Indirect Adaptive Control Method Research Articles

Artificial neural network‐based adaptive control for nonlinear dynamical systems

SummaryThis research article presents an artificial neural network (ANN)‐based indirect adaptive control method for nonlinear dynamical systems. In this article, a modified Elman recurrent neural network (MERNN) is proposed as an identifier and controller for controlling nonlinear systems. The architecture of the proposed controller is a modified form of the existing Elman recurrent neural network. The parameter training of ANN‐based controllers is obtained by using the most popular optimization algorithm which is known as the back‐propagation algorithm. A comparative study includes Elman, Diagonal, Jordan, feed‐forward neural network (FFNN), and radial basis function network (RBFN)‐based controllers to compare with the proposed MERNN controller. To determine the controller's robustness, parameter variations, and disturbance signals have been considered. The performance analysis of the proposed controller is illustrated by two simulation examples. The simulation results reveal that MERNN can not only identify the unknown dynamics of the plant but also adaptively control it compared to the others.

An improved indirect adaptive neural control performance based on MOPSO approach: An experimental validation via a transesterification reactor

This paper proposes an indirect adaptive control method based on recurrent neural networks. To achieve satisfactory closed-loop performances, a neural emulator (NE) and a neural controller (NC) adapting rates are established using the multiobjective particle swarm optimization (MOPSO) algorithm. The proposed MOPSO algorithm has been designed to minimize, simultaneously, two separated objective functions: the emulation and the tracking errors. The proposed approach guarantees that the NE tracks the system dynamics within a short time window. Consequently, it provides for the suggested control structure useful information to synthesize optimal adaptive rates of the NE and NC. To validate the effectiveness of the proposed MOPSO algorithm, a numerical example and an experimental validation on a chemical reactor are proposed.

Metaheuristic adaptive control based on polynomial regression and differential evolution for robotic manipulators

Adaptive control of systems consists of the online adjustment of their control parameters to deal with parametric uncertainties and disturbances. Indirect Adaptive Control (IAC) of electromechanical systems and robotic manipulators based on metaheuristic optimization has been shown to be competitive compared to other classical and advanced control schemes. In this approach, the identification of system model parameters is stated as an optimization problem and then solved online by metaheuristics. Subsequently, the identified model is used in a second stage of online metaheuristic optimization to predict the system behavior and tune its control parameters. Therefore, this approach can be computationally expensive and unaffordable for systems with more complex dynamics whose models are just as complex. Due to the above, this work proposes an indirect adaptive control method for robotic manipulators based on differential evolution (DE) optimization, which aims to reduce the computational cost of the online identification stage by using Polynomial Regressors (PR) whose adjustment can be performed through a closed-form solution. The proposed method is tested in simulation for a 2 d.o.f. robotic manipulator and the results are compared against the original indirect adaptive control approach based on metaheuristic optimization and also a controller optimized offline. The post-hoc pair-wise Friedman tests confirm that the proposed approach provides competitive results compared to the original approach, with significant savings in computational resources.

A New Indirect Adaptive Neural Control for Nonlinear Systems: A Real Validation on a Chemical Process

In the present work, an indirect adaptive neural control method for nonlinear systems having unknown dynamics is proposed. The proposed control architecture is composed by a neural emulator (NE) and a neural controller (NC) where a new decoupled variable learning rates (VLRs) combined with Taylor development (TD) are used to train the NE and the NC. The developed VLRs mixed with the TD (TDVLRs) ensure a quick adaptation of neural networks parameters guaranteeing a faster output convergence and reducing the tracking error. The effectiveness of the proposed TDVLRs is illustrated by simulation with a nonlinear dynamic system. In order to validate simulation results, an application on a transesterification reactors is, also, presented.

