Neural Dynamic Surface Research Articles

Adaptive neural network dynamic surface optimal saturation control for single‐phase grid‐connected photovoltaic systems

AbstractAn adaptive neural network (NN) based optimal saturation control scheme is investigated for single‐phase grid‐connected photovoltaic (PV) systems by incorporating dynamic surface control (DSC) and adaptive dynamic programming (ADP) based on the backstepping control design framework. For each backstepping step, a critic‐actor architecture is constructed via reinforcement learning (RL), and the PV system is optimized according to the cost function in the architecture. Due to the nonlinearity, it is difficult to solve the Hamilton–Jacobi–Bellman (HJB) equation. The neural networks (NNs) are employed to approximate the solution of the HJB equation such that the optimal virtual control and the actual controller are obtained. By considering control input symmetric saturation nonlinearity link, constraints on pulse width modulation (PWM) are ensured. On this basis, the combination of backstepping control design and dynamic surface technique is used to overcome the shortcomings of “differential explosion” and simplify calculations. Based on the Lyapunov method, the stability analysis proves that all signals of the closed‐loop PV systems are semiglobally uniformly ultimately bounded (SGUUB). Simulation experiments and comparative results are given to verify the efficacy of the studied control strategy.

Neural learning control for sampled‐data nonlinear systems based on Euler approximation and first‐order filter

AbstractThe primary focus of this research paper is to explore the realm of dynamic learning in sampled‐data strict‐feedback nonlinear systems (SFNSs) by leveraging the capabilities of radial basis function (RBF) neural networks (NNs) under the framework of adaptive control. First, the exact discrete‐time model of the continuous‐time system is expressed as an Euler strict‐feedback model with a sampling approximation error. We provide the consistency condition that establishes the relationship between the exact model and the Euler model with meticulous detail. Meanwhile, a novel lemma is derived to show the stability condition of a digital first‐order filter. To address the non‐causality issues of SFNSs with sampling approximation error and the input data dimension explosion of NNs, the auxiliary digital first‐order filter and backstepping technology are combined to propose an adaptive neural dynamic surface control (ANDSC) scheme. Such a scheme avoids the ‐step time delays associated with the existing NN updating laws derived by the common ‐step predictor technology. A rigorous recursion method is employed to provide a comprehensive verification of the stability, guaranteeing its overall performance and dependability. Following that, the NN weight error systems are systematically decomposed into a sequence of linear time‐varying subsystems, allowing for a more detailed analysis and understanding. In order to ensure the recurrent nature of the input variables, a recursive design is employed, thereby satisfying the partial persistent excitation condition specifically designed for the RBF NNs. Meanwhile, it can verify that the NN estimated weights converge to their ideal values. Compared with the common ‐step predictor technology, there is no need to redesign the learning rules due to the designed NN weight updating laws without time delays. Subsequently, after capturing and storing the convergence weights, a novel neural learning dynamic surface control (NLDSC) scheme is specifically formulated by leveraging the acquired knowledge. The introduced methodology reduces computational complexity and facilitates practical implementation. Finally, empirical evidence obtained from simulation experiments validates the efficacy and viability of the proposed methodology.

Predefined‐time adaptive neural dynamic surface tracking control for high‐order nonlinear switched systems

Predefined‐time adaptive neural dynamic surface tracking control for high‐order nonlinear switched systems

Fixed-time adaptive neural tracking control for high-order nonlinear switched systems with input saturation and dead-zone

This article investigates the problem of tracking control for high-order switched nonlinear systems with input saturation and dead-zone. The uncertain nonlinear functions are estimated via applying radial basis function neural networks (RBF NNs). An improved transformation approach is designed to simplify the design complexity caused by input nonlinearities. In particular, a novel filter is presented to handle the difficulty of “explosion of complexity”. With the support of the common Lyapunov function (CLF) approach, a novel neural fixed-time dynamic surface control (DSC) scheme is proposed to assure all the signals in closed-loop systems are bounded and the output signal tracks the expected signal within fixed time. The simulation example illustrates the validity of the proposed control algorithm.

