Adaptive Neural Network Control for Uncertain Switched Nonlinear Systems With Time Delays

Adaptive tracking control for a class of switched uncertain nonlinear systems under a new state-dependent switching law

In this note, adaptive neural control is presented for a class of strict-feedback nonlinear systems with unknown time delays. Using appropriate Lyapunov-Krasovskii functionals, the uncertainties of unknown time delays are compensated for such that iterative backstepping design can be carried out. In addition, controller singularity problems are solved by using the integral Lyapunov function and employing practical robust neural network control. The feasibility of neural network approximation of unknown system functions is guaranteed over practical compact sets. It is proved that the proposed systematic backstepping design method is able to guarantee semiglobally uniformly ultimate boundedness of all the signals in the closed-loop system and the tracking error is proven to converge to a small neighborhood of the origin.

Adaptive neural network control for nonlinear output-feedback systems under disturbances with unknown bounds

This paper investigates a scheme of adaptive neural network control for a stochastic switched system with input saturation. The unknown smooth nonlinear functions are approximated directly by neural networks. A modified approach is proposed to deal with unknown functions with nonstrict feedback form in the design process. Furthermore, by combining the auxiliary design signal and the adaptive backstepping design, a valid adaptive neural tracking controller design algorithm is presented such that all the signals of the switched closed-loop system are in probability semiglobally, uniformly, and ultimately bounded, and the tracking error eventually converges to a small neighborhood of the origin in probability. In the end, the effectiveness of the proposed method is verified by a simulation example.

By the combination of the adaptive backstepping design with the dynamic surface control technique, an novel adaptive neural control approach is investigated for a class of pure-feedback stochastic nonlinear systems with multiple unknown time-varying delays. To overcome the design difficulty arising from the non-affine structure of pure-feedback stochastic systems, the mean value theorem is exploited. The design difficulties due to multiple unknown time-varying delay functions are overcome by using the function separation technique, the appropriate Lyapunov-Krasovskii functionals and the desirable property of hyperbolic tangent functions. The radial-basis-function (RBF) neural networks are utilized to approximate the unknown nonlinear functions. It is shown that the proposed control approach can guarantee that all signals of the closed-loop system are bounded in probability, and the tracking errors can be made arbitrarily small in probability by choosing suitable design parameters. Finally, simulation example is provided to demonstrate the effectiveness of the proposed control scheme.

This paper proposes an online learning adaptive neural network for small unmanned aerial rotorcraft to improve control performance during flight. Based on state error information, the weight matrix of the adaptive neural network can be updated on line by using lyapunov function. Therefore, no prior training data is needed for the training of the adaptive neural network. Combined with feedback control, the adaptive neural network can construct the map between the state error information and disturbances to compensate for system disturbances. The effectiveness of the proposed method is validated by a series of simulations and flight tests. Compared with feedback control method, the adaptive neural network control method can estimate and eliminate disturbances quickly to yield a good tracking performance.

Adaptive neural network tracking control for a class of unknown nonlinear time-delay systems

In this paper, a reinforcement learning-based adaptive control algorithm is proposed to solve the tracking problem of a discrete-time (DT) nonlinear state and input time delayed system of the wheeled mobile robot (WMR). With the typical model of the WMR transformed into an affine nonlinear DT system, a delay matrix function and appropriate Lyapunov-Krasovskii functionals are introduced to overcome the problems caused by the state and input time delays, respectively. Furthermore, with the approximation of the radial basis function neural networks (NNs), the adaptive controller, the critic NN, and action NN adaptive laws are defined to guarantee the uniform ultimate boundedness of all signals in the WMR system, and the tracking errors convergence to a small compact set to zero. Two examples of simulation are given to illustrate the effectiveness of the proposed algorithm.

Data-based adaptive neural network optimal output feedback control for nonlinear systems with actuator saturation

This brief addresses the problem of designing adaptive neural network tracking control for a class of strict-feedback systems with unknown time-varying disturbances of known periods which nonlinearly appear in unknown functions. Multilayer neural network (MNN) and Fourier series expansion (FSE) are combined into a novel approximator to model each uncertainty in systems. Dynamic surface control (DSC) approach and integral-type Lyapunov function (ILF) technique are combined to design the control algorithm. The ultimate uniform boundedness of all closed-loop signals is guaranteed. The tracking error is proved to converge to a small residual set around the origin. Two simulation examples are provided to illustrate the feasibility of control scheme proposed in this brief.

The H∞ tracking control problem is investigated for a class of incommensurate fractional order strict feedback nonlinear systems with unknown nonlinear functions and external disturbances in this paper. The adaptive neural network H∞ tracking controller of the system is designed by combining backstepping technique, adaptive neural network technique with H∞ control theory and utilizing the frequency distributed model to construct Lyapunov functions. The H∞ tracking controller proposed in this paper can guarantee that the tracking error and all the signals in the closed-loop system are bounded, and the system has an H∞ performance for external disturbances. Finally, the simulation results verify the feasibility and effectiveness of the proposed control method.

