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1.
An adaptive autopilot to control a skid-to-turn missile during its boost phase is designed using the state-dependent Riccati equation (SDRE) method and neural networks (NN). To address the rapid changes in parameters during the boost phase, the translational and rotational motions of the missile are modeled with time-varying velocity and inertial parameters. The autopilot with a two-loop structure is designed to perform integrated roll-pitch-yaw control of the missile with cross-coupled dynamics; each loop has a baseline controller and an adaptive controller. The baseline controller is designed using the SDRE method for reference command tracking in a nominal environment, and the adaptive controller is designed based on NN to manage uncertainty during the boost phase. Stability analysis of the closed-loop system is performed, and the performance of the proposed autopilot is demonstrated by numerical simulation.  相似文献   

2.
The high-performance control requires the system to be stable, fast and accurate simultaneously. However, various systems (e.g., motors, industrial robots) generally face technical challenges such as nonlinearities, uncertainties, external disturbances and physical constraints, which make it difficult to reach the hardware potential of the systems to track the desired trajectories when satisfying the high-performance control requirements. Therefore, take a two-order nonlinear system for example, an optimization-based adaptive neural sliding mode control based on a two-loop control structure is proposed in this paper, where the outer and inner loops are designed separately to achieve different control requirements. Namely, the outer loop is designed as a model predictive control (MPC)-based optimization problem, which can optimize the desired trajectories to meet the state and input constraints, and maximize the converging speed of transient response as fast as possible, and the inner loop is designed with a recurrent neural network (RNN)-based adaptive neural sliding mode controller, which can guarantee the tracking of the replanned desired trajectories from outer loop as accurate as possible. The stability of the system is guaranteed by Lyapunov theorem, the optimal tracking performance is achieved under nonlinearities, uncertainties, external disturbances and physical constraints, and comparative simulation with a motor system is carried out to verify the effectiveness and superiority of the proposed approach.  相似文献   

3.
A spacecraft formation flying controller is designed using a sliding mode control scheme with the adaptive gain and neural networks. Six-degree-of-freedom spacecraft nonlinear dynamic model is considered, and a leader–follower approach is adopted for efficient spacecraft formation flying. Uncertainties and external disturbances have effects on controlling the relative position and attitude of the spacecrafts in the formation. The main benefit of the sliding mode control is the robust stability of the closed-loop system. To improve the performance of the sliding mode control, an adaptive controller based on neural networks is used to compensate for the effects of the modeling error, external disturbance, and nonlinearities. The stability analysis of the closed-loop system is performed using the Lyapunov stability theorem. A spacecraft model with 12 thrusts as actuators is considered for controlling the relative position and attitude of the follower spacecraft. Numerical simulation results are presented to show the effectiveness of the proposed controller.  相似文献   

4.
The terminal iterative learning control is designed for nonlinear systems based on neural networks. A terminal output tracking error model is obtained by using a system input and output algebraic function as well as the differential mean value theorem. The radial basis function neural network is utilized to construct the input for the system. The weights are updated by optimizing an objective function and an auxiliary error is introduced to compensate the approximation error from the neural network. Both time-invariant input case and time-varying input case are discussed in the note. Strict convergence analysis of proposed algorithm is proved by the Lyapunov like method. Simulations based on train station control problem and batch reactor are provided to demonstrate the effectiveness of the proposed algorithms.  相似文献   

5.
In this paper, an adaptive TSK-type CMAC neural control (ATCNC) system via sliding-mode approach is proposed for the chaotic symmetric gyro. The proposed ATCNC system is composed of a neural controller and a supervisory compensator. The neural controller uses a TSK-type CMAC neural network (TCNN) to approximate an ideal controller and the supervisory compensator is designed to guarantee system stable in the Lyapunov stability theorem. The developed TCNN provides more powerful representation than the traditional CMAC neural network. Moreover, all the control parameters of the proposed ATCNC system are evolved in the Lyapunov sense to ensure the system stability with a proportional–integral (PI) type adaptation tuning mechanism. Some simulations are presented to confirm the validity of the proposed ATCNC scheme without the occurrence of chattering phenomena. Further, the proposed PI type adaptation laws can achieve faster convergence of the tracking error than that using integral type adaptation laws in previous published papers.  相似文献   

