Adaptive Neural Network Backstepping Control Research Articles

Adaptive neural network backstepping control method for aerial manipulator based on coupling disturbance compensation

The aerial manipulator, designed for complex aerial tasks, encounters multifaceted operational environments influenced by various internal and external disturbances. This paper introduces an adaptive neural network backstepping control technique fortified with coupling disturbance compensation to enhance the resilience of the aerial manipulator against these disturbances. Firstly, we propose a cutting-edge coupling disturbance feedforward compensator based on variable inertia parameters, which offers precise and prompt compensation for significant internal coupling disturbances without needing external sensors or alternative disturbance estimation techniques. Subsequently, radial basis function neural networks with an online adaptive weight updating mechanism are designed to estimate and counteract lumped disturbances stemming from unmodeled dynamics, uncertainties, and external factors in real-time. Utilizing the Lyapunov stability criteria, we validate that the aerial manipulator can reliably track desired trajectories under our proposed controller. Experimental results and simulations further underscore the effectiveness and superiority of our control approach.

Distributed convex optimization of higher order nonlinear uncertain multi-agent systems with switched parameters and topologies

This paper investigates the distributed optimization problem for a class of nonlinear uncertain multi-agent systems with unmeasured states, switched parameters, and directed communication topologies changing in the control process. To achieve the goal of optimizing the global objective function, a penalty function is constructed through making up of a sum of local objective functions and integrating consensus conditions of the multi-agent systems to utilize local and neighboring information. Radial basis function neural-networks and neural-networks state observer are applied to approximate the unknown nonlinear functions and obtain the unmeasured states, respectively. To avoid “explosion of complexity” and obtain derivatives for virtual control functions continuously, dynamic surface control technology is proposed to develop a distributed adaptive backstepping neural network control protocol to ensure that all the agents’ outputs asymptotically reach consensus to the optimal solution of the global objective function. Simulations demonstrate the effectiveness of the proposed control scheme.

COMPOSITE NEURAL NETWORK LEARNING FROM FRACTIONAL BACKSTEPPING

This paper focuses on parameter convergence and precise modeling for fractional-order nonlinear systems with functional uncertainties via using adaptive backstepping neural network control (ABNNC) and composite learning adaptive backstepping neural network control (CLABNNC). In the ABNNC design, a command filter is proposed, and the neural network approximation system is considered to deal with the unknown function, where an adaptation law is designed to ensure tracking errors converge to an arbitrarily small region near the origin under a strict persistent excitation condition that is too strict for the convergence of adaptive parameters. In order to relax this condition, a composite learning adaptation law is established by taking advantage of the tracking error and the prediction error to update the free parameter of the neural network system. The proposed CLABNNC method can not only ensure the convergence of tracking errors, but also achieve the accurate approximation of functional uncertainties under a weaker interval excitation condition. Finally, a numerical simulation example is put forward to demonstrate the effectiveness of our method.

Improved adaptive NN backstepping control design for a perturbed PVTOL aircraft

This paper proposes an improved flight control scheme for a perturbed planar vertical take-off and landing (PVTOL) aircraft based on backstepping and neural networks. In this approach, three neural networks are applied to deal with the external disturbances of the aircraft system. Instead of using σ-modification to design the adaptive laws, gradient descent algorithm is adopted to train the weight parameters of neural networks such that high function approximation precision can be achieved. By resorting to Lyapunov stability criterion, it can be proved that the reference signals are tracked by the aircraft’s outputs with small errors. In the end, simulation results are used to illustrate the superiority of the our approach compared with traditional direct adaptive neural network backstepping control technique.

A new robust adaptive neural network backstepping control for single machine infinite power system with TCSC

For a single machine infinite power system with thyristor controlled series compensation &#x0028 TCSC &#x0029 device, which is affected by system model uncertainties, nonlinear time-delays and external unknown disturbances, we present a robust adaptive backstepping control scheme based on the radial basis function neural network &#x0028 RBFNN &#x0029 . The RBFNN is introduced to approximate the complex nonlinear function involving uncertainties and external unknown disturbances, and meanwhile a new robust term is constructed to further estimate the system residual error, which removes the requirement of knowing the upper bound of the disturbances and uncertainty terms. The stability analysis of the power system is presented based on the Lyapunov function, which can guarantee the uniform ultimate boundedness &#x0028 UUB &#x0029 of all parameters and states of the whole closed-loop system. A comparison is made between the RBFNN-based robust adaptive control and the general backstepping control in the simulation part to verify the effectiveness of the proposed control scheme.

