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1.
This paper investigates the state-feedback stabilization problem in the smooth case for a class of high-order nonlinear systems with time delays. By generalizing a novel radial basis function neural network (RBF NN) approximation approach to high-order nonlinear systems, we successfully remove the power order restriction and the growth conditions on system nonlinearities. It should be pointed out that the knowledge of NN nodes and weights does not need to be known a priori and operate on-line, and the adaptive parameter is only one. Furthermore, without imposing any growth assumptions on system nonlinearities, we construct a smooth adaptive state-feedback controller which guarantees the closed-loop system to be semi-globally uniformly ultimately bounded (SGUUB). Finally, we apply the proposed scheme to a single-link robot system and a numerical example to demonstrate the effectiveness of the controller.  相似文献   

2.
An adaptive backstepping control scheme is proposed for task-space trajectory tracking of robot manipulators in the presence of uncertain parameters and external disturbances. In the case of external disturbance-free, the developed controller guarantees that the desired trajectory is globally asymptotically followed. Moreover, taking disturbances into consideration, the controller is synthesized by using adaptive technique to estimate the system uncertainties. It is shown that L2 gain of the closed-loop system is allowed to be chosen arbitrarily small so as to achieve any level of L2 disturbance attenuation. The associated stability proof is constructive and accomplished by the development of a Lyapunov function candidate. Numerical simulation results are included to verify the control performance of the control approach derived.  相似文献   

3.
This paper develops a robust adaptive neural network (NN) tracking control scheme for a class of strict-feedback nonlinear systems with unknown nonlinearities and unknown external disturbances under input saturation. The radial basis function NNs with minimal learning parameter (MLP) are employed to online approximate the uncertain system dynamics. The adaptive laws are designed to online update the upper bound of the norm of ideal NN weight vectors, and the sum of the bounds of NN approximation errors and external disturbances, respectively. An auxiliary dynamic system is constructed to generate the augmented error signals which are used to modify the adaptive laws for preventing the destructive action due to the input saturation. Moreover, the command filtering backstepping control method is utilized to overcome the shortcoming of dynamic surface control method, the tracking-differentiator-based control method, etc. Our proposed scheme is qualified for simultaneously dealing with the input saturation effect, the heavy computational burden and the “explosion of complexity” problems. Theoretical analysis illuminates that our scheme ensures the boundedness of all signals in the closed-loop systems. Simulation results on two examples verify the effectiveness of our developed control scheme.  相似文献   

4.
In this paper, the development and experimental validation of a novel double two-loop nonlinear controller based on adaptive neural networks for a quadrotor are presented. The proposed controller has a two-loop structure: an outer loop for position control and an inner loop for attitude control. Similarly, both position and orientation controllers also have a two-loop design with an adaptive neural network in each inner loop. The output weight matrices of the neural networks are updated online through adaptation laws obtained from a rigorous error convergence analysis. Thus, a training stage is unnecessary prior to the neural network implementation. Additionally, an integral action is included in the controller to cope with constant disturbances. The error convergence analysis guarantees the achievement of the trajectory tracking task and the boundedness of the output weight matrix estimation errors. The proposed scheme is designed such that an accurate knowledge of the quadrotor parameters is not needed. A comparison against the proposed controller and two other well-known schemes is presented. The obtained results showed the functionality of the proposed controller and demonstrated robustness to parametric uncertainty.  相似文献   

5.
The energy regulation of fully actuated torque–driven robot manipulators in joint space is addressed in this paper. The proposed controller is designed via an energy shaping plus damping injection approach. The contribution is the proposal of an energy regulator with partial damping injection capable of inducing oscillations in the undamped joints of robot manipulators, with an user specified desired frequency and amplitude, by adding only damping in the rest of the joints, which may require less control effort than a trajectory tracking controller with full damping injection. Although viscous friction is considered in all joints of robot manipulator, it has been compensated via the proposed energy regulator. Moreover, the controlled periodic motion oscillates around a desired joint position as reference, and this provides a nice feature in the robot, mainly when there is not interest in the undamped joint to follow an specified time-varying sinusoidal function, but generating an oscillatory motion of constant amplitude and frequency. Instrumental in stability analysis is the Lyapunov’s theory and LaSalle’s theorem, which allows concluding that the closed-loop trajectories approach an invariant set that could include a unique equilibrium or periodic orbits. Numerical simulations on a manipulator arm model of two degrees of freedom illustrate the main results.  相似文献   

