Parameter Learning Laws Research Articles

Wavelet Fuzzy Neural Supertwisting Sliding Mode Control of an Active Power Filter

This paper presents a new intelligent sliding mode control approach to effectively achieve harmonic suppression of an active power filter (APF). An intelligent complementary terminal sliding mode controller (CTSMC) is proposed to improve the control accuracy of current loop and deal with the lumped disturbances in the system. Complementary terminal sliding mode control combines the features of complementary sliding mode control (CSMC) and terminal sliding mode control (TSMC) and does not depend on an accurate dynamic model. In addition, in order to reduce chattering, a multi-loop neural network (MLNN) whose parameter learning laws of MLNN are derived based on the Lyapunov laws are proposed to approximate the unknown nonlinear function term in the APF dynamic model, thus reducing the burden of sliding mode control. Finally, within the computing power of control board, detailed simulation and hardware experiments are carried out to demonstrate it has better harmonic suppression, steady-state and dynamic performance compared with the existing methods.

Intelligent Complementary Terminal Sliding Mode Using Multiloop Neural Network for Active Power Filter

This paper presents a new intelligent sliding mode control approach to effectively achieve harmonic suppression of an active power filter (APF). An intelligent complementary terminal sliding mode controller (CTSMC) is proposed to improve the control accuracy of current loop and deal with the lumped disturbances in the system. Complementary terminal sliding mode control combines the features of complementary sliding mode control (CSMC) and terminal sliding mode control (TSMC) and does not depend on an accurate dynamic model. In addition, in order to reduce chattering, a multi-loop neural network (MLNN) whose parameter learning laws of MLNN are derived based on the Lyapunov laws are proposed to approximate the unknown nonlinear function term in the APF dynamic model, thus reducing the burden of sliding mode control. Finally, within the computing power of control board, detailed simulation and hardware experiments are carried out to demonstrate it has better harmonic suppression, steady-state and dynamic performance compared with the existing methods.

Chattering-free adaptive iterative learning for attitude tracking control of uncertain spacecraft

This paper aims at developing chattering-free adaptive iterative learning for repetitive attitude tracking tasks of spacecraft subject to initial state errors. Thanks to the proposed reduction mechanism in the parametric learning law, iteration-varying initial state errors, as well as the unknown inertia matrix and external disturbances, can be addressed effectively. Moreover, to avoid the chattering phenomenon of the control signals, we introduce an approximation of the sign function. Of note is that this approximation leads to a class of non-negative definite problems. A new analytical method is consequently exploited with a Lyapunov-like theory based on contraction-mapping and composite-energy-function, which rigorously shows the boundedness and convergence of the iterative learning process in the presence of initial state errors and non-negative definite problems. These observations are validated by implementing the proposed chattering-free adaptive iterative learning method to achieve the specific attitude tracking task of an uncertain spacecraft.

Broad-learning recurrent Hermite neural control for unknown nonlinear systems

The broad-learning systems (BLS) with advance control theories have been studied, but found to have two disadvantages: one is that the calculations are too complicated and the other is that the convergence time cannot be guaranteed. In order to mitigate the high computational loading, this study proposes a broad-learning Hermite neural network (BHNN), which has the capability of dynamic mapping and reduces the structural complexity of neural network. Meanwhile, a broad-learning recurrent Hermite neural control (BRHNC) system is proposed while maintaining finite-time stability to speed up the tracking error convergence. The proposed BRHNC system comprises two controllers: a recurrent broad controller that utilizes a BHNN to approximate on-line an ideal finite-time controller and a robust exponential controller that ensures system stability through a Lyapunov function. Meanwhile, the BHNN’s full-tuned parameter learning laws are developed to increase the approximating capacity, learning capacity and accuracy using gradient descent method. Finally, simulation and experimental results show that the BRHNC system has good control, tracking and disturbance rejection properties, while the BRHNC system requires no prior knowledge about the system dynamics.

Adaptive Fuzzy Iterative Learning Control for High-Speed Trains With Both Randomly Varying Operation Lengths and System Constraints

In this article, a new adaptive fuzzy iterative learning control (AFILC) method is proposed for the tracking control of nonlinear uncertain high-speed train (HST) operation systems that have both randomly varying iteration lengths and speed and input force constraints. To cope with unknown time-varying basic resistance coefficients, an adaptive learning control law and two fully projected parameter learning laws are designed. The nonparametric and unknown additional resistance in the HST operation system is compensated and integrated into the control law by means of a newly constructed adaptive iterative learning fuzzy system. Moreover, due to the complex operation environment and various uncertainties, disturbances and emergencies, trains are often early or late compared with the prearranged timetable rather than being strictly on time in the repeated operations of each day, which leads to randomly varying operation lengths for actual HST running. Furthermore, as the traveling speed of modern HSTs increases, both the train's speed and input force should be constrained to guarantee the safe operation. Fortunately, the proposed AFILC can not only actively manipulate the position, speed, and input force of the train into prespecified and constrained ranges for safe operation but can also make both the position and speed tracking control errors converge to zero over the whole desired and scheduled time interval, even if the actual time interval varies in each operation of the HST. Simulations on a practical train operation system similar to China Railway High-speed (CRH)-3 train are further presented to demonstrate the applicability and effectiveness of the proposed method.

