Adaptive Neural Backstepping Control Research Articles

Robust adaptive backstepping neural networks fault tolerant control for mobile manipulator UAV with multiple uncertainties

The present study outlines the development of an Adaptive Backstepping Radial Basis Function Neural Networks Fault Tolerant Control (ABRBFNNFTC) methodology. The aforementioned methodology is employed to address the challenge of achieving trajectory following in the context of a Mobile Manipulator Unmanned Aerial Vehicle (MMUAV) when subjected to the effects of actuator faults and parametric uncertainties. The utilization of an adaptive radial basis function neural networks (RBFNNs) controller is employed for the purpose of approximating an unidentified nonlinear backstepping controller that relies on the precise model of the MMUAV. The Lyapunov direct method is utilized to establish the stability analysis of the entire system. The closed-loop system guarantees the Uniformly Ultimately Bounded (UUB) stability of all signals. The control methodology put forth ensures the achievement of a prescribed trajectory, and mitigates the impact of uncertainties and actuator faults. The efficiency of the proposed ABRBFNNFTC scheme is demonstrated through the presentation of extensive simulation studies.

Observer-based distributed convex optimization of bipartite containment control for higher order nonlinear uncertain multi-agent systems

This paper studies the distributed convex optimization of bipartite containment control problem for a class of higher order nonlinear multi-agent systems with uncertain states. For the optimization problem, the penalty function is constructed by summing the local objective function of each agent and combining the penalty term formed by the adjacency matrix. For the unknown nonlinear function and unpredictable states in the system, this paper construct radial basis function Neural-networks and state observer for approaching, respectively. In order to avoid “explosion of complexity,” under the framework of Lyapunov function theory, we propose the dynamic surface control (DSC) technology and design the distributed adaptive backstepping neural network controller to ensure all the signals remain semi-global uniformly ultimately bounded in the closed-loop system and all agents can converge to the convex hull containing each boundary trajectory as well as its opposite trajectory different in sign. Simulation results confirm the feasibility of the proposed control method.

Adaptive neural backstepping control of nonlinear fractional-order systems with input quantization

This article addresses the tracking control problem of uncertain fractional-order nonlinear systems in the presence of input quantization and external disturbance. An adaptive backstepping scheme is proposed by combining with radial basis function (RBF) neural networks (NNs), fractional-order disturbance observer (FODO), and backstepping method. The RBF NNs are used to approximate the unknown nonlinearities of fractional-order systems. The FODO is designed to compensate for disturbance and uncertain parameters. The hysteresis quantizer is used to avoid chattering that possibly appears in actual application. The stability of the proposed controller is proved by fractional-order Lyapunov method. In addition, all the signals in the closed-loop system are bounded. The effectiveness of the proposed method is confirmed by the simulation results.

Prescribed Performance-tangent Barrier Lyapunov Function for Adaptive Neural Backstepping Control of Variable Stiffness Actuator with Input and Output Constraints

Prescribed Performance-tangent Barrier Lyapunov Function for Adaptive Neural Backstepping Control of Variable Stiffness Actuator with Input and Output Constraints

Adaptive Neural Backstepping Control for Harmonic Drive System Based on Modified LuGre Friction Model

Adaptive Neural Backstepping Control for Harmonic Drive System Based on Modified LuGre Friction Model

IBLF-Based Adaptive Neural Control of State-Constrained Uncertain Stochastic Nonlinear Systems.

In this article, the adaptive neural backstepping control approaches are designed for uncertain stochastic nonlinear systems with full-state constraints. According to the symmetry of constraint boundary, two cases of controlled systems subject to symmetric and asymmetric constraints are studied, respectively. Then, corresponding adaptive neural controllers are developed by virtue of backstepping design procedure and the learning ability of radial basis function neural network (RBFNN). It is worth mentioning that the integral Barrier Lyapunov function (IBLF), as an effective tool, is first applied to solve the above constraint problems. As a result, the state constraints are avoided from being transformed into error constraints via the proposed schemes. In addition, based on Lyapunov stability analysis, it is demonstrated that the errors can converge to a small neighborhood of zero, the full states do not exceed the given constraint bounds, and all signals in the closed-loop systems are semiglobally uniformly ultimately bounded (SGUUB) in probability. Finally, the numerical simulation results are provided to exhibit the effectiveness of the proposed control approaches.