6.
Implementing human-like learning and control for nonlinear dynamical systems operating in different control situations is an important and challenging issue. This paper presents a pattern-based neural network (NN) control strategy for nonlinear pure-feedback systems via deterministic learning (DL). Firstly, an appropriately designed adaptive neural dynamic surface controller is proposed to achieve the finite time tracking control. By analyzing the recurrent property of NN input signals, a partial persistent excitation (PE) condition for radial basis function (RBF) network is established, the implicit desired control dynamics under different control situations are accurately identified via DL in the case that the dimension of NN input is reduced. And a set of pattern-based experienced actual and virtual controllers is constructed using the learned knowledge. Secondly, to classify different control situations, when the system is operating in different control situations but controlled by current normal experienced controller, the dynamics of each subsystem are accurately identified via DL, n sets of dynamical estimators are constructed using the learned knowledge. Thirdly, in the recognition phase, n sets of residuals are achieved by comparing each set of estimators with the monitored system, sudden change in the control situation is rapidly recognized based on the principle of the earliest occurrence of the minimum residual. Finally, in the control phase, according to the recognition result, the correct experienced actual and virtual controllers will be selected to control the plant, guaranteed stability and superior control performance are achieved without any further re-adaptation online. Simulation studies are given to verify the proposed scheme can not only acquire and memorize knowledge like humans, but also reuse the learned knowledge to achieve rapid recognition and control of current control situation.  相似文献   

7.
In this paper, a novel composite controller is proposed to achieve the prescribed performance of completely tracking errors for a class of uncertain nonlinear systems. The proposed controller contains a feedforward controller and a feedback controller. The feedforward controller is constructed by incorporating the prescribed performance function (PPF) and a state predictor into the neural dynamic surface approach to guarantee the transient and steady-state responses of completely tracking errors within prescribed boundaries. Different from the traditional adaptive laws which are commonly updated by the system tracking error, the state predictor uses the prediction error to update the neural network (NN) weights such that a smooth and fast approximation for the unknown nonlinearity can be obtained without incurring high-frequency oscillations. Since the uncertainties existing in the system may influence the prescribed performance of tracking error and the estimation accuracy of NN, an optimal robust guaranteed cost control (ORGCC) is designed as the feedback controller to make the closed-loop system robustly stable and further guarantee that the system cost function is not more than a specified upper bound. The stabilities of the whole closed-loop control system is certified by the Lyapunov theory. Simulation and experimental results based on a servomechanism are conducted to demonstrate the effectiveness of the proposed method.  相似文献   

8.
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.  相似文献   

9.
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10.
This paper considers the control problem of spacecraft line-of-sight (LOS) relative motion with thrust saturation in the presence of unmodeled dynamics, external disturbance and unknown mass property. By using skew-symmetric property, reference trajectory generator and anti-windup technique, a novel passivity-based adaptive sliding mode control (SMC) scheme is proposed without prior knowledge of uncertainty/disturbance bound. Within the Lyapunov framework, the establishment of a real sliding mode (which induces the practical stability of closed-loop error system) is validated. The main contributions are that a new control gain adaptive algorithm is adopted to attenuate the overestimation of switching gain and a differentiable projection-based parameter adaptive algorithm is proposed to force the mass approximator to remain in a desired domain, then the adaptive control law is modified by the reference trajectory generator and anti-windup technique to compensate for the effect of thrust saturation. Finally, simulations are conducted to show the fine performance of proposed control scheme.  相似文献   

