Adaptive Neural Dynamic Surface Control Research Articles

Global adaptive neural dynamic surface control of strict-feedback systems

By mainly activating an auxiliary robust control component for pulling back the transient escaped from the neural active region, the author has proposed a multi-switching robust neuroadaptive controller to achieve globally uniformly ultimately bounded (GUUB) tracking stability of strict-feedback systems recently. However, the so-called explosion of complexity problem, arising from the repeated differentiations of the virtual controllers in the backstepping procedure, has not been solved yet. The objective of this paper is to tackle this issue via using the dynamic surface control (DSC) technique. By a suitable modification to the DSC scheme, the complexity in the neuroadaptive control component can be successfully conquered. The modification is two-fold, firstly, high-order instead of the common first-order filters are used, secondly, the filters׳ inputs are space-modulated and proven to be bounded, which in turn ensures the bounded stability of the filter dynamics. However, the repeated differentiations in the auxiliary robust controller remain as the price of preserving the global stability. Nevertheless, since the neuroadaptive controller dominates most of the time, the overall controller still significantly improves the existing global designs in terms of computation and implementation. Simulation results are provided to demonstrate the validity of the proposed scheme.

Adaptive neural dynamic surface control for a class of uncertain nonlinear systems with disturbances

In this paper, adaptive dynamic surface control is investigated for a class of uncertain nonlinear systems with unknown bounded disturbances in strict-feedback form. Dynamic surface control technique is connected with radial basis function neural networks (RBFNNs) based control framework to avoid the explosion problem of complexity. The composite laws are constructed by prediction error and compensated tracking error between system state and serial–parallel estimation model for NN weights updating. Using Lyapunov techniques, the uniformly ultimate boundedness stability of all the signals in the closed-loop systems is guaranteed. Simulation results illustrate the superiority of the proposed scheme and verify the theoretical analysis.

Neural network-based adaptive dynamic surface control for permanent magnet synchronous motors.

This brief considers the problem of neural networks (NNs)-based adaptive dynamic surface control (DSC) for permanent magnet synchronous motors (PMSMs) with parameter uncertainties and load torque disturbance. First, NNs are used to approximate the unknown and nonlinear functions of PMSM drive system and a novel adaptive DSC is constructed to avoid the explosion of complexity in the backstepping design. Next, under the proposed adaptive neural DSC, the number of adaptive parameters required is reduced to only one, and the designed neural controllers structure is much simpler than some existing results in literature, which can guarantee that the tracking error converges to a small neighborhood of the origin. Then, simulations are given to illustrate the effectiveness and potential of the new design technique.

Adaptive Neural Network Dynamic Surface Control for a Class of Time-Delay Nonlinear Systems With Hysteresis Inputs and Dynamic Uncertainties.

In this paper, an adaptive neural network (NN) dynamic surface control is proposed for a class of time-delay nonlinear systems with dynamic uncertainties and unknown hysteresis. The main advantages of the developed scheme are: 1) NNs are utilized to approximately describe nonlinearities and unknown dynamics of the nonlinear time-delay systems, making it possible to deal with unknown nonlinear uncertain systems and pursue the L∞ performance of the tracking error; 2) using the finite covering lemma together with the NNs approximators, the Krasovskii function is abandoned, which paves the way for obtaining the L∞ performance of the tracking error; 3) by introducing an initializing technique, the L∞ performance of the tracking error can be achieved; 4) using a generalized Prandtl-Ishlinskii (PI) model, the limitation of the traditional PI hysteresis model is overcome; and 5) by applying the Young's inequalities to deal with the weight vector of the NNs, the updated laws are needed only at the last controller design step with only two parameters being estimated, which reduces the computational burden. It is proved that the proposed scheme can guarantee semiglobal stability of the closed-loop system and achieves the L∞ performance of the tracking error. Simulation results for general second-order time-delay nonlinear systems and the tuning metal cutting system are presented to demonstrate the efficiency of the proposed method.

Learning from adaptive neural dynamic surface control of strict-feedback systems.