6.
This paper is devoted to adaptive neural network control issue for a class of nonstrict-feedback uncertain systems with input delay and asymmetric time-varying state constraints. State-related external disturbances are involved into the system, and the upper bounds of disturbances are assumed as functions of state variables instead of constants. Additionally, during the approximations of unknown functions by neural networks, the online computation burdens are declined sharply, since the norms of neural network weight vectors are only estimated. In the process of dealing with input delay, an auxiliary function is applied such that the conditions for time delay are more general than the ones in existing literature. A novel adaptive neural network controller is designed by constructing the asymmetric barrier Lyapunov function, which guarantees that the output of system has a good tracking performance and the state variables never violate the asymmetric time-varying constraints. Finally, numerical simulations are presented to verify the proposed adaptive control scheme.  相似文献   

7.
In this paper, a modified adaptive neural network for the compensation of deadzone is described, and simulated on a hydraulic positioning system, in which the dynamic model is separated into a series of connection of a nonlinear (deadzone) subsystem and a linear plant. The proposed approach uses two neural networks. One is the radial basis function (RBF) neural network, which is used for identifying parameters of deadzone. Based on the penalty function used in optimization theory, a multi-objective cost function with constraint is adopted to provide the best deadzone approximation. The result is used to train the other neural network for the inverse compensation of deadzone. The RBF neural network also generates the parameters of the linear plant for the design of an adaptive controller. A convergence analysis for the network training process is also presented.  相似文献   

8.
《Journal of The Franklin Institute》2022,359(18):10558-10577
In this article, a secure exponential synchronization problem is studied for multiplex Cohen-Grossberg neural networks under stochastic deception attacks. In order to resist the malicious attack from attackers modifying the data in transmission module under a certain probability, an attack resistant controller, which has the ability to automatically adjust its own parameters according to external attacks, is designed for each Cohen-Grossberg neural subnet. An exponential adaptive quantitative controlling algorithm is proposed to synchronize Cohen-Grossberg neural network state, and a sufficient criterion is established to realize the synchronization error tends to zero under malicious attacks. Moreover, synchronization mode we study is the synchronization among Cohen-Grossberg neural subnets in multiplex networks. An example is presented to testify the validity of proposed theoretical framework.  相似文献   

9.
This paper aims to develop a robust optimal control method for longitudinal dynamics of missile systems with full-state constraints suffering from mismatched disturbances by using adaptive dynamic programming (ADP) technique. First, the constrained states are mapped by smooth functions, thus, the considered systems become nonlinear systems without state constraints subject to unknown approximation error. In order to estimate the unknown disturbances, a nonlinear disturbance observer (NDO) is designed. Based on the output of disturbance observer, an integral sliding mode controller (ISMC) is derived to counteract the effects of disturbances and unknown approximation error, thus ensuring the stability of nonlinear systems. Subsequently, the ADP technique is utilized to learn an adaptive optimal controller for the nominal systems, in which a critic network is constructed with a novel weight update law. By utilizing the Lyapunov's method, the stability of the closed-loop system and the convergence of the estimation weight for critic network are guaranteed. Finally, the feasibility and effectiveness of the proposed controller are demonstrated by using longitudinal dynamics of a missile.  相似文献   

10.
This paper investigates the fixed-time neural network adaptive (FNNA) tracking control of a quadrotor unmanned aerial vehicle (QUAV) to achieve flight safety and high efficiency. By combining radial basis function neural network (RBFNN) with fixed time adaptive sliding mode algorithm, a novel radial basis function neural network adaptive law is proposed. In addition, an extended state/disturbance observer (ESDO) is proposed to solve the problem of unmeasurable state and external interference, which can obtain reliable state feedback and interference input. Unlike most other ESO applications, this paper does not set the uncertainty model and external disturbances as total disturbances. Instead, the external disturbances are observed by extending the states and the observed states are fed back to the controller to cancel the disturbances. In view of the time-varying resistance coefficient and inertia torque in the QUAV model, the neural network is introduced so that the controller does not need to consider these nonlinear uncertainties. Finally, a numerical example is given to verify the effectiveness of the coupled non-simplified QUAV model.  相似文献   

