a faster reaching time and a good robustness. The parameter adaptation laws of the IDSNC system are derived based on the Lyapunov function, so that the system stability can be guaranteed. Finally, the IDSNC system is applied to an inverted pendulum problem and a chaotic synchronization problem. The simulation results demonstrate that the IDSNC system can achieve favorable control performance and is robust against parameter variations and external disturbances.

Composite Adaptive Fuzzy Output Feedback Dynamic Surface Control Design for Uncertain Nonlinear Stochastic Systems with Input Quantization

In this paper, a composite adaptive fuzzy output feedback control problem is investigated for a class of single-input and single-output stochastic nonlinear systems, where the input signal takes quantized values. In the control design, by using fuzzy logic systems to approximate the unknown nonlinear functions, a fuzzy adaptive state observer is designed to estimate the unmeasured states. By utilizing the designed fuzzy state observer, a serial–parallel estimation model is established. Based on adaptive backstepping dynamic surface control technique and the prediction error between the system states observer model and the serial–parallel estimation model, an adaptive output feedback controller is constructed. The designed fuzzy controller with the composite parameters adaptive laws ensures that all the variables of closed-loop system are bounded in probability, and tracking error converges to a small neighborhood of zero. Two examples are provided to verify the effectiveness of the proposed approach.

Observer-based indirect adaptive fuzzy control for SISO nonlinear systems with unknown gain sign

This paper develops an observer-based indirect adaptive fuzzy control scheme for a class of single-input single-output (SISO) nonlinear systems. The considered systems have unknown nonlinear functions, unknown control gain sign, and unmeasurable state variables. In this study, fuzzy systems are utilized to approximate unknown nonlinear functions, and a fuzzy adaptive state observer is designed to estimate the unmeasured state. A robust controller is used to compensate for the lumped errors, and the Nussbaum gain function is incorporated in the robust controller to estimate the unknown gain sign. Based on the strictly positive real Lyapunov design approach, the parameter adaptive laws are designed by the fuzzy basis functions rather than by its filtering. It is proved that all the signals in the closed-loop systems are bounded and that the tracking error converges asymptotically to zero. A simulation example is used to demonstrate the effectiveness of the proposed scheme.

Fuzzy generalized projective synchronization of incommensurate fractional-order chaotic systems

This paper proposes a novel fuzzy adaptive controller for achieving an appropriate generalized projective synchronization (GPS) of two incommensurate fractional-order chaotic systems. The master system and the slave system, considered here, are assumed to be with non-identical structure, external dynamical disturbances, uncertain models and distinct fractional-orders. The adaptive fuzzy systems are used for estimating some unknown nonlinear functions. A Lyapunov approach is adopted for deriving the parameter adaptation laws and proving the stability of the closed-loop system. Under some mild assumptions, the proposed controller can guarantee all the signals in the closed-loop system remain bounded and the underlying synchronization errors asymptotically converge towards a small of neighborhood of the origin. Finally, some numerical experiment results are presented to illustrate the effectiveness of the proposed synchronization scheme.

RETRACTED: Application of a V-belt continuously variable transmission system by using a composite recurrent Laguerre orthogonal polynomial neural network control system and modified particle swarm optimization

The nonlinear and time-varying characteristics of the V-belt continuously variable transmission system driven by a permanent magnet synchronous motor (PMSM) are unknown, therefore, improving the control performance of the linear control design is time-consuming. To overcome difficulties in the design of a linear controller for the PMSM servo-driven V-belt continuously variable transmission system with the lumped nonlinear load disturbances, a composite recurrent Laguerre orthogonal polynomial neural network (NN) control system which has online learning capability to respond the nonlinear time-varying system, was developed. The composite recurrent Laguerre orthogonal polynomial NN control system can perform inspector control, recurrent Laguerre orthogonal polynomial NN control which involves an adaptation law, and recouped control which involves an estimation law. Moreover, the adaptation law of online weight parameters in the recurrent Laguerre orthogonal polynomial NN is based on Lyapunov stability theorem. The use of modified particle swarm optimization yielded two optimal learning rates for the weight parameters which helped improve convergence. Finally, comparison of the experimental results of the present study with those of previous studies demonstrated the high control performance of the proposed control scheme.