Adaptive Neural Backstepping Control Approach for Tracker Design of Wheelchair Upper-Limb Exoskeleton Robot System

In this study, the desired tracking control of the upper-limb exoskeleton robot system under model uncertainty and external disturbance is investigated. For this reason, an adaptive neural network using a backstepping control strategy is designed. The difference between the actual values of the upper-limb exoskeleton robot system and the desired values is considered as the tracking error. Afterward, the auxiliary variable based on the tracking error is defined and the virtual control input is obtained. Then, by using the backstepping control procedure and Lyapunov stability concept, the convergence of the position tracking error is proved. Moreover, for the compensation of the model uncertainty and the external disturbance that exist in the upper-limb exoskeleton robot system, an adaptive neural-network procedure is adopted. Furthermore, for the estimation of the unknown coefficient related to the parameters of the neural network, the adaptive law is designed. Finally, the simulation results are prepared for demonstration of the effectiveness of the suggested method on the upper-limb exoskeleton robot system.

Composite adaptive neural tracking control of uncertain strict‐feedback systems

AbstractComposite adaptive neural control of uncertain strict‐feedback systems is synthesized. It mainly consists of an adaptive neural backstepping controller, an immersion and invariance (I&I) update algorithm, and a set of state filters for conquering the in‐feasibility of the states' derivatives required in the update algorithm. For compensating the indefinite coupling terms in the update algorithm, a novel nonlinear ‐modification method is promoted. To further tackle the case with unknown input gain functions, the dynamic surface control (DSC) and the adding‐an‐integrator technique are incorporated for preventing the so‐called explosion of complexity and the algebraic‐loop problems in the virtual controller and the composite update algorithm, respectively. The proposed design ensures the semi‐globally uniformly ultimately bounded (SGUUB) stability of the closed‐loop system and, in particular, the convergence of prediction errors to the vicinity of zero without persistent excitation (PE), which in turn improves the tracking performance.

Real‐time implementation of adaptive neural backstepping controller for battery‐less solar‐powered PMDC motor

A low-cost battery-less solar-powered PMDC motor using an adaptive backstepping Chebyshev neural network controller to track the desired speed for any change in irradiance and load torque is proposed in this paper. The neural network is used for approximating the variable load torque because of its approximation property. The computational burden of the control law is reduced because of the orthogonal property of Chebyshev polynomials. The asymptotically stable system is obtained by tuning the weights of neurons in accordance with the Lyapunov stability analysis. From the Lyapunov control function of backstepping control design procedure, the control law is obtained by an innovative way of elucidating the cubic equation, in place of resolving the derivative of the control law in the control function. This approach eliminates the constraints caused by the non-strict feedback system for the backstepping control approach and also this reduces ripples in the duty cycle which makes its appropriateness in real-time. To ensure its robustness in tracking the desired speed at a faster time and minimum overshoot, simulations are done for an extensive range of variations in irradiance and load torque, and the obtained results are assessed by comparing it with the PID controller and conventional backstepping controller. Because of the use of neural network the robustness of the proposed controller is ensured with enhanced transient and steady state responses. A prototype is developed in the laboratory and the obtained results are assessed by comparing it with the backstepping controller and PID controller.

Analog circuit implementation and adaptive neural backstepping control of a network of four Duffing-type MEMS resonators with mechanical and electrostatic coupling

This paper investigates the analog circuit implementation and adaptive neural backstepping control of a network of four Duffing-type MEMS resonators with mechanical and electrostatic coupling. Firstly, the mathematical model of such network is established by using a series-parallel mode of mechanical and electrostatic coupling between MEMS resonators. Secondly, the dynamic analysis reveals that the coupled network can generate complex nonlinear behaviors which seriously affect the system performance without taking actions. Thirdly, based on the energy flow theory, its equivalent analog electronic circuit is established to further verify inherent dynamical characteristics of a network of four Duffing-type MEMS resonators. Fourthly, to suppress the mentioned harmful nonlinear behaviors above, an adaptive neural backstepping control scheme is proposed here wherein the interval type 2 fuzzy neural network (IT2FNN) is used to estimate unknown nonlinear functions along with cosine barrier function to guarantee states boundedness. Stability analysis proves that all signals of the closed-loop system are bounded and the tracking errors are limited to the pregiven boundary. Finally, the effectiveness of our scheme is testified by abundant numerical simulation results.

