International Journal of Adaptive Control and Signal Processing最新文献

In this paper, a novel fixed time adaptive fuzzy control scheme for pure-feedback stochastic nonlinear systems with full state constraints is proposed based on dynamic surface control (DSC) technique and barrier Lyapunov functions (BLFs) method. Firstly, the mean value theorem is utilized to transform the pure-feedback structure of the considered systems into strict-feedback ones, which make it possible for the utilization of backstepping method to design the controller. Then, DSC technique is used to reduce the computational complexity problem caused by backstepping method. Fuzzy logic systems are exploited to approximate the unknown nonlinear functions. Moreover, combine BLFs method with fixed time stability theorem, the fixed time adaptive fuzzy controller is constructed, which guarantees that all states do not violate the prescribed constraints and all signals are semi-globally uniform ultimately bounded. Finally, a simulation example is given to verify the effectiveness of the studied control.

In this article, the finite-time control problem of adaptive neural network (NN) reinforcement learning (RL) is investigated for the continuous time stochastic non-linear systems with full state constraints and dead zone output. Firstly, the adaptive estimation and smooth approximation technique are introduced to solve the difficulty arising from the dead zone non-linearity. Moreover, to overcome the problem of calculating the explosion caused by the repeated differentiation of the virtual control signals, a finite-time command filter is constructed. Combining the backstepping technique and the identifier-actor-critic RL strategy, an adaptive neural finite-time RL control scheme is proposed for the considered system by constructing the tangent-type time-varying barrier Lyapunov functions (BLFs), which optimizes the tracking performance while ensuring all states do not violate the constraints. Under the proposed control strategy, it is guaranteed that all signals are bounded in probability, and the output of the system can track the reference signal within a finite-time. Finally, the simulation results verify the effectiveness of the proposed scheme.

Formation maneuverability is particularly important for multi-robots (MRs), especially when the robots are operating cooperatively in complex and dynamic environments. Although various methods have been developed for affine formation, it is still a difficult problem to design an affine formation controller for MRs with unknown dynamics. In this paper, a distributed actor-critic learning approach (DACL) in a look-ahead rollout manner is proposed for the affine formation of MRs under local communication, which improves the online learning efficiency. In the proposed approach, a distributed data-driven online optimization mechanism is designed via the sparse kernel technique to solve the near-optimal affine formation control issue of MRs with unknown dynamics as well as improve control performance. The unknown dynamics of MRs are learned offline based on precollected input-output datasets, and the sparse kernel-based approach is employed to increase the feature representation capability of the samples. Then, the proposed distributed online actor-critic algorithm for each robot in the formation includes two neural networks, which are utilized to approximate the costate functions and the near-optimal policies. Moreover, the convergence analysis of the proposed approach has been conducted. Finally, numerical simulation and KKSwarm-based experiment studies are performed to verify the effectiveness of the proposed approach.

Utilizing the cross-scale imperfect data, the reinforcement learning (RL) composite $��{�H�}_{�\infty �}�$ control of nonlinear two-time-scale (TTS) systems is proposed in the presence of unknown slow dynamics. First, with the feat of singular perturbation theory (SPT), the original $��{�H�}_{�\infty �}�$ control problem is decomposed and rearranged into standard fast and slow subproblems that have no cross terms between state, control and disturbance in the performance indices. Then, since the states of decomposed fast and slow subsystems cannot be measured perfectly, the state reconstruction mechanism is proposed based on the input-state data of the original system, and cross-scale information interaction is incorporated to correct the bias induced by the time-scale decomposition. Cross-scale composite RL algorithm is proposed with the $��{�H�}_{�\infty �}�$ slow and fast controllers designed in separate time scales. Next, the stability and $��{�H�}_{�\infty �}�$ performance of the TTS systems under the composite controller is analyzed considering the data inaccuracy of state reconstruction. Finally, the effectiveness of the proposed method is validated in the control application to the permanent magnet synchronous motor (PMSM) system.

The distributed filtering (DF) problem is investigated for state-saturated systems (SSSs) with switching nonlinearities under the adaptive event-triggered mechanism (AETM) and Rayleigh fading channels over sensor networks, where the data is transmitted between nodes through the Rayleigh fading channel. In addition, the AETM is introduced to save communication resources and improve data transmission efficiency. First, a distributed filter is designed incorporating the information of state saturation, switching nonlinearity, Rayleigh fading channel and AETM. Second, the upper bound (UB) on the filtering error covariance (FEC) is derived by the mathematical induction method, and the filter gain is obtained by minimizing the trace of the UB. Subsequently, the boundedness of UB on the FEC is shown through mathematical analysis. Finally, the effectiveness of the filtering scheme designed in this article is demonstrated through a numerical simulation example and a practical example, in which the influence of different Rayleigh parameters to the filtering performance and the superiority of using the AETM are discussed.

