International Journal of Adaptive Control and Signal Processing最新文献

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.

With the rapid development of the global renewable energy source field, the importance of dynamic index processing technology in distributed energy systems has become more and more obvious. To better improve the real-time dynamic interaction means of microgrids in the energy Internet and optimize the relevant methods for microgrid energy consumption detection, this article proposes to introduce the distributed Hadoop platform into the electrical thermal coupling multivariate data in the form of cluster configuration, and then use the Spark framework to detect and capture real-time data, to complete the tracking and analysis of energy consumption data. At the same time, the Internet of Things and the cloud intelligent monitoring system are combined to further clean and explore the data, to achieve the in-depth detection of the energy consumption problem of the microgrid under the premise of reducing the initial investment, and achieve the purpose of reducing the operating cost. In this case, the outliers are detected according to the photovoltaic indicators of photovoltaic power stations, the filtration and purification functions of photovoltaic indicators are used by the nuclear density curve, and the sustainable solar energy is optimized by combining multiple indicators such as wind direction and temperature. Based on reducing energy consumption, the overfitting phenomenon of the controller is controlled, and an optimized controller-led cloud platform is established. By establishing the objective function model, the robustness of the controller is guaranteed and the detection expectation is satisfied by the experiment of energy consumption data. In addition, when the cloud platform is created, this study uses a genetic algorithm to optimize the controller index and then builds a cloud console detection mechanism that collaborates with the Internet. Through the research, it is found that outliers may lead to the redundancy of energy consumption indicators in the non-processing state. This study adopts the optimization of energy consumption parameters and the help of a distributed data framework to deal with and effectively solve this problem. In terms of interpolation simulation verification combined with experimental data, this paper proposes to use the Internet of Things, wearable devices, sensors, and other means to monitor the cost of energy consumption, to realize the distributed dynamic storage of massive real-time data in the process of parallel processing, as well as the evaluation and detection of real-time data replacement.

Rock drilling jumbo is an important large construction machine used for tunneling construction, and its automation has an urgent demand in engineering. However, the electro-hydraulic system of the rock drilling jumbo has strong parameters uncertainties and some dynamics that are hard to model accurately, which causes certain challenges for designing model-based high-performance control algorithms. To solve these challenges, a parameter adaptive based neural network sliding mode control algorithm is proposed to enhance control performance of the electro-hydraulic system. The parameter adaptive law is developed to estimate unknown parameters of the system, the neural network is applied for compensating unmodeled dynamics, and then the final control law is designed by sliding mode control method, and the stability demonstration of the closed-loop system is given. In the simulations, the effectiveness of the designed parameter adaptive law is verified. Extensive comparison experiments are performed on a real rock drilling jumbo driven by proportional valves, the experimental results demonstrate that the developed control algorithm obviously improves the control precision of hydraulic cylinder of the rock drilling jumbo compared with the traditional sliding mode and PID control algorithm, thus the designed control algorithm can be expanded and applied for general hydraulic servo control mechanism.

In this paper, we investigate the global adaptive stabilization problem via partial-state feedback for a class of uncertain chained-form nonholonomic systems with the dynamic uncertainty and nonlinear parameterization. The notions of Sontag's input-to-state stability (ISS) and ISS-Lyapunov function, together with the changing supply rates technique are used to overcome the dynamic uncertainty. The nonlinear parameterization is well treated with the aid of the parameter separation technique. The discontinuous input-to-state scaling technique is employed in this procedure to derive the global stabilization controllers. Additionally, we develop a switching adaptive control strategy in order to get around the smooth stabilization burden associated with nonholonomic systems. The simulation results illustrate the efficacy of the presented algorithm.

In this paper, an event-triggered adaptive tracking control strategy is proposed for strict-feedback stochastic nonlinear systems with predetermined finite-time performance. Firstly, a finite-time performance function (FTPF) is introduced to describe the predetermined tracking performance. With the help of the error transformation technique, the original constrained tracking error is transformed into an equivalent unconstrained variable. Then, the unknown nonlinear functions are approximated by using the multi-dimensional Taylor networks (MTNs) in the backstepping design process. Meanwhile, an event-triggered mechanism with a relative threshold is introduced to reduce the communication burden between actuators and controllers. Furthermore, the proposed control strategy can ensure that all signals of the closed-loop system are bounded in probability and the tracking error is within a predefined range in a finite time. In the end, the effectiveness of the proposed control strategy is verified by two simulation examples.