Adaptive neural consensus tracking control for multi-agent systems with unknown state and input hysteresis

An indirect adaptive consensus control method is presented for multi-agent systems (MASs) with unknown hysteresis states and input. All system states that can be utilized to design the controller are measured by the sensors subjected to hysteresis, and thus, the system state values are inaccurate. Meanwhile, it is difficult to compensate the input hysteresis for it is coupled with the state hysteresis. The unknown function from agent’s neighbors also increases the difficulty of controller design. To eliminate the influence of unknown input hysteresis, an inverse adaptive compensated method is presented. The problem of state hysteresis is addressed by designing two adaptive laws to approximate the upper and lower bounds of unknown hysteresis coefficient. Neural networks are introduced to handle the unknown dynamics of agent and its neighbors. The proposed control scheme can guarantee that the consensus errors of followers converge to a predefined interval of zero asymptotically. In addition, the transient performance of MASs can be further ensured. The simulation examples are included to verify the effectiveness of the presented control approach.

A Novel Non-integer Indirect Adaptive Control for Non-integer Order Systems with Non-prior Knowledge

In this study, a new non-integer indirect adaptive control method with reference model is suggested for the class of non-integer order systems. The objective of model reference control is to include the output of the given reference fractional model in tracking the output of a controlled plant by using the concept of on-line goal adaptation. The stability of the closed-loop system is analyzed via the Lyapunov method. Finally, Matlab simulation results are presented to illustrate the effectiveness of the proposed method of indirect fractional model reference adaptive control.

Performance Comparison of Neural Network Training Approaches in Indirect Adaptive Control

This paper proposes an indirect adaptive control method using neural network (NN) based on a variable learning rates (VLRs) combined with Taylor development (TD) for nonlinear systems. In the proposed control architecture, two neural network blocks are used both as an identifier and a controller. The tracking error and the identification error are used, respectively, to train the neural controller and the neural model. The NN identifier approximates dynamic systems and provides the NN controller with information about the system sensitivity. The gradient-descent method using a developed variable learning rate is mixed with the Taylor development and is applied to train all weights of the NN. The NN TD-VLRs are applied to guarantee the convergence of the proposed control system. The effectiveness of the proposed algorithm applied to an example of nonlinear dynamic systems is demonstrated by simulation experiments. The results of simulation show that applying the mixed proposed method ensures the smallest MSE and the optimal time simulation. Added to that, the neural network controller is insensitive to variations of the system parameters.

Energy saving control in separate meter in and separate meter out control system

With the demand for energy efficiency in electro-hydraulic servo system (EHSS) increasing, the separate meter in and separate meter out (SMISMO) control system draws massive attention. In this paper, the SMISMO control system was decoupled completely into two subsystems by the proposed indirect adaptive robust dynamic surface control (IARDSC) method. Besides, a fast parameter estimation scheme was proposed to adapt to the parameter change for a better estimation performance. Also, a supply pressure controller with a disturbance observer and a supply flow rate controller with a grey model predictor were investigated and employed to save the power consumption. Finally, experimental results showed that the proposed IARDSC could achieve a good trajectory tracking performance with the fast parameter estimation. Meanwhile, the two energy saving techniques were validated.

Indirect adaptive robust dynamic surface control in separate meter-in and separate meter-out control system

With the demand for energy efficiency in electrohydraulic servo systems (EHSS), the separate meter-in and separate meter-out (SMISMO) control system draws massive attention. In this paper, the SMISMO control system is decoupled completely into two subsystems by the proposed indirect adaptive robust dynamic surface control (IARDSC) method. Indirect adaptive robust control (IARC) is proposed to address the internal parameter uncertainties and external disturbances. Dynamic surface control (DSC) is utilized in the design procedure of IARC to deal with the inherent ‘explosion of terms’ problem. The proposed IARDSC simplifies the design procedure and decreases the computational cost of the controller. Besides, a faster parameter estimation scheme is proposed to adapt to the parameter change for a better estimation performance. Finally, experimental results show that the proposed IARDSC can achieve a good parameter estimation and trajectory tracking performance. Meanwhile, two energy saving techniques are discussed.