Neural adaptive dynamic surface control of an electro-hydraulic loading system for rail grinders

Neural adaptive dynamic surface control of an electro-hydraulic loading system for rail grinders

Adaptive neural dynamic surface control for nonstrict feedback systems with state delays

AbstractThis article focuses on a class of nonstrict feedback systems with input delay, state delays and time‐varying full‐state constraints by proposing an adaptive neural control scheme. To overcome the problems of all state variables effected by time‐varying constraints, the asymmetric time‐varying barrier Lyapunov functions are constructed. The influence of state delays and input delay is eliminated by employing suitable Lyapunov–Krasovskii functionals. Additionally, the process of controller design is based on backstepping method and the unknown functions can be approximated by radial basis function neural networks. Moreover, the problem of repeated differentiations for nonlinear components during controller design is hugely simplified by taking advantage of the dynamic surface control method. The boundness of all the closed‐loop signals can be ensured by the designed controller. Finally, two numerical simulations illustrate that the proposed adaptive neural control scheme is effective.

Neural adaptive dynamic surface control for position tracking of electrohydraulic servo systems based on extended state observer

High-performance motion tracking of electro-hydraulic servo systems plays an important role in developing industrial technology. However, the system itself has parameter uncertainty, uncertain nonlinearity, and measurement noise, significantly decreasing the performance of conventional controllers designed based on nominal models. To improve the tracking performance of electro-hydraulic servo systems, an adaptive dynamic surface control method combining extended state observer and radial basis function neural network is proposed. Firstly, an extended state observer combining neural network output and parameter adaptation technology is designed without the need for speed and pressure feedback to observe the system’s unknown state variables and nonlinear matched disturbance. The design of neural networks is used to approximate mismatched disturbance with significant nonlinearity online. Then, the proposed extended state observer is introduced into the neural adaptive backstepping design, effectively compensating for simultaneous matched and mismatched disturbances, thereby suppressing most of the uncertain nonlinearity of the system. Meanwhile, online update of uncertain parameters further improves the tracking performance of the controller. In addition, the introduction of dynamic surface overcomes the “explosion of complexity” issue in backstepping design. The rigorous stability of the closed-loop system is proved by the Lyapunov stability theory. Finally, the effectiveness of the proposed controller is validated through comparative experiments on valve-controlled hydraulic cylinder.

Adaptive fixed-time dynamic surface tracking control for high-order nonstrict-feedback nonlinear switched systems

This paper considers the adaptive fixed-time neural tracking dynamic surface control (DSC) for high-order nonlinear switched systems. The uncertain nonlinear functions are approximated by neural networks (NNs). And the obstacles of high-order terms are eliminated by the adding a power integrator. A novel fixed-time convergence filter is proposed to refrain from the problem of repeated differentiation of virtual controllers. On the basis of the backstepping control algorithm and the common Lyapunov function (CLF) method, a novel adaptive fixed-time neural DSC strategy is developed. It is proven that the states in the closed-loop system are bounded and the tracking error converges to an area of zero in fixed time. The simulation experiments further verify the feasibility of the mentioned method.

Adaptive Neural Network-Based Dynamic Surface Control for a 3-DoF Helicopter

Adaptive Neural Network-Based Dynamic Surface Control for a 3-DoF Helicopter

Research on Some Control Algorithms to Compensate for the Negative Effects of Model Uncertainty Parameters, External Interference, and Wheeled Slip for Mobile Robot

In this article, the research team systematically developed a method to model the kinematics and dynamics of a 3-wheeled robot subjected to external disturbances and sideways wheel sliding. These models will be used to design control laws that compensate for wheel slippage, model uncertainties, and external disturbances. These control algorithms were developed based on dynamic surface control (DSC). An adaptive trajectory tracking DSC algorithm using a fuzzy logic system (AFDSC) and a radial neural network (RBFNN) with a fuzzy logic system were used to overcome the disadvantages of DSC and expand the application domain for non-holonomic wheeled mobile robots with lateral slip (WMR). However, this adaptive fuzzy neural network dynamic surface control (AFNNDSC) adaptive controller ensures the closed system is stable, follows the preset trajectory in the presence of wheel slippage model uncertainty, and is affected by significant amplitude disturbances. The stability and convergence of the closed-loop system are guaranteed based on the Lyapunov analysis. The AFNNDSC adaptive controller is evaluated by simulation on the Matlab/simulink software R2022b and in a steady state. The maximum position error on the right wheel and left wheel is 0.000572 (m) and 0.000523 (m), and the angular velocity tracking error in the right and left wheels of the control method is 0.000394 (rad/s). The experimental results show the theoretical analysis’ correctness, the proposed controller’s effectiveness, and the possibility of practical applications. Orbits are set as two periodic functions of period T as follows.