In this paper, a leader–follower formation control strategy is presented based on adaptive neural network and disturbance observer, which is aimed at resolving model uncertainties as well as the time-varying disturbances for autonomous underactuated surface vessels. The model uncertainties which can be expressed by unknown nonlinear functions are approximated and compensated by the adaptive neural network. The disturbance observer introduced can estimate time-varying disturbances and compensate them to the feedforward control loop, so as to make the external time-varying disturbances suppressed and the robustness of controller against the disturbances improved. The dynamic surface control technology is applied in the procedure of designing the controller through utilizing the backstepping method, which solves the computational explosion of the derivative of virtual control signals. Finally, through Lyapunov analysis, the stability of adaptive neural formation control system is proved and all the error signals uniformly converge to a very small range ultimately. The excellent performance of the presented formation control strategy is demonstrated through numerical simulations.

In this article, the event‐triggered adaptive neural networks tracking control problem for nonlinear systems with prescribed performance and actuator fault is investigated. Meanwhile, bias faults and loss of effectiveness in actuators are taken into consideration, and the effectiveness factor is unknown. In the process of controller design under the framework of backstepping technology, neural networks are implemented to model the unknown terms of systems, and the tracking error is limited to a predefined boundary by using an error transformation. To economize communication resources, an adaptive neural networks event‐triggered control (ETC) strategy is developed, which can ensure that all the closed‐loop signals are bounded, and the tracking error not only confines to a prescribed function but also asymptotically converges to zero. Finally, two simulation examples are presented to further confirm the effectiveness of the proposed control strategy.

In this paper, an adaptive neural network (NN) tracking controller is developed for a class of uncertain multi-input multi-output (MIMO) nonlinear systems with input saturation. Radial basis function neural networks are utilized to approximate the unknown nonlinear functions in the MIMO system. A novel auxiliary system is developed to compensate the effects induced by input saturation (in both magnitude and rate) during tracking control. Endowed with a switching structure that integrates two existing representative auxiliary system designs, this novel auxiliary system improves control performance by preserving their advantages. It provides a comprehensive design structure in which parameters can be adjusted to meet the required control performance. The auxiliary system signal is utilized in both the control law and the neural network weight-update laws. The performance of the resultant closed-loop system is analyzed, and the bound of the transient error is established. Numerical simulations are presented to demonstrate the effectiveness of the proposed adaptive neural network control.

The stability of a hybrid AC-DC microgrid depends mainly upon the bidirectional interlinking converter (BIC), which is responsible for power transfer, power balance, voltage solidity, frequency and transients sanity. The varying generation from renewable resources, fluctuating loads, and bidirectional power flow from the utility grid, charging station, super-capacitor, and batteries produce various stability issues on hybrid microgrids, like net active-reactive power flow on the AC-bus, frequency oscillations, total harmonic distortion (THD), and voltage variations. Therefore, the control of BIC between AC and DC buses in grid-connected hybrid microgrid power systems is of great importance for the quality/smooth operation of power flow, power sharing and stability of the whole power system. In literature, various control schemes are suggested, like conventional droop control, communication-based control, model predictive control, etc., each addressing different stability issues of hybrid AC-DC microgrids. However, model dependence, single-point-failure (SPF), communication vulnerability, complex computations, and complicated multilayer structures motivated the authors to develop online adaptive neural network (NN) Q-learning-based full recurrent adaptive neurofuzzy nonlinear control paradigms for BIC in a grid-connected hybrid AC-DC microgrid. The proposed strategies successfully ensure the following: (i) frequency stabilization, (ii) THD reduction, (iii) voltage normalization and (iv) negligible net active-reactive power flow on the AC-bus. Three novel adaptive NN Q-learning-based full recurrent adaptive neurofuzzy nonlinear control paradigms are proposed for PQ-control of BIC in a grid-connected hybrid AC-DC microgrid. The control schemes are based on NN Q-learning and full recurrent adaptive neurofuzzy identifiers. Hybrid adaptive full recurrent Legendre wavelet-based Neural Network Q-learning-based full recurrent adaptive NeuroFuzzy control, Hybrid adaptive full recurrent Mexican hat wavelet-based Neural Network Q-learning-based full recurrent adaptive NeuroFuzzy control, and Hybrid adaptive full recurrent Morlet wavelet-based Neural Network Q-learning-based full recurrent adaptive NeuroFuzzy control are modeled and tested for the control of BIC. The controllers differ from each other, based on variants used in the antecedent part (Gaussian membership function and B-Spline membership function), and consequent part (Legendre wavelet, Mexican hat wavelet, and Morlet wavelet) of the full recurrent adaptive neurofuzzy identifiers. The performance of the proposed control schemes was validated for various quality and stability parameters, using a simulation testbench in MATLAB/Simulink. The simulation results were bench-marked against an aPID controller, and each proposed control scheme, for a simulation time of a complete solar day.