International Journal of Adaptive Control and Signal Processing最新文献

A partial model-free, data-driven adaptive optimal output feedback (OPFB) control scheme with low computational cost continuous-time is proposed in this paper. The design objective is to obtain the optimal control law by using measurable input and output data, without some knowledge of system model information. Firstly, the system states are decoupled into measurable and unmeasurable parts, and a new state-space equation is built to estimate the unmeasurable states by using a reduced-order observer. Based on this, a parametrization method is utilized to reconstruct the system states. Subsequently, by using the reconstructed states, the adaptive dynamic programming (ADP) Bellman equations based on policy-iteration (PI) and value-iteration (VI) are presented to solve the control problems with initially stable and unstable conditions, respectively. Then, the convergence of the system is proved. Compared with the early proposed OPFB algorithms, only the unknown internal state needs to be reconstructed. Therefore, the computation cost and design complexity are reduced for the proposed scheme. The effectiveness of the proposed scheme is verified through two numerical simulations. In addition, a practical inverted pendulum experiment is carried out to demonstrate the performance of the proposed scheme.

This article addresses the fuzzy finite-time command filtering tracking control problem for switched nonlinear systems with input nonlinearities via using backstepping technique. An equivalent transformation method is proposed for the purpose of handling the impediment problem caused by the nonlinearity of control input. Finite-time command filtering control technology can solve the “explosion of complexity” issue, which is caused by the multiple derivation of virtual controllers appeared in the classic backstepping control. A filtering compensation scheme is developed to reduce the filtering error. By combining the fuzzy logic system's approximation ability and the finite-time theory, a new fast convergence adaptive control scheme is proposed so that the output of the system can converge to a small area near the desired signal and the boundedness of all signals of the controlled system can be assured in finite time. Finally, a simulation example successfully verifies the feasibility of the developed control scheme.

This article handles the probability-guaranteed state estimation problem for a class of nonlinear memristive neural networks (MNNs) by using an event-triggered mechanism. Both time-varying delays and incomplete measurements are considered in the MNNs dynamics. To mitigate the impact of limited communication bandwidth, a communication protocol is proposed that incorporates an encoding–decoding technique in addition to an event-triggered scheme. The aim is to devise a state estimator that can estimate the states of MNNs, ensuring that the state estimation error falls within the required ellipsoidal area with a desired chance. We obtain sufficient conditions for the feasibility of the addressed problem, where the requested gains can be found iteratively by solving certain convex optimization problems. On the basis of the proposed framework, some issues are further presented to determine locally optimal estimator parameters according to different specifications. Finally, we utilize an illustrative numerical example to show the validity of our provided theoretical results.

The control problem of the hypersonic vehicles is studied in this article. A new control approach is presented. This approach consists of a data-driven dynamic model established by multiple neural networks, an online identification method for system parameters, and a basic backstepping controller. The implementation of this approach requires a dynamic model and system parameters including the moment of inertia and aerodynamic parameters of the hypersonic vehicles. The parameter identification problem is regarded as a dynamic optimization process. The loss function is designed by the Lagrange criterion, and its constraints are determined by the physical and the numerical values. In the case of model mutation, the system parameters identified online are used as the nominal values of the output of the neural network in the data-driven model to adjust the controller through its gradient descent. Simulation comparisons are given to show the effectiveness of the proposed data-driven approach.

In this paper, we consider the optimal control of unknown stochastic dynamical system for both the finite-horizon and infinite-horizon cases. The objective of this paper is to find an optimal controller to minimize the expected value of a function which depends on the random disturbance. Throughout this paper, it is assumed that the mean vector and covariance matrix of the disturbance distribution is unknown. An uncertainty set in the space of mean vector and the covariance matrix is introduced. For the finite-horizon case, we derive a closed-form expression of the unique optimal policy and the opponents policy that generates the worst-case distribution. For the infinite-horizon case, we simplify the Riccati equation obtained in the finite-hozion setting to an algebraic Riccati equation, which can guarantee the existence of the solution of the Riccati equation. It is shown that the resulting optimal policies obtained in these two cases can stabilize the expected value of the system state under the worst-case distribution. Furthermore, the unknown system matrices can also be explicitly computed using the adaptive dynamic programming technique, which can help compute the optimal control policy by solving the algebraic Riccati equation. Finally, a simulation example is presented to demonstrate the effectiveness of our theoretical results.