Adaptive Back-stepping Neural Control for an Embedded and Tiltable V-tail Morphing Aircraft

Adaptive Back-stepping Neural Control for an Embedded and Tiltable V-tail Morphing Aircraft

Global adaptive neural backstepping control of a flexible hypersonic vehicle with disturbance estimation

PurposeThis paper aims to discuss the precise altitude and velocity tracking control of a hypersonic vehicle, a global adaptive neural backstepping controller was studied based on a disturbance observer (DOB).Design/methodology/approachThe DOB combined with a radial basis function (RBF) neural network (NN) was used to estimate the disturbance terms that are generated by the flexible modes of the hypersonic vehicle system. A global adaptive neural method was introduced to approximate the unknown system dynamics, with robust control terms pulling the system transient states back into the neural approximation domain externally.FindingsThe globally uniformly ultimately bounded for all signals of a closed-loop system can be guaranteed by the proposed control algorithm. Additionally, the command filtered backstepping methods can avoid the explosion of the complexity problem caused by the backstepping design process. In addition, the effectiveness of the proposed controller can be verified by the simulation used in this study.Research limitations/implicationsNormally lateral dynamics issue should be discussed in the process of control system designed, the lateral dynamics are not included in the nonlinear dynamic model of hypersonic vehicle used in this paper, merely the longitudinal flight dynamics are discussed in this paper.Originality/valueThe flexible states in rigid modes are considered as the disturbance of the system, which is estimated by structuring DOB with NN approximations. The compensating tracking error and prediction error are used in the update law of RBF NN weight. The differential explosions complexity derived from the backstepping procedure is dealt with by using command filters.

Adaptive neural backstepping control for flexible-joint robot manipulator with bounded torque inputs

Aiming at tracking control with bounded torque inputs of the flexible-joint robot manipulators, we propose a generalized saturated adaptive controller based on backstepping control, singular perturbation decoupling and neural networks. First, by using the singular perturbation theory, the full-order rigid-flexible dynamics of the robot manipulator is decoupled into a slow subsystem and a fast subsystem. Second, saturated sub-controller by backstepping method is proposed for the slow subsystem, where the projection-type parameter adaptation and a class of saturation functions are applied to make the torque inputs bounded, and a saturated neural network approximator is involved to simplify the control law and to compensate for the uncertain nonlinearity. Third, for fast subsystem, a new filtered tracking error of the elastic torque is used in the fast control law to make the boundary layer subside quickly. In addition, explicit but strict stability analysis is given for the system. Finally, comparisons indicate that the proposed controller results in a more satisfactory tracking performance with keeping the control inputs bounded within the given range all the time and superior anti-disturbance capability.

Adaptive finite-time neural backstepping control for multiple-input–multiple-output uncertain nonlinear systems with full state constraints

This paper investigates the issue of finite-time tracking control for multiple-input–multiple-output nonlinear systems subject to uncertainties and full state constraints. To deal with full state constraints directly, integral barrier Lyapunov functionals (iBLF) are introduced. By using finite-time stability theory, an iBLF-based adaptive finite-time neural control scheme is presented. To solve the problem of “explosion of complexity” in the design of traditional backstepping control, a new finite-time convergent differentiator is presented. Through stability analysis, all closed-loop signals are proved to be semi-globally uniformly ultimately bounded, the finite time convergence can be guaranteed, and the state constraints are never violated. Finally, the attitude tracking simulations for an autonomous airship are conducted to verify the effectiveness of the proposed scheme.

Real-Time Implementation of Adaptive Neuro Backstepping Controller for Maximum Power Point Tracking in Photo Voltaic Systems

The efficiency of the low-cost renewable energy source i.e. solar is very poor due to inadequate extraction of maximum power. By employing the proper maximum power point tracking algorithm, the efficiency can be increased. An innovative adaptive backstepping neural network controller is proposed in this paper to extract the maximum power from the solar panels by tracking the desired photovoltaic array voltage in real-time. The maximum power is extracted indirectly by tuning the PV voltage to the desired PV voltage where the maximum power is attained at the desired PV voltage point. The desired photovoltaic array voltage is obtained from the linear regression method. The change in photovoltaic current caused by varying irradiance and temperature is approximated using the Chebyshev polynomials. The quicker steady-state and transient responses are accomplished and the computational burden of the photovoltaic system control law is reduced because of the orthogonal property of Chebyshev polynomials. The asymptotically stable system is obtained by tuning the weights of the neurons in accordance with the Lyapunov stability analysis. Also, Lyapunov control function of the backstepping control design procedure finds a control law by an innovative cubic equation interpretation, instead of resolving the first derivative of the control law, that diminishes the ripples in the duty cycle to make its appropriateness in real-time. A prototype is developed to validate the robustness of this controller in maximum power extraction at a faster time and the results confirm that adaptive backstepping neural network controller surpasses the performances of conventional backstepping controller and constant voltage PID controller.