Learning plays an essential role in autonomous control systems. However, how to achieve learning in the nonstationary environment for nonlinear systems is a challenging problem. In this paper, we present learning method for a class of n th-order strict-feedback systems by adaptive dynamic surface control (DSC) technology, which achieves the human-like ability of learning by doing and doing with learned knowledge. To achieve the learning, this paper first proposes stable adaptive DSC with auxiliary first-order filters, which ensures the boundedness of all the signals in the closed-loop system and the convergence of tracking errors in a finite time. With the help of DSC, the derivative of the filter output variable is used as the neural network (NN) input instead of traditional intermediate variables. As a result, the proposed adaptive DSC method reduces greatly the dimension of NN inputs, especially for high-order systems. After the stable DSC design, we decompose the stable closed-loop system into a series of linear time-varying perturbed subsystems. Using a recursive design, the recurrent property of NN input variables is easily verified since the complexity is overcome using DSC. Subsequently, the partial persistent excitation condition of the radial basis function NN is satisfied. By combining a state transformation, accurate approximations of the closed-loop system dynamics are recursively achieved in a local region along recurrent orbits. Then, the learning control method using the learned knowledge is proposed to achieve the closed-loop stability and the improved control performance. Simulation studies are performed to demonstrate the proposed scheme can not only reuse the learned knowledge to achieve the better control performance with the faster tracking convergence rate and the smaller tracking error but also greatly alleviate the computational burden because of reducing the number and complexity of NN input variables.

Wheeled Mobile Robot RBFNN Dynamic Surface Control Based on Disturbance Observer

This paper focuses on the problem of an adaptive neural network dynamic surface control (DSC) based on disturbance observer for the wheeled mobile robot with uncertain parameters and unknown disturbances. The nonlinear observer is used to compensate for the external disturbance, and the neural network is employed to approximate the uncertain and nonlinear items of system. Then, the Lyapunov theory is introduced to demonstrate the stabilization of the proposed control algorithm. Finally, the simulation results illustrate that the proposed algorithm not only is superior to conventional DSC in trajectory tracking and external friction disturbance compensation but also has better response, adaptive ability, and robustness.

Adaptive control for a class of nonlinear time-delay systems preceded by unknown hysteresis

In this article, a robust adaptive neural dynamic surface control is proposed for a class of time-delay nonlinear systems preceded by saturated hystereses. Compared with the present schemes of dealing with time delay and hystereses input, the main advantages of the proposed scheme are that the prespecified transient and steady-state performance of tracking error can be guaranteed, the computational burden can be greatly reduced and the explosion of complexity problem inherent in backstepping control can be eliminated. Moreover, the utilisation of saturated-type Prandtl–Ishlinskii model makes our scheme more applicable. It is proved that the new scheme can guarantee all the closed-loop signals semiglobally uniformly ultimate bounded. Simulation results are presented to demonstrate the validity of the proposed scheme.

Dynamic Surface Neural Control for a Class of Nonlinear Time-Varying Delay Systems Preceded by Unknown Hysteresis

In this paper, a new robust adaptive neural dynamic surface control is proposed for a class of time-varying delay nonlinear systems preceded by backlash-like hysteresis. Compared with the present schemes of dealing with time-varying delay and hysteresis input, the main advantages of the proposed scheme are that the prespecified transient and steady state performance of tracking error can be guaranteed for the first time when using DSC to deal with the time-varing delays; the computational burden can be greatly reduced and the explosion of complexity problem inherent in backstepping control can be eliminated. It is proved that the new scheme can guarantee all the closed-loop signals semi-globally uniformly ultimate bounded. Simulation results are presented to demonstrate the validity of the proposed scheme.

Adaptive neural network dynamic surface control for perturbed nonlinear time-delay systems

This paper proposes an adaptive neural network control method for a class of perturbed strict-feedback nonlinear systems with unknown time delays. Radial basis function neural networks are used to approximate unknown intermediate control signals. By constructing appropriate Lyapunov-Krasovskii functionals, the unknown time delay terms have been compensated. Dynamic surface control technique is used to overcome the problem of "explosion of complexity" in backstepping design procedure. In addition, the semiglobal uniform ultimate boundedness of all the signals in the closed-loop system is proved. A main advantage of the proposed controller is that both problems of "curse of dimensionality" and "explosion of complexity" are avoided simultaneously. Finally, simulation results are presented to demonstrate the effectiveness of the approach.