11.
The main challenges of modular robot manipulators (MRMs) with the environmental constraints include the avoidance of catastrophic collision and the precious contacting in the whole interaction process. Consequently, an event-triggered optimal interaction control method of MRMs under the complex multi-task constraints is presented in this paper. Firstly, on the basis of the joint torque feedback (JTF) technique, the dynamic model of constrained MRM subsystem is established. Secondly, the sensorless-based decentralized nonlinear disturbance observer (NDOB) is proposed to detect and identify the sudden external collision for each joint. Then, the performance index function is improved to achieve the interaction control, which contains the fusion state variable function, the influence of external collision, the known model term, and the estimation of model uncertainties through the radial basis function neural network (RBFNN) identifier. Further, based on event-triggered mechanism and adaptive dynamic programming (ADP) algorithm, the approximate event-triggered optimal interaction control strategy is acquired by the critic neural network (NN). Next, the closed-loop MRM system is demonstrated to be uniformly ultimately bounded (UUB) through the Lyapunov stability theorem. Finally, the experiments are achieved effectively for each joint on the platform, such that the feasibility and universality of the proposed interaction control approach are testified by the experimental results.  相似文献   

12.
A novel robust hierarchical multi-loop composite control scheme is proposed for the trajectory tracking control of robotic manipulators subject to constraints and disturbances. The inner loop based on inverse dynamics control is used to reduce the nonlinear tracking error system to a set of decoupled linear subsystems to alleviate the computational effort during the sequel optimization. The feasible regions of the equivalent state and control input of each subsystem can be computed efficiently by choosing an appropriate inertia matrix estimate. The external loop, relying on a set of separate disturbance-observer-based tube model predictive composite controllers, is used to robustly stabilize the decoupled subsystems. In particular, the disturbance observers are designed to compensate for the disturbances actively, while the tube model predictive controllers are used to reject the residual disturbances. The robust tightened constraints are obtained by calculating the outer-bounding-tube-type residual disturbance invariant sets of the closed-loop subsystems. Furthermore, the recursive feasibility and input-to-state stability of the closed-loop system are investigated. The effectiveness of the proposed control scheme is verified by the simulation experiment on a PUMA 560 robotic manipulator.  相似文献   

13.
In this paper, a sensorless speed control for interior permanent magnet synchronous motors (IPMSM) is designed by combining a robust backstepping controller with integral actions and an adaptive interconnected observer. The IPMSM control design generally requires rotor position measurement. Then, to eliminate this sensor, an adaptive interconnected observer is designed to estimate the rotor position and the speed. Moreover, a robust nonlinear control based on the backstepping algorithm is designed where an integral action is introduced in order to improve the robust properties of the controller. The stability of the closed-loop system with the observer–controller scheme is analyzed and sufficient conditions are given to prove the practical stability. Simulation results are shown to illustrate the performance of the proposed scheme under parametric uncertainties and low speed. Furthermore, the proposed integral backstepping control is compared with the classical backstepping controller.  相似文献   

14.
In this paper, a novel fast attitude adaptive fault-tolerant control (FTC) scheme based on adaptive neural network and command filter is presented for the hypersonic reentry vehicles (HRV) with complex uncertainties which contain parameter uncertainties, un-modeled dynamics, actuator faults, and external disturbances. To improve the performance of closed-loop FTC, command filter and neural network are introduced to reconstruct system nonlinearities that are related to complex uncertainties. Compared with the FTC scheme with only neural network, the FTC scheme with command filter and neural network has fewer controller design parameters so that the computational complexity is decreased and the control efficiency is improved, which is of great significance for HRV. Then, the adaptive backstepping fault-tolerant controller based on command filter and neural network is designed, which can solve the complexity explosion problem in the standard backstepping control and the small uncertainty problem in the backstepping control only containing command filter. Moreover, to improve the approximation accuracy of the neural network-based universal approximator, an adaptive update law of neural network weights is designed by using the convex optimization technique. It is proved that the presented FTC scheme can ensure that the closed-loop control system is stable and the tracking errors are convergent. Finally, simulation results are carried out to verify the superiority and effectiveness of the presented FTC scheme.  相似文献   

