Constrained adaptive neural network control of an MIMO aeroelastic system with input nonlinearities

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.

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.

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

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.

This article addresses the challenge of designing obstacle avoidance control strategies for unmanned ship systems operating in environments with moving obstacles and unmodeled dynamics. First, we utilize an enhanced artificial potential field method to generate real-time paths that allow unmanned ships to avoid obstacles effectively, overcoming the design challenges posed by moving obstacles. Next, we incorporate convex optimization techniques to create a novel adaptive neural network control strategy aimed at tackling potential dynamic uncertainties in unmanned ship systems. Finally, we present simulation results that demonstrate the effectiveness of the proposed dynamic obstacle avoidance control strategy.

Observer-based neural adaptive control for a class of MIMO delayed nonlinear systems with input nonlinearities

In this work, considering the influence of input saturation and time-varying output constraints, an adaptive neural networks force/position control method is proposed for a class of uncertain robotic manipulators in contact with the external environment. Radial basis function neural networks are introduced to approximate the system uncertainty and only one parameter needs to be updated online. The output constraints are satisfied by converting the constrained system to an unconstrained one. Moreover, an auxiliary dynamic system is applied in the transformed unconstrained system to handle the input saturation nonlinearity and the Moore–Penrose pseudo-inverse term is introduced for controller design. All signals in the closed-loop system are proved to be bounded by using the Lyapunov method. To show the good performance of the given scheme, comparative simulation is conducted on a two-joint manipulator, which is controlled by the proposed adaptive neural networks force/position tracking controller and a conventional force/position controller without considering input saturation and output constraints. Simulation results show that the adaptive neural network force/position controller has better control performance than the traditional method.

PurposeThis paper aims to design a fixed-time disturbance observer (FTDO)-based adaptive neural fixed-time control strategy for the uncertain medium-scale unmanned autonomous helicopter (UAH) with full-state constraints and external disturbances.Design/methodology/approachFirst, the tan-type barrier Lyapunov function (BLF) and neural network (NN) are constructed to deal with the full-state constraints and system uncertainties, respectively. Subsequently, the compound disturbances are estimated by FTDOs. Simultaneously, the FTDOs and fixed-time control strategy guarantees that the errors of disturbances and states converge to the desired region in fixed-time, and the upper bound on the convergence time of the FTDOs and the designed controller can be estimated through the devised parameters. In addition, Lyapunov stability theory proves that all states of the UAH system are semiglobally uniform ultimately bounded.FindingsThe designed controller combines the fixed-time stability theory with tan-type BLF, which proves that all states of the UAH can be constrained in a predefined region. Furthermore, the state and the disturbance errors converge to a desired region in fixed time, which improves the performance of the controller.Practical implicationsThe designed controller keeps the UAH within the safe range, reduces the accident rate and improves the operation efficiency.Originality/valueThere are few research studies on the constraints and convergence time of UAH. In this paper, based on the FTDO, an adaptive NN control strategy with tan-type BLF is proposed to ensure that the state errors can be constrained to a safety region and deal with the system uncertainties and the influence of external disturbances.

The control performance of quadrotor unmanned aerial vehicles (UAVs) in complex environments can be affected by external disturbances and other factors. In this paper, an adaptive neural network backstepping controller based on the barrier Lyapunov function (BLF) is designed for a quadrotor UAV with internal uncertainties, input–output constraints and external disturbances. Radial basis function neural networks are used to approximate the uncertainties in the dynamic model of the UAV, while the minimum parameter learning method is combined to accelerate the adjustment speed of neural network weights. A robust term is designed to balance the total system disturbance and improve the anti-interference performance. The BLF is used to handle the output constraint so that the constrained parameters cannot break the predefined constraints. An auxiliary system is introduced to solve input saturation and avoid the dependence of tracking error on the input amplitude in the method of approximating input saturation using the smoothing function. The stability of the control system is demonstrated by the Lyapunov method. The simulation results show that the proposed method has high tracking accuracy compared with the backstepping dynamic surface control method, and the input and output are in the predefined range.