Adaptive tracking control for a class of pure-feedback non-linear systems including actuator hysteresis and dynamic uncertainties

In this study, an adaptive neural network dynamic surface control for a class of pure-feedback non-linear systems with dynamic uncertainties and unknown hysteresis is proposed. The hysteresis is described by a saturated-type Prandtl–Ishlinskii (PI) model, which is more applicable than the traditional PI model. The main advantages of the authors scheme are that by introducing an initialising technique, the ℒ∞ performance of the tracking error can be achieved, the assumption on dynamic uncertainties is relaxed, the explosion of the complexity problem when the hysteresis is fused with back-stepping design can be eliminated, which together with the estimation of vector norm of unknown parameters makes the control law be simplified and the computational burden be greatly reduced. Simulation results are presented to demonstrate the efficiency of the proposed scheme.

Identifier-based adaptive neural dynamic surface control for uncertain DC–DC buck converter system with input constraint

In this paper, an identifier-based adaptive neural dynamic surface control (IANDSC) is proposed for the uncertain DC–DC buck converter system with input constraint. Based on the analysis of the effect of input constraint in the buck converter, the neural network compensator is employed to ensure the controller output within the permissible range. Subsequently, the constrained adaptive control scheme combined with the neural network compensator is developed for the buck converter with uncertain load current. In this scheme, a newly presented finite-time identifier is utilized to accelerate the parameter tuning process and to heighten the accuracy of parameter estimation. By utilizing the adaptive dynamic surface control (ADSC) technique, the problem of “explosion of complexity” inherently in the traditional adaptive backstepping design can be overcome. The proposed control law can guarantee the uniformly ultimate boundedness of all signals in the closed-loop system via Lyapunov synthesis. Numerical simulations are provided to illustrate the effectiveness of the proposed control method.

Adaptive neural dynamic surface control for servo systems with unknown dead-zone

An adaptive neural controller is proposed for nonlinear systems with a nonlinear dead-zone and multiple time-delays. The often used inverse model compensation approach is avoided by representing the dead-zone as a time-varying system. The “explosion of complexity” in the backstepping synthesis is eliminated in terms of the dynamic surface control (DSC) technique. A novel high-order neural network (HONN) with only a scalar weight parameter is developed to account for unknown nonlinearities. The control singularity and some restrictive requirements on the system are circumvented. Simulations and experiments for a turntable servo system with permanent-magnet synchronous motor (PMSM) are provided to verify the reliability and effectiveness.

Adaptive Neural Control of Pure-Feedback Nonlinear Time-Delay Systems via Dynamic Surface Technique

This paper is concerned with robust stabilization problem for a class of nonaffine pure-feedback systems with unknown time-delay functions and perturbed uncertainties. Novel continuous packaged functions are introduced in advance to remove unknown nonlinear terms deduced from perturbed uncertainties and unknown time-delay functions, which avoids the functions with control law to be approximated by radial basis function (RBF) neural networks. This technique combining implicit function and mean value theorems overcomes the difficulty in controlling the nonaffine pure-feedback systems. Dynamic surface control (DSC) is used to avoid "the explosion of complexity" in the backstepping design. Design difficulties from unknown time-delay functions are overcome using the function separation technique, the Lyapunov-Krasovskii functionals, and the desirable property of hyperbolic tangent functions. RBF neural networks are employed to approximate desired virtual controls and desired practical control. Under the proposed adaptive neural DSC, the number of adaptive parameters required is reduced significantly, and semiglobal uniform ultimate boundedness of all of the signals in the closed-loop system is guaranteed. Simulation studies are given to demonstrate the effectiveness of the proposed design scheme.

비선형 비행 시스템을 위한 H∞접근법 기반 적응 신경망 동적 표면 제어

In this paper, we propose an adaptive neural dynamic surface control (DSC) approach with tracking performance for full dynamics of nonlinear flight systems. It is assumed that the model uncertainties such as structured and unstrutured uncertainties, and external disturbances influence the nonlinear aircraft model. In our control system, self recurrent wavelet neural networks (SRWNNs) are used to compensate the model uncertainties of nonlinear flight systems, and an adaptive DSC technique is extended for the disturbance attenuation of nonlinear flight systems. All weights of SRWNNs are trained on-line by the smooth projection algorithm. From Lyapunov stability theorem, it is shown that performance nom external disturbances can be obtained. Finally, we present the simulation results for a nonlinear six-degree-of-freedom F-16 aircraft model to confirm the effectiveness of the proposed control system.