15.
This paper focuses on the problem of adaptive output feedback control for a class of uncertain nonlinear systems with input delay and disturbances. Radial basis function neural networks (NNs) are employed to approximate the unknown functions and an NN observer is constructed to estimate the unmeasurable system states. Moreover, an auxiliary system is introduced to compensate for the effect of input delay. With the aid of the backstepping technique and Lyapunov stability theorem, an adaptive NN output feedback controller is designed which can guarantee the boundedness of all the signals in the closed-loop systems. Finally, a simulation example is given to illustrate the effectiveness of the proposed method.  相似文献   

16.
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   

17.
In the study, an adaptive event-triggered control strategy is proposed for image-based visual servoing of eye-to-hand robot manipulators, where the camera used does not need to be calibrated, and the dynamics behavior of manipulator is considered in design and analysis. To address the uncertainty in camera parameters and the nonlinearity in robot dynamics, and accommodate the errors between the real-time control signals and the piecewise-constant event-triggered control signals, a new and novel robust adaptive estimation approach is developed, and well fused with visual feedback control design so that the boundedness of all closed-loop signals, and the asymptotic convergence of image error towards zero are established simultaneously. Besides rigorous proof in theory, the obtained results are also confirmed by comparative simulation tests.  相似文献   

18.
In this paper, an adaptive quantized control method with guaranteed transient performance is presented for a class of uncertain nonlinear systems. By introducing the Nussbaum function technique, the difficulty caused by quantization is handled and a novel adaptive control scheme is designed. In comparing with the existing adaptive control scheme, the key advantages of the proposed control scheme are that the controller needs no information about the parameters of the quantizer and the stability of the closed-loop system and the transient performance are independent of the coarseness of the quantizer. Based on Lyapunov stability theory and Barbalat’s Lemma, it is proven that all the signals in the resulting closed-loop system are bounded and the tracking error converges to zero asymptotically with the prescribed performance bound at all times. Simulation results are presented to verify the effectiveness of the proposed control method.  相似文献   

19.
This article is dedicated to the issue of asynchronous adaptive observer-based sliding mode control for a class of nonlinear stochastic switching systems with Markovian switching. The system under examination is subject to matched uncertainties, external disturbances, and quantized outputs and is described by a TS fuzzy stochastic switching model with a Markovian process. A quantized sliding mode observer is designed, as are two modes-dependent fuzzy switching surfaces for the error and estimated systems, based on a mode dependent logarithmic quantizer. The Lyapunov approach is employed to establish sufficient conditions for sliding mode dynamics to be robust mean square stable with extended dissipativity. Moreover, with the decoupling matrix procedure, a new linear matrix inequality-based criterion is investigated to synthesize the controller and observer gains. The adaptive control technique is used to synthesize asynchronous sliding mode controllers for error and SMO systems, respectively, so as to ensure that the pre-designed sliding surfaces can be reached, and the closed-loop system can perform robustly despite uncertainties and signal quantization error.Finally, simulation results on a one-link arm robot system are provided to show potential applications as well as validate the effectiveness of the proposed scheme.  相似文献   

20.
The adaptive asymptotic tracking control problem for a class of stochastic non-strict-feedback switched nonlinear systems is addressed in this paper. For the unknown continuous functions, some neural networks are used to approximate them online, and the dynamic surface control (DSC) technique is employed to develop the novel adaptive neural control scheme with the nonlinear filter. The proposed controller ensures that all the closed-loop signals remain semiglobally bounded in probability, at the same time, the output signal asymptotically tracks the desired signal in probability. Finally, a simulation is made to examine the effectiveness of the proposed control scheme.  相似文献   

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