Intelligent exponential sliding-mode control with uncertainty estimator for antilock braking systems

The purpose of the antilock braking system (ABS) is to regulate the wheel longitudinal slip at its optimum point in order to generate the maximum braking force; however, the vehicle braking dynamic is highly nonlinear. To relax the requirement of detailed system dynamics, this paper proposes an intelligent exponential sliding-mode control (IESMC) system for an ABS. A functional recurrent fuzzy neural network (FRFNN) uncertainty estimator is designed to approximate the unknown nonlinear term of ABS dynamics, and the parameter adaptation laws are derived in the sense of projection algorithm and Lyapunov stability theorem to ensure the stable control performance. Since the outputs of the functional expansion unit are used as the output weights of the FRFNN uncertainty estimator, the FRFNN can effectively capture the input---output dynamic mapping. In addition, a nonlinear reaching law, which contains an exponential term of sliding surface to smoothly adapt the variations of sliding surface, is designed to reduce the level of the chattering phenomenon. Finally, the simulation results demonstrate that the proposed IESMC system can achieve robustness slip tracking performance in different road conditions.

Novel Approach to Direct Adaptive Fuzzy Control Applied to the Asynchronous Machine

This paper presents an advanced direct adaptive fuzzy control for asynchronous machine which uses the theory of approximation and the theory of Lyapunov to establish a parametric adaptation law ensuring the stability and boundedness of all the control signals and the tracking error. In the direct approach, the fuzzy system is used to describe directly the control law and the parameters of the fuzzy system are directly adjusted to achieve the control objectives. Through the obtained results, stable direct adaptive fuzzy control generalized has proved a great effectiveness and a strong robustness in the presence of parameter variations and disturbances.

Novel Approach to Direct Adaptive Fuzzy Control Applied to the Asynchronous Machine

This paper presents an advanced direct adaptive fuzzy control for asynchronous machine which uses the theory of approximation and the theory of Lyapunov to establish a parametric adaptation law ensuring the stability and boundedness of all the control signals and the tracking error. In the direct approach, the fuzzy system is used to describe directly the control law and the parameters of the fuzzy system are directly adjusted to achieve the control objectives. Through the obtained results, stable direct adaptive fuzzy control generalized has proved a great effectiveness and a strong robustness in the presence of parameter variations and disturbances.

Novel Approach to Direct Adaptive Fuzzy Control Applied to the Asynchronous Machine

This paper presents an advanced direct adaptive fuzzy control for asynchronous machine which uses the theory of approximation and the theory of Lyapunov to establish a parametric adaptation law ensuring the stability and boundedness of all the control signals and the tracking error. In the direct approach, the fuzzy system is used to describe directly the control law and the parameters of the fuzzy system are directly adjusted to achieve the control objectives. Through the obtained results, stable direct adaptive fuzzy control generalized has proved a great effectiveness and a strong robustness in the presence of parameter variations and disturbances.

Projective synchronization of two different fractional-order chaotic systems via adaptive fuzzy control

In this paper, the projective synchronization problem of two fractional-order different chaotic (or hyperchaotic) systems with both uncertain dynamics and external disturbances is considered. More particularly, a fuzzy adaptive control system is investigated for achieving an appropriate projective synchronization of unknown fractional-order chaotic systems. The adaptive fuzzy logic systems are used to approximate some uncertain nonlinear functions appearing in the system model. These latter are augmented by a robust control term to compensate for the unavoidable fuzzy approximation errors and external disturbances as well as residual error due to the use of the so-called e-modification in the adaptive laws. A Lyapunov approach is adopted for the design of the parameter adaptation laws and the proof of the corresponding stability as well as the asymptotic convergence of the underlying synchronization errors towards zero. The effectiveness of the proposed synchronization system is illustrated through numerical experiment results.

Single input sliding mode control for hyperchaotic Lu system with parameter uncertainty

In this paper, design of sliding mode controller (SMC) is presented to investigate the stabilization, complete synchronization and adaptive synchronization of four dimensional hyperchaotic Lu systems with parameter uncertainty. To achieve this goal, sliding mode control scheme along with Lyapunov stability theory is utilized. A proportional integral switching surface is proposed to ensure the stability of the closed-loop system in sliding motion. The SMC has been proposed to guarantee the occurrence of the sliding motion. It has also been shown that by proper choice of the adaptation laws for parameters, systems can be synchronized in conventional manner in master-slave configuration, in uncertain environment. The proposed adaptation laws also ensure the convergence of uncertain parameters to their true value in all the cases. Finally, numerical simulations are performed to demonstrate the effectiveness of the proposed approach.