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 switching control for fuzzy dynamical systems based on T–S multiple models

A new indirect adaptive switching fuzzy control method for fuzzy dynamical systems, based on Takagi–Sugeno (T–S) multiple models is proposed in this article. Motivated by the fact that indirect adaptive control techniques suffer from poor transient response, especially when the initialisation of the estimation model is highly inaccurate and the region of uncertainty for the plant parameters is very large, we present a fuzzy control method that utilises the advantages of multiple models strategy. The dynamical system is expressed using the T–S method in order to cope with the nonlinearities. T–S adaptive multiple models of the system to be controlled are constructed using different initial estimations for the parameters while one feedback linearisation controller corresponds to each model according to a specified reference model. The controller to be applied is determined at every time instant by the model which best approximates the plant using a switching rule with a suitable performance index. Lyapunov stability theory is used in order to obtain the adaptive law for the multiple models parameters, ensuring the asymptotic stability of the system while a modification in this law keeps the control input away from singularities. Also, by introducing the next best controller logic, we avoid possible infeasibilities in the control signal. Simulation results are presented, indicating the effectiveness and the advantages of the proposed method.

Indirect adaptive robust predictive control of robotic manipulators based on uncertain parameter approximation

In order to make the robotic manipulator system track well and have strong disturbance-opposing ability under the circumstance of parametric uncertainty and external disturbance,an indirect adaptive robust predictive control method was presented.The nonlinear robust predictive controller was designed based on the robotic manipulator system.Then,the cubic spline functions controller was constructed to approximate the unknown terms in the predictive control law caused by system model uncertainties.And the D-controller was added into the control law to inhibit the external disturbance.It is proved that the proposed controller can make the tracking error converge to the origin.The simulation results show the effectiveness of the method.

A discrete-time parameter estimation based adaptive actuator failure compensation control scheme

This article studies discrete-time adaptive failure compensation control of systems with uncertain actuator failures, using an indirect adaptive control method. A discrete-time model of a continuous-time linear system with actuator failures is derived and its key features are clarified. A new discrete-time adaptive actuator failure compensation control scheme is developed, which consists of a total parametrisation of the system with parameter and failure uncertainties, a stable adaptive parameter estimation algorithm, and an on-line design procedure for feedback control. This work provides a new design of direct adaptive compensation of uncertain actuator failures, using an indirect adaptive control method. Such an adaptive design ensures desired closed-loop system stability and tracking properties despite uncertain actuator failures. Simulation results are presented to show the desired adaptive actuator failure compensation performance.

An indirect adaptive scheme for attenuation of multiple narrow-band disturbances: stability analysis

In this paper, a stability proof for an indirect adaptive control method used for the attenuation of multiple unknown time varying narrow-band disturbances is presented. The method is based on the real time estimation of the frequency of the unknown narrow-band disturbances using adaptive notch filters (ANF) followed by the design of a controller using adjustable band-stop filters (BSF) for the appropriate shaping of the output sensitivity function. An Youla-Kučera parametrization of the controller is used for reducing the computation load. Sufficient conditions for stability of the full scheme are stated and a stability proof is given. Finally, an experimental comparison with a direct adaptive method based on the internal model principle (IMP) is shown.

Indirect adaptive control of an autonomous underwater vehicle-manipulator system for underwater manipulation tasks

This paper presents an indirect adaptive control method for an autonomous underwater vehicle-manipulator system (UVMS) based on an extended Kalman filter (EKF). This method overcomes the disadvantages of existing disturbance observers and direct adaptive control schemes, which are based on linear system techniques and regressor-based techniques. The proposed control scheme can be applied to UVMSs for various purposes such as payload compensation, interaction effects compensation, underwater current or external disturbance compensation, reaction compensation, and independent system control. The performance of the proposed controller was demonstrated numerically by payload compensation, where it compensated for the reaction effects experienced during a manipulation task, and disturbance (underwater current) compensation in a UVMS with a six degrees of freedom (DOF) underwater vehicle and a 3-DOF underwater manipulator.