Adaptive neural fault-tolerant control for a class of strict-feedback nonlinear systems with actuator and sensor faults

This paper investigates the adaptive neural fault-tolerant control problem for a class of strict-feedback nonlinear systems with simultaneous actuator and sensor faults. The faults considered in this paper are bias (lock-in-place), drift, loss of accuracy, and loss of effectiveness faults. Only one parameter law is updated at each step to reduce the computational burden. By utilizing the adaptive neural network backstepping control strategy, the closed-loop nonlinear system is guaranteed to be semi-globally uniform ultimate bounded, and all the signals are bounded. Finally, a simulation example is given to show the effectiveness of the proposed control strategy.

Adaptive NN Backstepping Control Design for a 3-DOF Helicopter: Theory and Experiments

This paper presents an adaptive neural network backstepping control design for an experimental helicopter with three rotational degrees of freedom. The helicopter system is described by a state-space model with unmodeled dynamics and external disturbances. By resorting to radial basis function neural networks with online gradient descent training, such unmodeled dynamics and external disturbances can be compensated in real time. With the help of Lyapunov stability criteria, it is proved that the desired trajectories can be boundedly tracked by the corresponding attitude angles of the helicopter under the proposed adaptive neural network backstepping controller. Compared with robust adaptive integral backstepping technique, our approach has smaller root mean squared error. Finally, four sets of contrastive experiment results are, respectively, given to show the effectiveness and advantages of the proposed control scheme.

Adaptive neural network backstepping control of fractional-order Chua\u2013Hartley chaotic system

In this paper, the control of uncertain fractional-order Chua–Hartley (FOCH) chaotic systems by means of adaptive neural network backstepping control is considered. Neural network is utilized as a universal approximator to estimate the unknown nonlinear function. By using the fractional Lyapunov stability criterion and the backstepping technique, an adaptive neural network control (ANNC) method is implemented. In each backstepping step, the unknown nonlinear functions are approximated by neural networks, and a virtual control input is designed. The proposed method guarantees that all the closed-loop signals keep bounded and tracking error converges to an arbitrary small region of zero. Finally, numerical simulation is given to confirm the effectiveness and the good control performance of the proposed method.

Adaptive backstepping neural network control for three dimensions trajectory tracking of robotic airships

The robotic airship provides a unique aerostatic platform for various applications, and these applications require high-precise trajectory tracking. However, it is a challenging problem due to nonlinearity and uncertainty of airship dynamics. This paper proposes an adaptive backstepping neural network control (ABNNC) approach to address this problem. First, the kinematics model and dynamics model of the robotic airship are presented. Second, the control problem of trajectory tracking is formulated, and a trajectory controller is designed using backstepping approach. A radial basis function neural network (RBFNN) is employed to approximate the uncertain dynamics model of the airship, and an adaptive law is designed to update the NN weight in the processing of approximation. The ultimate boundedness of the tracking errors are proven based on the Lyapunov theory. Finally, simulations are presented to illustrate the effectiveness and high precision of the designed controller.

Composite learning from adaptive backstepping neural network control

In existing neural network (NN) learning control methods, the trajectory of NN inputs must be recurrent to satisfy a stringent condition termed persistent excitation (PE) so that NN parameter convergence is obtainable. This paper focuses on command-filtered backstepping adaptive control for a class of strict-feedback nonlinear systems with functional uncertainties, where an NN composite learning technique is proposed to guarantee convergence of NN weights to their ideal values without the PE condition. In the NN composite learning, spatially localized NN approximation is employed to handle functional uncertainties, online historical data together with instantaneous data are exploited to generate prediction errors, and both tracking errors and prediction errors are employed to update NN weights. The influence of NN approximation errors on the control performance is also clearly shown. The distinctive feature of the proposed NN composite learning is that NN parameter convergence is guaranteed without the requirement of the trajectory of NN inputs being recurrent. Illustrative results have verified effectiveness and superiority of the proposed method compared with existing NN learning control methods.

