Adaptive Neural Dynamic Surface Control Research Articles

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

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.

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 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.

A yaw stability-guaranteed hierarchical coordination control strategy for four-wheel drive electric vehicles using an unscented Kalman filter

A novel hierarchical coordination control strategy (HCCS) is offered to guarantee the stability of four-wheel drive electric vehicles (4WD-EVs) combining the Unscented Kalman filter (UKF). First, a dynamics model of the 4WD-EVs is established, and the UKF-based estimator of sideslip angle and yaw rate is constructed concurrently. Second, the equivalent cornering stiffness coefficients are jointly estimated to consider the impact of vehicle uncertainty parameters on the estimator design. Afterwards, a HCCS with two-level controller is presented. The sideslip angle and yaw rate are controlled by an adaptive backstepping-based yaw moment controller, and the computational burden is relieved by an improved adaptive neural dynamic surface control technology in the upper-level controller. Simultaneously, the optimal torque distribution controller of hub motors is developed to optimize the adhesion utilization ratio of tire in the lower-level controller. Finally, the proposed HCCS has shown effective improvement in the trajectory tracking capability and yaw stability of the 4WD-EVs under various maneuver conditions compared with the traditional Luenberger observer-based and the federal-cubature Kalman filter-based hierarchical controller.

Adaptive neural dynamic surface control with fixed-time prescribed performance for uncertain nonstrict-feedback stochastic switched systems

An adaptive neural dynamic surface control (DSC) problem with fixed-time prescribed performance (FTPP) is investigated for a class of nonstrict-feedback stochastic switched systems. Differently from the existing works for FTPP problem, the stochastic switched systems with nonstrict-feedback form and completely unknown systems are considered in this paper, and the unknown functions are approximated by some radial basis function (RBF) neural networks (NNs). The desired adaptive neural controller is designed by using common Lyapunov function method and defining fixed-time prescribed performance function (PPF). And based on the adaptive DSC scheme with the nonlinear filter, the “explosion of complexity” problem is avoided. Besides, the constructed fixed-time PPF just need to meet the requirement of second derivative exists. According to the Lyapunov stability theory, the FTPP of output tracking error is achieved, and all signals of closed-loop system remain bounded in probability. Finally, simulation results are presented to verify the availability of the designed control strategy.

Adaptive neural dynamic surface control for uniform energy exploitation of floating wind turbine

To avoid the harmful effects of global warming on the earth planet and its atmosphere, the expansion of wind energy consumption based on new control techniques leads to improved energy capacity. The development of wind power plants on the sea is more efficient owing to the stronger wind flows in comparison with the onshore structure. Furthermore, stable installation of the offshore wind energy base meets the energy management requirements of today's consumers. As a result, dependency on fossil fuels as the main source of global warming decreases. The feedback control system and on-line sensor signals provide the stability and increased efficiency of the floating wind turbine. The aggressive environment and large size structure of the turbine lead to entering uncertainty in the turbine model and exogenous noises that should not be effectively compensated by conventional control methods. Therefore, a radial based functional neural network (RBFNN) controller is proposed to estimate and compensate the effect of uncertainty on feedback control of the wind turbine. For comparison purposes, a linear quadratic regulator (LQR) state feedback is also developed for optimal control of the floating wind turbines. The neural network adaptively determines the upper bound of uncertainty/noise. Therefore, conservative high-gain control actions to achieve robustness of the classical feedback controllers against structural deviations of the turbine body are decreased. In our newly proposed adaptive control algorithm, using a basic raised cosine function guides to restore the computational efficiency of RBFNN. With the Lyapunov-based stability analysis, the final limits of the closed-loop system and the convergence caused by the terminal sliding mode (TSM) tracking error are determined. Detailed software simulations of both the LQR and the designed RBFNN control systems indicate the superiority of the neural network-based approach.

Adaptive neural network dynamic surface control of uncertain strict-feedback nonlinear systems with unknown control direction and unknown actuator fault

In this paper, the tracking control problem of a class of uncertain strict-feedback nonlinear systems with unknown control direction and unknown actuator fault is studied. By using the neural network control approach and dynamic surface control technique, an adaptive neural network dynamic surface control law is designed. Based on the neural network approximator, the uncertain nonlinear dynamics are approximated. Using the dynamic surface control technique, the complexity explosion problems in the design of virtual control laws and adaptive updating laws can be overcome. Moreover, to solve the unknown control direction and unknown actuator fault problems, a type of Nussbaum gain function is incorporated into the recursive design of dynamic surface control. Based on the designed adaptive control law, it can be confirmed that all of the signals in the closed-loop system are semi-global bounded, and the convergence of the tracking error to the specified small neighborhood of the origin could be ensured by adjusting the designing parameters. Finally, two examples are provided to demonstrate the effectiveness of the proposed adaptive control law.

Adaptive Neural Digital Control of Hysteretic Systems With Implicit Inverse Compensator and Its Application on Magnetostrictive Actuator.

Hysteresis is a complex nonlinear effect in smart materials-based actuators, which degrades the positioning performance of the actuator, especially when the hysteresis shows asymmetric characteristics. In order to mitigate the asymmetric hysteresis effect, an adaptive neural digital dynamic surface control (DSC) scheme with the implicit inverse compensator is developed in this article. The implicit inverse compensator for the purpose of compensating for the hysteresis effect is applied to find the compensation signal by searching the optimal control laws from the hysteresis output, which avoids the construction of the inverse hysteresis model. The adaptive neural digital controller is achieved by using a discrete-time neural network controller to realize the discretization of time and quantizing the control signal to realize the discretization of the amplitude. The adaptive neural digital controller ensures the semiglobally uniformly ultimately bounded (SUUB) of all signals in the closed-loop control system. The effectiveness of the proposed approach is validated via the magnetostrictive-actuated system.