Adaptive robust H∞ state feedback control for linear uncertain systems with time‐varying delay

AbstractThis paper considers the problem of adaptive robust H∞ state feedback control for linear uncertain systems with time‐varying delay. The uncertainties are assumed to be time varying, unknown, but bounded. A new adaptive robust H∞ controller is presented, whose gains are updating automatically according to the online estimates of uncertain parameters. By combining an indirect adaptive control method and a linear matrix inequality method, sufficient conditions with less conservativeness than those of the corresponding controller with fixed gains are given to guarantee robust asymptotic stability and H∞ performance of the closed‐loop systems. A numerical example and its simulation results are given to demonstrate the effectiveness and the benefits of the proposed method. Copyright © 2008 John Wiley & Sons, Ltd.

Indirect adaptive control of nonlinear dynamic systems using self recurrent wavelet neural networks via adaptive learning rates

This paper proposes an indirect adaptive control method using self recurrent wavelet neural networks (SRWNNs) for dynamic systems. The architecture of the SRWNN is a modified model of the wavelet neural network (WNN). However, unlike the WNN, since a mother wavelet layer of the SRWNN is composed of self-feedback neurons, the SRWNN can store the past information of wavelets. In the proposed control architecture, two SRWNNs are used as both an identifier and a controller. The SRWNN identifier approximates dynamic systems and provides the SRWNN controller with information about the system sensitivity. The gradient-descent method using adaptive learning rates (ALRs) is applied to train all weights of the SRWNN. The ALRs are derived from discrete Lyapunov stability theorem, which are applied to guarantee the convergence of the proposed control system. Finally, we perform some simulations to verify the effectiveness of the proposed control scheme.

Wavelet Neural Network Based Indirect Adaptive Control of Chaotic Nonlinear Systems

In this paper, we present a indirect adaptive control method using a wavelet neural network (WNN) for the control of chaotic nonlinear systems without precise mathematical models. The proposed indirect adaptive control method includes the off-line identification and on-line control procedure for chaotic nonlinear systems. In the off-line identification procedure, the WNN based identification model identifies the chaotic nonlinear system by using the serial-parallel identification structure and is trained by the gradient-descent method. And, in the on-line control procedure, a WNN controller is designed by using the off-line identification model and is trained by the error back-propagation algorithm. Finally, the effectiveness and feasibility of the proposed control method is demonstrated with applications to the chaotic nonlinear systems.

Reconfigurable Flight Control System Design Using Direct Adaptive Method

A reconfigurable flight control system provides better survivability through the automatic reconfiguration of control system when faults occur during flight. The adaptive control method has been effectively applied to the reconfigurable flight control system design. However, reconfigurable flight control systems based on the indirect adaptive control method require persistent input excitation and smooth input-output data. To deal with the persistent input excitation problem and to obtain smooth control input, the system identification algorithm in reconfiguration flight control systems usually imposes some constraints on past input-output data, which may deteriorate the reconfiguration performance. Thus, a new reconfigurable flight control system based on the direct adaptive method is proposed to achieve better reconfiguration performance without the system identification process. The proposed control method uses a model following controller with direct adaptive update rules. To control the inner-loop states and the outer-loop states of the flight system simultaneously,the timescale separation principle is applied. The reconfiguration performance of the proposed control method is evaluated through numerical simulations using a six-degree-of-freedom nonlinear aircraft model.

Indirect adaptive control for a class of non-linear systems with a time-varying structure

In this paper we present an indirect adaptive control method for a class of uncertain non-linear systems with a timevarying structure. We view the non-linear systems as composed of a finite number of 'pieces', which are interpolated by functions that depend on a possibly exogenous scheduling variable. We assume that each piece is in strict feedback form, and show that the indirect adaptive method yields stability of all signals in the closed-loop, as well as convergence of the state vector to a residual set around the equilibrium, whose size can be set by the choice of several design constants. The class of systems considered here is a generalization of the class of strict feedback systems traditionally considered in the backstepping literature. Finally, we apply the indirect adaptive method to the problem of regulation of aircraft wing rock with a time-varying angle of attack.