This research article presents an artificial neural network (ANN)-based indirect adaptive control method for nonlinear dynamical systems. In this article, a modified Elman recurrent neural network (MERNN) is proposed as an identifier and controller for controlling nonlinear systems. The architecture of the proposed controller is a modified form of the existing Elman recurrent neural network. The parameter training of ANN-based controllers is obtained by using the most popular optimization algorithm which is known as the back-propagation algorithm. A comparative study includes Elman, Diagonal, Jordan, feed-forward neural network (FFNN), and radial basis function network (RBFN)-based controllers to compare with the proposed MERNN controller. To determine the controller's robustness, parameter variations, and disturbance signals have been considered. The performance analysis of the proposed controller is illustrated by two simulation examples. The simulation results reveal that MERNN can not only identify the unknown dynamics of the plant but also adaptively control it compared to the others.

A novel integral sliding mode control (ISMC) strategy combined with an adaptive super twisting observer (ASTO) for an uncertain robotic manipulator tracking control system is presented in this article. The comprehensive uncertainties including both parameter perturbations and external disturbances are considered during the controller design. Firstly, a new nominal control law with prescribed time convergent property based on time varying scaling function is presented for the system without uncertainties. Then this nominal control law constitutes the prescribed time convergent sliding surface for ISMC. As the reaching phase is eliminated in ISMC, leading to the prescribed time stability of the whole control system without uncertainties. Secondly, take the system uncertainties (both the matched and unmatched uncertainties) into consideration, two ASTOs are designed for handling them. So, the lumped uncertainties of the robotic manipulator control system can be well estimated and compensated in finite time with the help of backstepping method. Besides, the finite time convergent adaptive switching gains of the ASTO make the system stable without knowing the bounds of the uncertainties exactly and suppress the chattering phenomenon of control input. Finally, the proposed control algorithm is validated by simulation and experiment on a robotic manipulator. Also, from a quantitative analysis, we testify the proposed control scheme outperforms the compared one in all of the discussed cases of simulation part.

This article proposes a fixed-time adaptive neural command filtered controller for a category of high-order systems based on adding a power integrator technique. Different from existing research, the presented controller has the following distinguishing advantages: (i) a fixed-time control framework is extended to the tracking control problem of high-order systems. (ii) The error compensation mechanism eliminates filter errors that arise from dynamic controllers. (iii) Growth assumptions about unknown functions are relaxed with the help of adaptive neural networks. (iv) More general systems: the developed controller can apply to high-order systems subject to uncertain dynamics, unknown gain functions and asymmetric constraints. Stability analysis shows that all states are semi-globally uniformly ultimately bounded, and the convergence rate of tracking error is independent of initial conditions. Finally, simulation results validate the advantages and efficacy of the developed control scheme.

This paper addresses the problem of adaptive tracking control for multi-input and multi-output (MIMO) non-strict feedback systems with unmodeled dynamics and full state time-varying constraints. To tackle the interference of unmodeled dynamics, the dynamic signal generated by the auxiliary system is used. Hyperbolic tangent function is used as a nonlinear mapping tool to transform the constrained system into an unconstrained one. Hysteresis quantizer is introduced to mitigate the chattering phenomenon and quantization error in the quantization signal. The derivative of virtual signal can be approximated more efficiently by command filter. Furthermore, an error compensation mechanism is established to mitigate the error introduced by the command filter. Unknown nonlinear functions are approximated by radial basis function neural networks (RBFNNs). Stability analysis of the proposed controller is performed through the Lyapunov stability theory and the output tracking error can be constrained within a specified range. Finally, simulation results are presented to demonstrate the effectiveness of the proposed method.

In this study, the problem of preview tracking control (PTC) was considered with regard to the linear parameter periodic systems (LPPSs) subject to previewed signals. First, the difference operator approach was extended to derive an augmented error system (AES). As a result, the PTC problem was transformed into a stabilization problem via state/output feedback. Second, sufficient conditions guaranteeing the closed-loop stability of the augmented error systems were derived, and the design of a periodic controller with preview actions was proposed through the approach of linear matrix inequalities (LMIs). Finally, a numerical simulation was performed to illustrate the effectiveness of the proposed periodic controller.