In this paper, adaptive impedance control is developed for an ${n}$ -link robotic manipulator with input saturation by employing neural networks. Both uncertainties and input saturation are considered in the tracking control design. In order to approximate the system uncertainties, we introduce a radial basis function neural network controller, and the input saturation is handled by designing an auxiliary system. By using Lyapunov’s method, we design adaptive neural impedance controllers. Both state and output feedbacks are constructed. To verify the proposed control, extensive simulations are conducted.

The article proposes an estimator and command filtering-based adaptive neural network controller for the electrically driven flexible-joint robotic manipulators with output constraints under the circumstance of matched and mismatched disturbances in system dynamics. The presented method is designed based on electrically driven model of the n-link flexible-joint robotic manipulators, which introduces more uncertainties and increases the dimensionality of the system but is more in line with practical. In view of the properties of fast convergence speed and great estimation performance in radial basis function neural network, radial basis function neural network is used to approximate the internal uncertain dynamic parameters of the system. An observer-based estimator is introduced for estimating the matched and mismatched disturbances in flexible-joint robotic manipulator dynamics. As to the differential explosion problem in backstepping control design, this article utilizes second-order command filters to overcome it. This article also adopts barrier Lyapunov functions for implementing output constraint to consider security issues in practical use. For demonstrating the effectiveness of the proposed controller, numerical simulations on two-link flexible-joint robotic manipulators are conducted. On the basis of the comparisons among estimator and command filtering-based adaptive neural network controller and other advanced controllers, the superiorities of estimator and command filtering-based adaptive neural network controller in several areas are proved.

In this paper, the problem of spacecraft attitude control using adaptive neural network disturbance compensation technique is investigated. The proposed disturbance observer is developed based on the Radial Basis Function (RBF) neural network. Firstly, the RBF neural network algorithm and spacecraft dynamic model are given. Then, the RBF neural network observer is developed to estimate the external disturbance moment. Using the estimated information, an adaptive neural network disturbance compensation controller is designed. Meanwhile, the stability of closed-loop attitude control system is analyzed by using Lyapunov approach. The simulation results show that compared with the traditional PD controller, the developed control scheme can decrease the effect of the external disturbance and has a better control performance.

In this paper; a new robust adaptive neural network controller (RANNC) is presented for a class of non-affine nonlinear systems in the presence of unknown nonlinearities and disturbances. Firstly, the existence of an ideal implicit feedback linearisation control (IFLC) is established based on implicit function theory. Using Taylor series expansion, it is shown that the control of non-affine nonlinear systems is equivalent to the control ofaffine nonlinear systems in the neighbourhood of the operating trajectory under mild conditions. Then, a robust adaptive neural network control scheme is presented for the transformed nonlinear systems by using neural networks as universal approximators for the unknown system nonlinearities. The proposed RANNC can guarantee that all the signals in the closed-loop system are bounded and the tracking error asymptotically converges to zero. Simulation studies on the control of a continuously stirred tank reactor (CSTR) system are used to show the effectiveness of the scheme.

An adaptive neural network control scheme is developed for a class of nonlinear systems in the strict-feedback form. Compared with the existing approaches, the main advantage is that the developed scheme can be implemented by utilizing only one neural network approximator. Thus, the designed controller structure is simplified. In addition, less neural network can reduce the running cost in practical application. The developed neural network control scheme can achieve that all the signals of the closed-loop system are uniformly bounded and the tracking errors converge to an arbitrary small neighborhood around zero by selecting suitably design parameters.

This manuscript considers the finite time adaptive neural tracking control problem for a class of the nonstrict-feedback stochastic nonlinear systems with Bouc-Wen hysteresis input. During the design process, a Bouc-Wen model is first adopted to obtain the input hysteresis phenomenon. By fusion with the backstepping technique and the neural network approximation capability, the unknown nonlinearities are coped with, a constructive finite time adaptive neural network control strategy is proposed. Furthermore, the finite-time mean square stability of stochastic nonlinear systems is proved, and at last the effectiveness of the proposed control strategy is validated by the simulations.