Finite-Time Command-Filtered Composite Adaptive Neural Control of Uncertain Nonlinear Systems.

This article presents a new command-filtered composite adaptive neural control scheme for uncertain nonlinear systems. Compared with existing works, this approach focuses on achieving finite-time convergent composite adaptive control for the higher-order nonlinear system with unknown nonlinearities, parameter uncertainties, and external disturbances. First, radial basis function neural networks (NNs) are utilized to approximate the unknown functions of the considered uncertain nonlinear system. By constructing the prediction errors from the serial-parallel nonsmooth estimation models, the prediction errors and the tracking errors are fused to update the weights of the NNs. Afterward, the composite adaptive neural backstepping control scheme is proposed via nonsmooth command filter and adaptive disturbance estimation techniques. The proposed control scheme ensures that high-precision tracking performances and NN approximation performances can be achieved simultaneously. Meanwhile, it can avoid the singularity problem in the finite-time backstepping framework. Moreover, it is proved that all signals in the closed-loop control system can be convergent in finite time. Finally, simulation results are given to illustrate the effectiveness of the proposed control scheme.

Adaptive neural decentralized output-feedback control for nonlinear large-scale systems with input time-varying delay and saturation

For a class of nonstrict-feedback nonlinear large-scale systems with input delay, saturation and unknown virtual control gains, we propose a decentralized output-feedback control scheme in this paper. In the process of control design, a novel auxiliary system is proposed to overcome the difficulty caused by time-varying input delay. The convex combination technique is used to overcome the difficulty caused by unknown virtual control gains, and further construct the state observer. Then a decentralized output-feedback controller is designed by using the adaptive neural backstepping control approach. By Lyapunov stability theory, it is proved that the proposed decentralized output-feedback controller can ensure that tracking errors converge to a small neighborhood of the origin and other closed-loop states are bounded. Finally, the results of simulation example further verify the validity of this decentralized output-feedback control scheme.

Finite-Time Antisaturation Tracking Control for Hypersonic Vehicle with Uncertain Dynamics

This paper concentrates on the problem of finite-time altitude and velocity tracking control for hypersonic flight vehicles that encounter unmodeled dynamics and input saturations. An adaptive neural finite-time backstepping control strategy is constructed by designing modified virtual commands and compensation signals. The minimum learning parameter algorithm based on a radial basis function is employed to approximate the unknown dynamics with low computational burden. Furthermore, an auxiliary system is established to cope with the nonlinearity caused by actuator saturation. It is concluded by a Lyapunov-based analysis that the finite-time stability is guaranteed under the developed architecture. Finally, numeral simulation is provided to demonstrate the effectiveness of the proposed controller.

Adaptive neural consensus tracking control for a class of 2-order multi-agent systems with nonlinear dynamics

This paper investigates the problem of finite-time consensus tracking control for a class of 2-order nonlinear multi-agent systems (MASs). In order to present a consensus control protocol by adaptive neural control approach, a novel fast finite-time stability criterion is first set up, which provides a theoretical basis for applying approximation-based adaptive control approaches to solve the finite-time control issues. Furthermore, an adaptive neural fast finite-time consensus tracking controller is constructed based on the developed finite-time stability criterion. The suggested adaptive neural backstepping control design scheme successfully avoids the problem of singularity of controllers. Under the action of the presented protocol, the output of each follower tracks the reference signal and other signals of the closed-loop system remain bounded in finite time. The efficacy of the proposed control scheme is confirmed by simulation study.

Event-triggered adaptive neural backstepping control for nonstrict-feedback nonlinear time-delay systems

This paper investigates the adaptive tracking control problem for a class of nonstrict-feedback nonlinear time-delay systems under event-triggered mechanism. The approach of neural network(NN) approximation is extended to nonstrict-feedback nonlinear systems. The adaptive NN controller is designed via backstepping technique and event-triggered mechanism. By the above techniques, the global Lipschitz condition of the unknown nonlinear function is released and the assumption of input-to-state stability (ISS) with respect to the measurement error is removed. It is shown that the proposed control scheme can guarantee all signals in closed-loop systems to be semi-global uniformly ultimately bounded (SGUUB), while the tracking error can converge to a small neighborhood of the origin. Finally, two examples are given to clarify the feasibility and effectiveness of the proposed design methodology.