Different-lags Synchronization in Time-delay and Circuit Simulation of Fractional-order Chaotic System Based on Parameter Identification

This study constructs a novel four-dimensional fractional-order chaotic system. It verifies chaotic nonlinear dynamic behaviors and physical reliability by numerical simulation and hardware circuit design. For a class of parameter uncertainty fractional different-lags ( τi ) chaotic systems, the authors design a time-delayed (δ) synchronization controllers and parameter adaptive laws. It proves that the drive system and the response system tend to be synchronized and identified parameter when the control parameter matrix K satisfies the condition that K – nE is a positive definite matrix. Simulation results show physical reliability of the fractional-order different-lags chaotic system and verify effectiveness of different-lags synchronization in time-delayed system method design.

Composite recurrent Laguerre orthogonal polynomials neural network dynamic control for continuously variable transmission system using altered particle swarm optimization

The composite recurrent Laguerre orthogonal polynomials neural network (NN) control system using altered particle swarm optimization (PSO) is developed for controlling the V-belt continuously variable transmission (CVT) system driven by permanent magnet synchronous motor to obtain better control performance. The simplified dynamic and kinematic models of a V-belt CVT system are derived by law of conservation. The control system consists of an inspector control, a recurrent Laguerre orthogonal polynomials NN control with adaptation law, and a recouped control with estimation law. Moreover, the adaptation law of online parameters in the recurrent Laguerre orthogonal polynomials NN is originated from Lyapunov stability theorem. Additionally, two optimal learning rates of the parameters by means of altered PSO are posed in order to achieve better convergence. At last, comparative studies shown by experimental results are illustrated to demonstrate the control performance of the proposed control scheme.

Line-of-Sight Path Following for Dubins Paths With Adaptive Sideslip Compensation of Drift Forces

We present a nonlinear adaptive path following controller that compensates for drift forces through vehicle sideslip. Vehicle sideslip arises during path following when the vehicle is subject to drift forces caused by ocean currents, wind, and waves. The proposed algorithm is motivated by a line-of-sight (LOS) guidance principle used by ancient navigators, which is here extended to path following of Dubins paths. The unknown sideslip angle is treated as a constant parameter, which is estimated using an adaptation law. The equilibrium points of the cross-track and parameter estimation errors are proven to be uniformly semiglobally exponentially stable. This guarantees that the estimated sideslip angle converges to its true value exponentially. The adaptive control law is in fact an integral LOS controller for path following since the parameter adaptation law provides integral action. The proposed guidance law is intended for maneuvering in the horizontal-plane at given speeds and typical applications are marine craft, autonomous underwater vehicles, unmanned aerial vehicles as well as other vehicles and crafts, where the goal is to follow a predefined parametrized curve without time constraints. Two vehicle cases studies are included to verify the theoretical results.

Backstepping fuzzy-neural-network control design for hybrid maglev transportation system.

This paper focuses on the design of a backstepping fuzzy-neural-network control (BFNNC) for the online levitated balancing and propulsive positioning of a hybrid magnetic levitation (maglev) transportation system. The dynamic model of the hybrid maglev transportation system including levitated hybrid electromagnets to reduce the suspension power loss and the friction force during linear movement and a propulsive linear induction motor based on the concepts of mechanical geometry and motion dynamics is first constructed. The ultimate goal is to design an online fuzzy neural network (FNN) control methodology to cope with the problem of the complicated control transformation and the chattering control effort in backstepping control (BSC) design, and to directly ensure the stability of the controlled system without the requirement of strict constraints, detailed system information, and auxiliary compensated controllers despite the existence of uncertainties. In the proposed BFNNC scheme, an FNN control is utilized to be the major control role by imitating the BSC strategy, and adaptation laws for network parameters are derived in the sense of projection algorithm and Lyapunov stability theorem to ensure the network convergence as well as stable control performance. The effectiveness of the proposed control strategy for the hybrid maglev transportation system is verified by experimental results, and the superiority of the BFNNC scheme is indicated in comparison with the BSC strategy and the backstepping particle-swarm-optimization control system in previous research.

Design of an adaptive sliding mode controller for robust yaw stabilisation of in-wheel-motor-driven electric vehicles

A robust yaw stability control system is designed to stabilise the vehicle yaw motion. Vehicles undergo changes in parameters and disturbances with respect to the wide range of driving conditions, e.g., tyre–road conditions. Therefore, a robust control design technique is required to guarantee system stability and enhance the robustness. In this paper, a sliding mode control methodology is applied to make vehicle yaw rate to track its reference with robustness against model uncertainties and disturbances. In addition, a parameter adaptation law is also applied to estimate varying vehicle parameters with respect to road conditions and is incorporated into sliding mode control framework. The control performance of the proposed control system was evaluated through field tests.