Perturbation wavelet neural sliding mode position control for a voice coil motor driver

To cope with the nonlinear electro-magneto-mechanical characteristics, this paper proposes a perturbation wavelet neural sliding mode position control (PWSPC) system for a voice coil motor (VCM) driver. A perturbed wavelet neural network (PWNN) approximator is used to online approximate the unknown nonlinear term in the VCM system dynamics. The PWNN approximator uses perturbed wavelet functions to handle the rules uncertainties like interval type-2 fuzzy sets. The structure learning ability enables the PWNN approximator to evolve its structure online. Further, the parameter learning laws and stability analysis are derived in the sense of Lyapunov function; thus, the parameter convergence and system stability can be guaranteed. Finally, the experimental results verify that the proposed PWSPC system can achieve favorable control performance such as good disturbance rejection and good tracking accuracy.

Adaptive iterative learning control for nonlinear uncertain systems with both state and input constraints

This work proposes a new adaptive iterative learning control (AILC) scheme for nonlinear systems with both state and input constraints, where the time-varying parametric uncertainties, external disturbances, and random initial errors are also considered together. The proposed AILC consists of a learning control law and two fully projected parameter learning laws. By incorporating a barrier composite energy function into the learning control law and using a projection mechanism for the parameter learning laws, the proposed AILC can flexibly and actively manipulate the states and inputs of the system into their pre-specified and constrained ranges, respectively. It is theoretically shown that the asymptotic and pointwise convergence properties are guaranteed without violating any state and input constraints. The validity of the proposed AILC scheme is further verified with a practical train operation system.

Adaptive synchronization of delayed reaction-diffusion FCNNs via learning control approach

A new adaptive learning control strategy is used to solve the synchronization problem for delayed reaction-diffusion fuzzy cellular neural networks with unknown periodically time-varying parameters. By constructing suitable Lyapunov-Krasovskii-like composite energy functional and employing some analysis techniques, a differential-difference mixed parametric learning law and an adaptive learning control law are constructed to achieve the adaptive synchronization of reaction-diffusion fuzzy cellular neural networks with unknown periodically time-varying parameters. Finally, a numerical example is presented to show the effectiveness of the proposed synchronization approach.

Projective Synchronization of Complex Dynamical Networks with Time-Varying Coupling Strength via Hybrid Feedback Control

We introduce a hybrid feedback control scheme to design a controller for the projective synchronization of complex dynamical networks with unknown periodically time-varying parameters. A differential-difference mixed parametric learning law and an adaptive learning control law are constructed to ensure the asymptotic convergence of the error in the sense of square error norm. Moreover, numerical simulation results are used to verify the effectiveness of the proposed method.

On-line optimization of fedbatch bioreactors by adaptive extremum seeking control

In this paper, we present an adaptive extremum seeking control scheme for fed-batch bioreactors with Haldane kinetics. The proposed adaptive extremum seeking approach utilizes the structure information of the process kinetics to derive a seeking algorithm that drives the system states to the desired setpoints that maximize the biomass production. It assumes that only the substrate concentration is available for on-line measurement. Lyapunov stability is used in the design of the extremum seeking controller structure and the development of the parameter learning laws. The performance of the approach is illustrated via numerical simulations.

An ILC scheme for a class of nonlinear continuous‐time systems with time‐iteration‐varying parameters subject to second‐order internal model

AbstractIn this paper, a new iterative learning control (ILC) scheme is proposed to deal with unknown time‐iteration‐varying parameters for a class of nonlinear continuous‐time systems. The parametric non‐repetitiveness in the iteration domain is described by a second‐order internal model, which includes systems with iteration‐invariant parameters as a subset. By incorporating the internal model into the parametric learning law, the proposed ILC scheme can handle more generic systems with parametric uncertainties, and includes existing ILC schemes as a special case with the first‐order internal model. The conditions under which the new ILC scheme can guarantee learning convergence are delicately explored. Utilizing the information of the previous two iterations and the method of composite energy function (CEF), it is able to derive pointwise convergence along the time axis and asymptotic convergence along the iteration axis. The controller design is further extended to the case for systems with mixed parameters and unknown time‐varying input gain. Copyright © 2010 John Wiley & Sons, Ltd.Copyright © 2010 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society

A High-Order Internal Model Based Iterative Learning Control Scheme for Nonlinear Systems With Time-Iteration-Varying Parameters

In this technical note, we propose a new iterative learning control (ILC) scheme for nonlinear systems with parametric uncertainties that are temporally and iteratively varying. The time-varying characteristics of the parameters are described by a set of unknown basis functions that can be any continuous functions. The iteratively varying characteristics of the parameters are described by a high-order internal model (HOIM) that is essentially an auto-regression model in the iteration domain. The new parametric learning law with HOIM is designed to effectively handle the unknown basis functions. The method of composite energy function is used to derive convergence properties of the HOIM-based ILC, namely the pointwise convergence along the time axis and asymptotic convergence along the iteration axis. Comparing with existing ILC schemes, the HOIM-based ILC can deal with nonlinear systems with more generic parametric uncertainties that may not be repeatable along the iteration axis. The validity of the HOIM-based ILC under identical initialization condition (i.i.c.) and the alignment condition is also explored.