Adaptive neural network backstepping control for a class of uncertain fractional-order chaotic systems with unknown backlash-like hysteresis

In this paper, we consider the control problem of a class of uncertain fractional-order chaotic systems preceded by unknown backlash-like hysteresis nonlinearities based on backstepping control algorithm. We model the hysteresis by using a differential equation. Based on the fractional Lyapunov stability criterion and the backstepping algorithm procedures, an adaptive neural network controller is driven. No knowledge of the upper bound of the disturbance and system uncertainty is required in our controller, and the asymptotical convergence of the tracking error can be guaranteed. Finally, we give two simulation examples to confirm our theoretical results.

Simplified adaptive neural control of strict-feedback nonlinear systems

This paper presents a simplified adaptive backstepping neural network control (ABNNC) strategy for a general class of uncertain strict-feedback nonlinear systems. In the backstepping design, all unknown functions at intermediate steps are passed down such that only a single neural network is needed to approximate a lumped uncertainty at the last step. The closed-loop system achieves practical asymptotic stability in the sense that all involved signals are bounded and the tracking error converges to a small neighborhood of zero. The contribution of this study is that the complexity growing problem of the traditional ABNNC design is substantially eliminated for a general class of uncertain strict-feedback nonlinear systems, where the constraints of control parameters that guarantee closed-loop stability is clearly demonstrated. An illustrative example has verified effectiveness of our approach.

Semi-globally/globally stable adaptive NN backstepping control for uncertain MIMO systems with tracking accuracy known a priori

This paper focuses on the problem of direct adaptive neural network (NN) tracking control for a class of uncertain nonlinear multi-input/multi-output (MIMO) systems by employing backstepping technique. Compared with the existing results, the outstanding features of the two proposed control schemes are presented as follows. Firstly, a semi-globally stable adaptive neural control scheme is developed to guarantee that the ultimate tracking errors satisfy the accuracy given a priori, which cannot be carried out by using all existing adaptive NN control schemes. Secondly, we propose a novel adaptive neural control approach such that the closed-loop system is globally stable, and in the meantime the ultimate tracking errors also achieve the tracking accuracy known a priori, which is different from all existing adaptive NN backstepping control methods where the closed-loop systems can just be ensured to be semi-globally stable and the ultimate tracking accuracy cannot be determined a priori by the designers before the controllers are implemented. Thirdly, the main technical novelty is to construct three new nth-order continuously differentiable switching functions such that multiswitching-based adaptive neural backstepping controllers are designed successfully. Fourthly, in contrast to the classic adaptive NN control schemes, this paper adopts Barbalat׳s lemma to analyze the convergence of tracking errors rather than Lyapunov stability theory. Consequently, the accuracy of ultimate tracking errors can be determined and adjusted accurately a priori according to the real-world requirements, and all signals in the closed-loop systems are also ensured to be uniformly ultimately bounded. Finally, a simulation example is provided to illustrate the effectiveness and merits of the two proposed adaptive NN control schemes.

Adaptive Neural Network Control for a Simple Pendulum Using Backstepping with Uncertainties

An adaptive neural network backstepping control for a class of uncertain nonlinear systems is presented in this paper. Three main issues will be treated: (1) unknown nonlinearities; (2) unknown system parameters; (3) external or internal disturbances. The proposed technique is applied to a simple pendulum. This latter is an unstable system which is perfectly described by a nonlinear model obtained by applying physics laws. A solution has to be found to stabilize pendulum in a desired position. A specific type of artificial neural networks (ANN) called Multilayer Perceptron (MLP) is used and simulation results clearly demonstrate the power of this extension.

Decentralized Output-Feedback Neural Control for Systems With Unknown Interconnections

An adaptive backstepping neural-network control approach is extended to a class of large-scale nonlinear output-feedback systems with completely unknown and mismatched interconnections. The novel contribution is to remove the common assumptions on interconnections such as matching condition, bounded by upper bounding functions. Differentiation of the interconnected signals in backstepping design is avoided by replacing the interconnected signals in neural inputs with the reference signals. Furthermore, two kinds of unknown modeling errors are handled by the adaptive technique. All the closed-loop signals are guaranteed to be semiglobally uniformly ultimately bounded, and the tracking errors are proved to converge to a small residual set around the origin. The simulation results illustrate the effectiveness of the control approach proposed in this correspondence.