11.
This paper is concerned with the distributed formation control problem of multi-quadrotor unmanned aerial vehicle (UAV) in the framework of event triggering. First, for the position loop, an adaptive dynamic programming based on event triggering is developed to design the formation controller. The critic-only network structure is adopted to approximate the optimal cost function. The merit of the proposed algorithm lies in that the event triggering mechanism is incorporated the neural network (NN) to reduce calculations and actions of the multi-UAV system, which is significant for the practical application. What’s more, a new weight update law based on the gradient descent technology is proposed for the critic NN, which can ensure that the solution converges to the optimal value online. Then, a finite-time attitude tracking controller is adopted for the attitude loop to achieve rapid attitude tracking. Finally, the efficiency of the proposed method is illustrated by numerical simulations and experimental verification.  相似文献   

12.
In this paper, the prescribed performance trajectory tracking problem of quadrotor aircraft with six degrees of freedom is addressed. Firstly, for the sake of facilitating the construction of controller, the aircraft is decomposed into position loop and attitude loop through time scale decomposition method. A fixed-time sliding mode controller is proposed to guarantee the convergence time of the aircraft system regardless of initial states. After that, to enhance security of control system, the hyperbolic tangent performance function is designed as performance index function to maintain the error within a prescribed range. Then, the event-triggered strategy is adopted to attitude subsystem which can significantly save communication resources, and the stability of control system is analyzed by Lyapunov method. In addition, the Zeno phenomenon is avoided which can be proved by ensuring the two consecutive trigger events have a positive lower limit. Finally, the validity of the constructed controller is confirmed by simulation results.  相似文献   

13.
This paper deals with the problem of adaptive output feedback neural network controller design for a SISO non-affine nonlinear system. Since in practice all system states are not available in output measurement, an observer is designed to estimate these states. In comparison with the existing approaches, the current method does not require any information about the sign of control gain. In order to handle the unknown sign of the control direction, the Nussbaum-type function is utilized. In order to approximate the unknown nonlinear function, neural network is firstly exploited, and then to compensate the approximation error and external disturbance a robustifying term is employed. The proposed controller is designed based on strict-positive-real (SPR) Lyapunov stability theory to ensure the asymptotic stability of the closed-loop system. Finally, two simulation studies are presented to demonstrate the effectiveness of the developed scheme.  相似文献   

14.
This paper presents a fixed-time composite neural learning control scheme for nonlinear strict-feedback systems subject to unknown dynamics and state constraints. To address the problem of state constraints, a new unified universal barrier Lyapunov function is proposed to convert the constrained system into an unconstrained one. Taking the unconstrained system, a modified fixed-time convergence state predictor is explored, enabling the prediction error for compensating the neural adaptive law to be obtained and improving the learning ability of online neural networks (NNs). Without employing fractional power terms or a complicated switching strategy to build the control law, a new method of constructing a smooth fixed-time dynamic surface control scheme is proposed. This overcomes the potential singularity problem and the explosion of complexity often encountered in fixed-time back-stepping designs. The representative features of our design are threefold. First, it is free of the fractional power terms, yet offers fixed-time convergence. Second, it addresses the state constraint problem without requiring a feasibility check. Third, it constructs a new state-predictor and enhances the approximation accuracy of NNs. The stability of the proposed control scheme is analyzed using the Lyapunov technique. Simulation results are presented to illustrate the effectiveness of the proposed controller.  相似文献   

15.
This work aims to design a neural network-based fractional-order backstepping controller (NNFOBC) to control a multiple-input multiple-output (MIMO) quadrotor unmanned aerial vehicle (QUAV) system under uncertainties and disturbances and unknown dynamics. First, we investigated the dynamic of QUAV composed of six inter-connected nonlinear subsystems. Then, to increase the convergence speed and control precision of the classical backstepping controller (BC), we design a fractional-order BC (FOBC) that provides further degrees of freedom in the control parameters for every subsystem. Besides, designing control is a challenge as the FOBC requires knowledge of accurate mathematical model and the physical parameters of QUAV system. To address this problem, we propose an adaptive approximator that is a radial basis function neural network (RBFNN) included in FOBC to fix the unknown dynamics problem which results in the new approach NNFOBC. Furthermore, a robust control term is introduced to increase the tracking performance of a reference signal as parametric uncertainties and disturbances occur. We have used Lyapunov's theorem to derive adaptive laws of control parameters. Finally, the outcoming results confirm that the performance of the proposed NNFOBC controller outperforms both the classical BC , FOBC and a neural network-based classical BC controller (NNBC) and under parametric uncertainties and disturbances.  相似文献   