Generalized projective synchronization of chaotic systems via adaptive learning control

In this paper, a learning control approach is applied to the generalized projective synchronisation (GPS) of different chaotic systems with unknown periodically time-varying parameters. Using the Lyapunov–Krasovskii functional stability theory, a differential-difference mixed parametric learning law and an adaptive learning control law are constructed to make the states of two different chaotic systems asymptotically synchronised. The scheme is successfully applied to the generalized projective synchronisation between the Lorenz system and Chen system. Moreover, numerical simulations results are used to verify the effectiveness of the proposed scheme.

On-line optimization of fed-batch bioreactors by adaptive extremum seeking control

In this paper, we present an adaptive extremum seeking control scheme for fed-batch bioreactors with Haldane kinetics. The proposed adaptive extremum seeking approach utilizes the structure information of the process kinetics to derive a seeking algorithm that drives the system states to the desired set-points that maximize the biomass production. It assumes that only the substrate concentration is available for on-line measurement. Lyapunov stability is used in the design of the extremum seeking controller structure and the development of the parameter learning laws. The performance of the approach is illustrated via numerical simulations.

Extremum‐seeking control of retention for a microparticulate system

AbstractThe operation of a paper machine relies on the close monitoring and control of several integrated units to ensure a high quality paper with the required specifications. In this paper, the retention control system in the wet‐end of a paper machine is considered. The control objective is to maximize the retention of fines and fibres in the paper sheet to prevent the accumulation of micro particles in the water system. We present an adaptive extremum‐seeking scheme for the optimization and control of retention in the wet‐end of a paper machine. An adaptive learning technique is introduced to construct an algorithm that drives the system to the optimal retention value. Lyapunov's stability theory is used in the design of the extremum‐seeking controller structure and the development of the parameter learning laws. The performance of the technique is illustrated via simulations based on a first‐principles dynamic model developed previously for a micro‐particulate system.

Real‐time optimization of a tubular reactor with distributed feed

AbstractAn adaptive extremum seeking control scheme for a class of nonlinear distributed parameter systems is presented. It addresses the real‐time optimization of a parallel chemical reaction system in an isothermal tubular reactor with uniform distributed feed described by a set of hyperbolic partial differential equations. Only limited knowledge of the kinetics is assumed. An adaptive learning technique is introduced to design an extremum seeking algorithm that drives the system states to a set point that maximizes the value of an objective function. Lyapunov's stability theorem is used in the design of the extremum seeking controller structure and the development of the parameter learning laws. © 2006 American Institute of Chemical Engineers AIChE J, 2006

Adaptive extremum seeking control of continuous stirred tank bioreactors with unknown growth kinetics

In this paper, we present an adaptive extremum seeking control scheme for continuous stirred tank bioreactors. We assume limited knowledge of the growth kinetics. An adaptive learning technique is introduced to construct a seeking algorithm that drives the system states to the desired set-points that maximize the value of an objective function. Lyapunov's stability theorem is used in the design of the extremum seeking controller structure and the development of the parameter learning laws. Simulation results are given to show the effectiveness of the proposed approach.

Adaptive Extremum Seeking Control of Enzyme Production in Filamentous Fungal Fermentation

Abstract The aim of this paper is to investigate, via a case study, an adaptive extremum seeking control scheme for product formation in fed-batch bioreactors. The presented approach utilizes the structure information of the Haldane kinetic model, to derive an extremum seeking algorithm that drives the system to the desired set points with the objective to maximize the product formation rate. The adaptive extremum seeking algorithm consists of a control law and parameter learning laws designed by using Lyapunov's stability arguments. The adaptive extremum seeking control scheme is applied to the maximization of the enzyme production yield in filamentous fungal fermentation. Numerical simulations are provided in order to investigate the effectiveness of the proposed scheme.

Adaptive Extremum Seeking Control of Continuous Stirred Tank Bioreactors 1

In this paper, we present an adaptive extremum seeking control scheme for continuous stirred tank bioreactors. We assume limited knowledge of the growth kinetics. An adaptive learning technique is introduced to construct a seeking algorithm that drives the system states to the desired set-points that maximizes the value of an objective function. Lyapunov's stability theorem is used in the design of the extremum seeking controller structure and the development of the parameter learning laws. A simulation experiment is given to show the effectiveness of the proposed approach.

Adaptive Extremum Seeking Control of Nonisothermal Continuous Stirred Tank Reactors 1

Abstract In this paper, we present an adaptive extremum seeking control scheme for non-isothermal continuous stirred tank reactors. We assume limited knowledge of the reaction kinetics. An adaptive learning technique is introduced to construct an optimum seeking algorithm that drives the system states to optimal equilibrium concentrations of the reaction mixture. Lyapunov's stability theorem is used in the design of the extremum seeking controller structure and the development of the parameter learning laws. Under mild assumptions, the resulting controller is an output-feedback controller, the performance of the technique is demonstrated with the van de Vusse reaction.