This work studies the synchronization issue for two-time-scale Markov jump neural networks subject to redundant channels. In such systems, mode information may not be directly available (e.g., packet loss), and traditional synchronous control methods cannot meet this challenge. The hidden Markov model can deal with the situation that the systems state cannot be accessed directly, and estimate the current state of the system through the “observation” mode, so as to improve the controller design and advance the stability and robustness of the systems. Therefore, the controller is designed based on a hidden Markov model for the above scenarios. Meanwhile, the redundant channels are built to reduce the influence of packet loss. Moreover, the two-time-scale phenomenon of the plant is considered by using the singular perturbation parameter. Then, the Lyapunov function construction is associated with the singular perturbation parameter and some sufficient conditions to guarantee the stability of the plant are obtained. Finally, the designed control law is available which is demonstrated by two illustrative examples.

Based on a three-dimensional flexible, rotating manipulator, this paper is dedicated to the issue of vibration suppression and angle planning subject to actuator failures and input signal constraints. Considering that a flexible link belongs to a distributed parameter system, the motion model is obtained by Hamilton's principle and described by partial differential equations (PDEs). A novel fault-tolerant control law is proposed to eliminate the elastic deflection and vibration of the flexible link and to follow the desired angle of the rotating base and the manipulator with the appearance of actuator failures. The adaptive operator based on projection mapping is adopted to estimate the loss of the actuator. In addition, the hyperbolic tangent function is employed to constrain the input signal so that it is within an adjustable interval. The Lyapunov's method and LaSalle invariance principle are applied to prove the stability and convergence of the closed-loop system. The simulation results are provided to demonstrate the effectiveness of the proposed method.

The objective of this study is to develop a new control method for nonlinear time-delay systems with input saturation and to achieve the prescribed performance control target by exploiting the excellent approximation performance of multidimensional Taylor networks (MTNs). The transformation method is used to transform the complex saturation model into a simple model with bounded error, simplifying the controller design process. The MTN is used to handle the nonlinear functions of the system, and an original adaptive control algorithm is constructed with the help of the backstepping method. The proposed control approach is feasible and can implement the tracking error signal that falls into the desired prescribed scope with all the signals in the control system being bounded. Finally, the efficiency of the proposed approach is demonstrated by presenting simulation results.

Aiming at the noise interference problem in wing fatigue tests, this paper improves the traditional LMS algorithm using the variational Versoria function and the variational Gaussian function. Additionally, this paper proposes a variable step-size LMS (VSS-LMS) filtering algorithm based on the composite function (CVSS-LMS). The composite function combines the variational Versoria function and the variational Gaussian function to describe the nonlinear relationship between the iteration step size and the error. To adapt to environments with different signal-to-noise ratios, the algorithm replaces the fixed parameters with a combination of current and previous errors, thus enabling adaptive adjustment of the parameters. Moreover, a step-size dynamic constraint rule is proposed to further improve the stability of the algorithm. The algorithm is normalized using a combination of the cumulative sum of error squares, the mean square error (MSE), and the power of the input signal, which reduces the sensitivity to the input signal amplitude. The above parts finally constitute the adaptive CVSS-LMS (ACVSS-LMS) filtering algorithm. The convergence of the ACVSS-LMS algorithm is verified through theoretical derivation. The ACVSS-LMS algorithm is experimentally analyzed by using the simulation data generated by MATLAB and the actual data collected from the wing fatigue test, and the results show that the ACVSS-LMS algorithm proposed in this paper has a faster convergence speed and lower steady-state error compared to other algorithms.

This paper studies the finite-time control problem of a class of switched non-linear systems under time-varying state constraints and proposes an adaptive finite-time controller based on a multi-dimensional Taylor network (MTN). Firstly, the time-varying tangent barrier Lyapunov functions (TBLFs) are constructed to ensure that all system states are constrained within a certain range. Secondly, MTNs are used to estimate the unknown non-linear functions during the controller design process. The proposed control scheme ensures that the tracking error of the system can converge to a small domain of the origin in a finite-time. At the same time, all the signals of the closed-loop system are semi-global practical finite-time stable (SGPFS) and all states satisfy the defined time-varying state constraints. Finally, the effectiveness of the control strategy is verified through numerical simulation examples and practical simulation examples.