International Journal of Adaptive Control and Signal Processing最新文献

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.

This article investigates an adaptive neural networks (NNs) tracking control design issue for nonlinear multi-input and multi-output (MIMO) systems involving the sensor-to-controller event-triggered mechanism (ETM). In the design, NNs are utilized to approximate the unknown nonlinear functions. A sensor-to-controller ETM is designed to save unnecessary transmission and communication resources. Subsequently, a first-order filter technique is presented to solve the problem that the virtual control function is not differentiable. Furthermore, an event-triggered adaptive NNs control strategy is presented by constructing Lyapunov functions and using adaptive backstepping recursive design. It is demonstrated that the presented scheme can ensure the whole closed-loop signals are uniformly ultimately bounded without exhibiting the Zeno behavior. Finally, a numerical simulation example confirms the effectiveness of the presented adaptive event-triggered control (ETC) approach.

In this article, a novel distributed adaptive parameter estimation (DAPE) algorithm is proposed for an multi-agent system over weakly connected digraph networks, where parameter convergence is ensured under a newly coined relaxed excitation condition, called generalized cooperative initial excitation (gC-IE). This is in contrast to the past literature, where such DAPE algorithms demand cooperative persistent of excitation (C-PE) and generalized cooperative persistent of excitation (gC-PE) for strongly connected digraph, and weakly connected digraph networks, respectively, for parameter convergence. The gC-PE and C-PE conditions are restrictive in the sense that they require the richness/excitation of information over the entire time-span of the signal/data, unlike gC-IE condition where excitation is needed only in the initial time-span. The newly coined gC-IE condition is an extension of cooperative initial excitation (C-IE) condition. While the C-IE condition is applicable to a strongly connected digraph, the newly proposed gC-IE condition extends the concept to weakly connected digraph. The proposed algorithm utilizes a novel set of weighted integrator dynamics, which omits the requirement of computationally involved multiples switching mechanisms in past literature, while still ensuring parameter convergence. The proposed algorithm provides global exponential stability of origin of the parameter estimation error dynamics under gC-IE condition. Furthermore, robustness to unmodeled disturbance is also established in the form of input-to-state stability. Simulation results validate the efficacy of the proposed algorithm in contrast to the gC-PE based algorithm.

In this article, the problem of global adaptive practical tracking for high-order uncertain nonstrict feedback nonlinear systems with unknown control coefficients is studied. To avoid the algebraic loop problem associated with the nonstrict feedback condition and guarantee the controllability of the tracking error, a novel dual dynamic gain scaling method is introduced to compensate nonlinearities and the tracking error simultaneously. Besides, by incorporating the sign functions into the design of adding a power integrator, a general approach for the handing of unknown control coefficients and the direction design of the controller is developed. The presented control scheme can ensure that all system states are globally bounded without constraints on state variables while the reference signal is tracked with expected precision. Three simulation examples, including a practical application, are provided to illustrate the validity of the control scheme.

This article introduces a novel optimal trajectory tracking control scheme designed for uncertain linear discrete-time (DT) systems. In contrast to traditional tracking control methods, our approach removes the requirement for the reference trajectory to align with the generator dynamics of an autonomous dynamical system. Moreover, it does not demand the complete desired trajectory to be known in advance, whether through the generator model or any other means. Instead, our approach can dynamically incorporate segments (finite horizons) of reference trajectories and autonomously learn an optimal control policy to track them in real time. To achieve this, we address the tracking problem by learning a time-varying $��Q�$-function through state feedback. This $��Q�$-function is then utilized to calculate the optimal feedback gain and explicitly time-varying feedforward control input, all without the need for prior knowledge of the system dynamics or having the complete reference trajectory in advance. Additionally, we introduce an adaptive observer to extend the applicability of the tracking control scheme to situations where full state measurements are unavailable. We rigorously establish the closed-loop stability of our optimal adaptive control approach, both with and without the adaptive observer, employing Lyapunov theory. Moreover, we characterize the optimality of the controller with respect to the finite horizon length of the known components of the desired trajectory. To further enhance the controller's adaptability and effectiveness in multitask environments, we employ the Efficient Lifelong Learning Algorithm, which leverages a shared knowledge base within the recursive least squares algorithm for multitask $��Q�$-learning. The efficacy of our approach is substantiated through a comprehensive set of simulation results by using a power system example.