Tracking control problem of nonlinear strict-feedback systems with input nonlinearity: An adaptive neural network dynamic surface control method.

In this work, the tracking control problem for a class of nonlinear strict-feedback systems with input nonlinearity is addressed. In response to the influence of input nonlinearity, an auxiliary control system is constructed to compensate for it. To process unknown nonlinear dynamics, radial basis function neural networks (RBFNNs) are introduced to approximate them, and some adaptive updating control laws are designed to estimate unknown parameters. Furthermore, during the dynamic surface control (DSC) design process, first-order low-pass filters are introduced to solve the complexity explosion problems caused by repeated differentiation. After that, an NN-based adaptive dynamic surface tracking controller is proposed to achieve the tracking control. By applying the proposed controller, it can be guaranteed that not only the output of the system can track the desired trajectory, but also that the tracking error can converge to a small neighborhood of zero, while all signals of the closed-loop system are bounded. Finally, the effectiveness of the proposed controller is verified through two examples.

Fixed-Time Neural Network-Based Dynamic Surface Control for Hypersonic Flight Vehicle With Historical Data Online Learning

Fixed-Time Neural Network-Based Dynamic Surface Control for Hypersonic Flight Vehicle With Historical Data Online Learning

Adaptive neural DSC for nonlinear switched systems with prescribed performance and input saturation

This paper solves the problem of an adaptive neural dynamic surface control for a class of uncertain strict-feedback nonlinear systems with guaranteed transient and steady-state performance under arbitrary switchings. First, by utilizing the prescribed performance control, the prescribed tracking control performance can be ensured, while the requirement for the initial error is removed. Second, radial basis function (RBF) neural networks (NNs) are used to handle unknown nonlinear functions, the Gaussian error function is employed to represent a continuous differentiable asymmetric saturation model and the dynamic surface control (DSC) technique is used to overcome the problem of 'explosion of complexity' inherent in the control design. At last, by using the common Lyapunov function method in combination with the backstepping technology, a common adaptive neural controller is constructed. The designed controller overcomes the problem of the over-parameterization, and further alleviates the computational burden. Under the proposed common adaptive controller, all the signals in the closed-loop system are bounded, and the prescribed transient and steady tracking control performance are guaranteed under arbitrary switchings. Simulation studies demonstrate the effectiveness of the proposed method.

Dynamic surface adaptive RBF neural network control for a class of non‐linear uncertain systems

AbstractAn adaptive neural network dynamic surface control method for strict feedback non‐linear systems with model uncertainty is proposed in this paper. The uncertain parts are approached by RBF neural network; meanwhile, the virtual controller is designed to make system stable according to dynamic surface control. The method of Lyapunov is used to prove the stability and convergence of the system. The simulation results prove the feasibility of this controller and prove that the controller has an advantage to approach the uncertain non‐linear system and make system have well convergence and traceability.

Adaptive neural dynamic surface composite control for path following of underactuated vessels with fixed-time convergence

The article discusses the path following control issue for underactuated marine surface vessels (MSVs) subject to nonlinear uncertainties. Firstly, the tracking goal of MSVs will be changed from the earth-fixed position to the line-of-sight (LOS) angle with the aid of LOS approach. To deal with the nonlinear uncertainties existing in MSVs, radial basis function neural networks (RBFNNs) are utilized. In particular, the fixed-time serial–parallel estimation models are used to boost the approximation of RBFNNs and determine whether RBFNNs really estimate the unknown nonlinearities by using the forecast and the track biases to change the weight of RBFNNs. Then, a fixed-time filter is produced to handle the ‘explosion of complex’ issue and improve the structure of the developed controller. In addition, the function tanh with smooth derivative function is utilized to overcome the singularity issue of the virtual controller derivations caused by fixed-time control. Finally, an adaptive neural fixed-time dynamic surface composite control method is developed for the path following control problem based on the adaptive backstepping control technique. The stability analysis results show the yaw angle ψ converges to the LOS angle ψs when t≥TS where Ts given later is not determined by the system initial variables. The system internal variables also reach a bounded compact set, which indicates the RBFNNs indeed estimate the uncertainties existing in MSVs by using fixed-time serial–parallel estimation models. And comparation simulation results also certificate the utilizability of the developed controller.

Neural Adaptive Dynamic Surface Asymptotic Tracking Control of Hydraulic Manipulators With Guaranteed Transient Performance.