Stability control of a fixed full-wing layout UAV under manipulation constraints

The fixed full-wing layout unmanned aerial vehicle (UAV) has simple structure and high aerodynamic efficiency, but the special configuration and manipulation characteristics bring challenges to the controller design, which has little related research work. Aiming at the aileronless and rudderless manipulation mode, considering the disturbance and uncertainty, this paper proposes a comprehensive stability controller for the fixed full-wing layout UAV under manipulation constraints. First, adaptive neural network dynamic surface control schemes based on the asymmetric barrier Lyapunov function and auxiliary system are designed for pitch and yaw attitude control. Then, this paper combines the adaptive super-twisting observer, terminal sliding mode control, and double power reaching law to design the airspeed controller and the altitude controller, and further introduces the pitch angle command constraint in the altitude controller. In addition, an improved vector field-based lateral-directional path following control method is designed to realize straight-line and circular orbit path following control. In this paper, the approximation and compensation effects of the radial basis function neural network and robust compensator are fully verified. The design of the pitch angle command constraint specifically considers the characteristics of weak pitch manipulation ability, small available trim airspeed range, and low longitudinal moment of inertia. The developed path following control approach effectively prevents the nonlinear tracking differentiator from generating unreasonable yaw angle commands. Simulation results also show that the proposed controller can enable the fixed full-wing layout UAV to achieve the typical three-dimensional mission flight with high robustness and high precision, and make the fixed full-wing layout UAV remain stable even after suffering stall and manipulation saturation under the action of the gust wind field.

Adaptive Asymptotic Regulation for Uncertain Nonlinear Stochastic Systems with Time-Varying Delays

In this paper, for a class of uncertain stochastic nonlinear systems with input time-varying delays, an adaptive neural dynamic surface control (DSC) method is proposed. To approximate the unknown continuous functions online, the neural network approximation technique was applied, and based on the DSC scheme, the desired controller was constructed. A compensation system is presented to compensate for the effect of the input delay. The Lyapunov–Krasovskii functionals (LKFs) were employed to compensate for the effect of the state delay. Compared with the existing works, based on using the DSC scheme with the nonlinear filter and stochastic Barbalat’s lemma, the asymptotic regulation performance of this closed-loop system can be guaranteed under the developed controller. To certify the availability for the designed control method, some simulation results are presented.

Event-triggered fixed-time adaptive neural dynamic surface control for stochastic non-triangular structure nonlinear systems

The problem of event-triggered fixed-time adaptive neural dynamic surface control (DSC) for stochastic non-triangular structure nonlinear systems is discussed in this article. Combined with the fixed-time stability theory, DSC technique and event-triggered control (ETC) technique, a novel event-triggered fixed-time adaptive controller is designed, under which both the closed-loop stability and the tracking performance can be guaranteed simultaneously in a fixed time. At the same time, the problems of “explosion of complexity” and “singularity” under the traditional backstepping design framework are avoided. Moreover, the design of event-triggered control mechanism can save the network resources effectively. In addition, the unknown nonlinear functions are approximated by some radial basis function neural networks (RBFNNs), and the filtering errors are compensated by the novel error compensating signals. Rigorous theoretical derivation and two simulations are included to illustrate the effectiveness of the proposed method.

Adaptive cooperative dynamic surface control of non-strict feedback multi-agent systems with input dead-zones and actuator failures

This paper discusses the consensus tracking problem for a class of nonlinear multi-agent systems (MASs) with output constraints, unmodeled dynamics, nonsymmetric input dead-zones and actuator failures under directed graphs, and proposes an adaptive cooperative neural dynamic surface control (DSC) strategy. Using the properties of invertible nonlinear mapping and Gaussian function, output constraints and non-strict feedback terms are separately dealt with. A measurable dynamic signal produced by an auxiliary first-order system is used to eliminate the influence of unmodeled dynamics on the system. Two input models of input dead-zone and actuator failure are linearized, each follower control signal is constructed via DSC. All the signals of the closed-loop system are proved to be cooperative semi-globally uniformly ultimately bounded, and all the followers can accomplish a desired consensus results. Finally, the simulation results are provided to illustrate the availability of the presented adaptive control approach.

Finite‐time adaptive neural dynamic surface control for non‐linear systems with unknown dead zone

Finite‐time adaptive neural dynamic surface control for non‐linear systems with unknown dead zone

Adaptive Neural Dynamic Surface Control for the Chaotic PMSM System with External Disturbances and Constrained Output

Background:This article studies the issue of adaptive neural dynamic surface control for the chaotic permanent magnet synchronous motor system with constrained output, external disturbances and parameter perturbations.Methods:Firstly, a virtual controller and two practical controllers are created based on the backstepping framework. In the process of creating controllers, adaptive technique and radial basis function neural networks are used to handle unknown parameters and nonlinearities, respectively. The nonlinear damping items are applied to overcome external disturbances. The barrier Lyapunov function is used to prevent the violation of system output constraint. Meanwhile, the first-order filter to eliminate the “explosion of complexity” of traditional back stepping has been introduced. Then, it is proved that all the closed-loop signals are uniform ultimate asymptotic stability and the tracking error converges to a small set of origin.Results:The effectiveness and robustness of the developed approach are illustrated by numerical simulations.Conclusion:The raised control scheme is a useful tool for enhancing the performance of the chaotic PMSM system with external disturbances, constrained output and parameter perturbations.