16.
The main contribution of this paper is to develop an adaptive output-feedback control approach for a class of uncertain nonlinear systems with unknown time-varying delays in the pure-feedback form. Both the non-affine nonlinear functions and the unknown time-varying delayed functions related to all state variables are considered. These conditions make the controller design difficult and challenging because the output-feedback controller should be designed using only the output information. In order to overcome these conditions, we design an observer-based adaptive dynamic surface controller where the time-delay effects are compensated by using appropriate Lyapunov–Krasovskii functionals and the function approximation technique using neural networks. A first-order filter is added to the control input to avoid the algebraic loop problem caused by the non-affine structure. It is proved that all the signals in the closed-loop system are semi-globally uniformly bounded and the tracking error converges to an adjustable neighborhood of the origin.  相似文献   

17.
A novel finite-time complex-valued zeroing neural network (FTCVZNN) for solving time-varying Sylvester equation is proposed and investigated. Asymptotic stability analysis of this network is examined with any general activation function satisfying a condition or with an odd monotonically increasing activation function. So far, finite-time model studies have been investigated for the upper bound time of convergence using a linear activation function with design formula for the derivative of the error or with variations of sign-bi-power activation functions to zeroing neural networks. A function adaptive coefficient for sign-bi-power activation function (FA-CSBP) is introduced and examined for faster convergence. An upper bound on convergence time is derived with the two components in the function adaptive coefficients of sign-bi-power activation function. Numerical simulation results demonstrate that the FTCVZNN with function adaptive coefficient for sign-bi-power activation function is faster than applying a sign-bi-power activation function to the zeroing neural network (ZNN) and the other finite-time complex-valued models for the selected example problems.  相似文献   

18.
In this paper, a fixed-time dual closed-loop attitude control method is investigated for a quadrotor unmanned aerial vehicle. Firstly, a fixed-time adaptive fast super-twisting disturbance observer is presented for estimating the unknown external disturbance. A modified adaptive law is employed based on an equivalent control method to obtain proper observer gains. Secondly, a fixed-time controller is designed by using a universal barrier Lyapunov function to satisfy asymmetric tracking error constraints. Then, a tracking differentiator is utilised to arrange the transition process. Finally, the implementation of the developed method in a quadrotor unmanned aerial vehicle is performed. Through stability analysis and simulation results, the effectiveness and superiority of the proposed fixed-time control method are validated.  相似文献   

19.
This paper investigates the problem of decentralized adaptive backstepping control for a class of large-scale stochastic nonlinear time-delay systems with asymmetric saturation actuators and output constraints. Firstly, the Gaussian error function is employed to represent a continuous differentiable asymmetric saturation nonlinearity, and barrier Lyapunov functions are designed to ensure that the output parameters are restricted. Secondly, the appropriate Lyapunov–Krasovskii functional and the property of hyperbolic tangent functions are used to deal with the unknown unmatched time-delay interactions, and the neural networks are employed to approximate the unknown nonlinearities. At last, based on Lyapunov stability theory, a decentralized adaptive neural control method is proposed, and the designed controller decreases the number of learning parameters. It is shown that the designed controller can ensure that all the closed-loop signals are 4-Moment (or 2 Moment) semi-globally uniformly ultimately bounded (SGUUB) and the tracking error converges to a small neighborhood of the origin. Two examples are provided to show the effectiveness of the proposed method.  相似文献   

20.
This paper investigates the adaptive attitude tracking problem for the rigid satellite involving output constraint, input saturation, input time delay, and external disturbance by integrating barrier Lyapunov function (BLF) and prescribed performance control (PPC). In contrast to the existing approaches, the input delay is addressed by Pade approximation, and the actual control input concerning saturation is obtained by utilizing an auxiliary variable that simplifies the controller design with respect to mean value methods or Nussbaum function-based strategies. Due to the implementation of the BLF control, together with an interval notion-based PPC strategy, not only the system output but also the transformed error produced by PPC are constrained. An adaptive fuzzy controller is then constructed and the predesigned constraints for system output and the transformed error will not be violated. In addition, a smooth switch term is imported into the controller such that the finite time convergence for all error variables is guaranteed for a certain case while the singularity problem is avoided. Finally, simulations are provided to show the effectiveness and potential of the proposed new design techniques.  相似文献   

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