In this article, a novel neural network (NN)-based adaptive dynamic surface asymptotic tracking controller with guaranteed transient performance is proposed for n-degrees of freedom (DOF) hydraulic manipulators. To fulfill the work, the entire manipulator system model, including hydraulic actuator dynamics, is first established. Then, the neural adaptive dynamic surface controller is designed, in which the NN is utilized to approximate the unknown joint coupling dynamics, while the approximation error and uncertainties of the actuator dynamics are addressed by the nonlinear robust control law with adaptive gains. In addition, a modified funnel function that ensures the joint tracking errors remains within a predefined funnel boundary and is skillfully incorporated into the adaptive dynamic surface control (ADSC) design to achieve a guaranteed transient tracking performance. The theoretical analysis reveals that both the guaranteed transient tracking performance and asymptotic stability can be achieved with the proposed controller. Contrastive simulations are performed on a 2-DOF hydraulic manipulator to demonstrate the superiority of the proposed controller.

Adaptive neural dynamic surface control of load/grid connected voltage source inverters with LC/LCL filters

Utilizing passive filters such as L, LC and LCL is preferred to cancel out high-frequency harmonics caused by pulse width modulation of voltage source inverters. However, the LC and LCL filters have shown better harmonic attenuation than the conventional L filter. Nevertheless, the control process of LC and LCL filters is more complicated due to their higher-order dynamics. The problem gets more challenging in the presence of uncertainties such as load and grid impedance variations. To overcome these challenges, two novel adaptive neural dynamic surface controllers are proposed for LC and LCL filters in the load and grid-connected modes, respectively. Meanwhile, the issue of computational complexity inherent in the conventional backstepping method is avoided here by utilizing the dynamic surface control technique. Furthermore, the matched and unmatched uncertainties of LC/LCL filters are approximated via multi-input multi-output radial basis function neural networks. Stability of the closed-loop systems is guaranteed by converging the tracking errors to a small neighborhood of the origin. Simulations are given to illustrate the effectiveness and potential of the proposed adaptive neural dynamic surface control methods under the load and grid impedance changes.

Finite-Time Adaptive Dynamic Surface Asymptotic Tracking Control of Uncertain Multi-Agent Systems with Unknown Control Gains

In this work, the finite-time asymptotic tracking control problem of uncertain multi-agent systems with unknown control gains is studied. For the unknown control gain of each subsystem in multi-agent systems, we consider using the Nussbaum gain function techniques to handle them. To deal with the unknown uncertain nonlinear dynamics, the radial basis function neural network is introduced in each step of the dynamic surface control design. In addition, a nonlinear compensating term with the estimation of an unknown bounded parameter is designed to avoid repeated differentiation of each virtual control law. Then, based on the neural network control method, dynamic surface control technique, and finite-time control theory, an adaptive neural network finite-time dynamic surface control law is finally designed. Using stability analysis, it is proven that the presented adaptive control law can guarantee all signals of the closed-loop system semi-global practical finite-time stable, and the tracking error of each follower agent can converge to a small neighborhood of zero in finite time. Finally, a class of single-link robot systems is provided to illustrate the effectiveness of the designed control law.

Neural Predictor-Based Dynamic Surface Parallel Control for MIMO Uncertain Nonlinear Strict-Feedback Systems

We propose a neural predictor-based dynamic surface parallel control method for a class of uncertain nonlinear systems in this brief. The dynamics of the physical system is in the strict-feedback form and subject to multi-input, multi-output, and uncertain nonlinearities. The parallel control method is developed based on an ACP methodology, including three steps. Firstly, an artificial system to the physical system is developed using an echo state network-based neural predictor structure. Next, a high-order tuner-based computational experiment is developed to achieve online adaptive training of the echo state network. Finally, parallel execution is developed by using a second-order linear tracking differentiator-based dynamic surface control approach. The total closed-loop system can be proved to be input-to-state stable. The effectiveness of the proposed theoretical results is demonstrated by a simulation of trajectory tracking of an autonomous surface vehicle.

Neural Networks-Based Adaptive Dynamic Surface Control for Nonlinear Pure-Feedback Systems under Prescribed Performance and Zero Tracking Error

Neural Networks-Based Adaptive Dynamic Surface Control for Nonlinear Pure-Feedback Systems under Prescribed Performance and Zero Tracking Error