Taking into consideration the issue of the quadrotor unmanned aircraft robots (UARs) actuated by motors with hysteresis input, this research presents an adaptive dynamic implicit inverse control technique based on neural networks to achieve the desired trajectories. The following summarizes the primary technologies: 1) the hysteresis effect in UARs has been considered and eliminated by the proposed implicit inverse algorithms, which means a searching method for acquiring the real control signals is designed resulting in selecting to avoid constructing the hysteresis direct inverse model; 2) precise tracking is accomplished by designing an adaptive dynamic surface control (DSC) technology with enhanced state observer under the constraint that only the position data is available. In the meanwhile, the $L_{\infty }$ performance can be obtained by selecting the suitable parameters; and 3) the underactuated Drone platform has been constructed as well as the control results have implemented to confirm that the successful application of the proposed implicit inverse control algorithms.

With the aim of addressing the problem of the trajectory tracking control of quadrotor unmanned aircraft robots (UARs), in this study, we developed a neural network and event-triggering mechanism-based adaptive control scheme for a quadrotor UAR control system. The main technologies included this scheme are as follows. (1) Under the condition that only the quadrotor’s position information can be obtained, a modified high-gain state observer-based adaptive dynamic surface control (DSC) method was applied and the tracking control of quadrotor UARs was acquired. (2) An event-triggered mechanism for UARs was designed, in which the energy consumption was greatly reduced and the communication efficiency between the system and the control terminal was improved. (3) By selecting appropriate parameters, appropriate initial conditions for the adaptive laws, and establishing a high-gain state observer, a tracking performance of $L_{\infty }$ could be achieved. Finally, simulation results of the hardware-in-loop strategy are presented. The control method we propose here outperformed the traditional backstepping sliding mode control (BSMC) scheme.

For improving the precision of the load forecasting in different time spans, a new load forecasting model which combines the improved complete ensemble empirical mode decomposition algorithm based on adaptive noise (ICEEMDAN) algorithm, the least squares support vector machine (LS-SVM) and the long short-term memory network (LSTM) is proposed. In this paper, the training set of the forecasting model is acquired from the department of dispatch center from a large city in the north of China. And the advantages of the forecasting algorithms and decomposition algorithm are applied reasonably, where the ICEEMDAN algorithm is used to decompose the original historical load data. By using the ICEEMDAN algorithm, the fluctuation trend of different periods can be obtained. And the structure of the neural network combining LS-SVM and LSTM, which is used to obtain the load forecasting result. Based on LS-SVM and LSTM algorithm the non-stationary and stationary signals have been processed, respectively. The simulation results show that whether testing by the dataset acquired from a city in the north of China or the Elia dataset, the proposed method outperforms the load forecasting model in the short-, medium- and long-term which is based on PSO-SVR, LS-SVM, LSTM and ICCEMDAN-LSTM, respectively.

This article investigates the decentralized control scheme for fractional-order (FO) interconnected systems actuated by multiaxis smart material-based actuators (SMBAs), which subject to multiasymmetric hysteresis, coupling issue and time-delay. First, a novel indirect inverse control strategy is proposed to handle the hysteresis by resorting to the asymmetric shifted Prandtl–Ishlinskii (ASPI) model. Then, the radial basis function neural networks (RBFNNs) combine with the finite cover lemma are utilized to deal with the time-delay problem. Moreover, with the decentralized control technique, a decentralized indirect inverse neural control (DIINC) strategy is proposed, while the unknown interconnected coupling term is handled by RBFNNs. Finally, experiments are conducted on a three-axis giant magnetostrictive actuator (three-axis GMA) motion control platform, and its dynamic behavior is accurately captured by establishing a phenomenological model. The effectiveness of the proposed controller is validated through the motion control experiments.

This study contrives a discrete-time adaptive decentralized control algorithm with input quantization for interconnected multi-machine power systems with SVC. First, a dynamic surface scheme is applied to the excitation controller design, in which first-order digital low-pass filters are used to predict the next virtual control law, which overcomes the model conversion problem in backstepping. Therefore, the controller design and structure are simplified. Further, an improved hysteresis quantizer is utilized for amplitude quantization of control input signals; along with the discretization of time, this achieves digital decentralized control. Finally, semi-global uniformly ultimately boundedness (SGUUB) of the whole control system is demonstrated based on the Lyapunov stability theory, and the effectiveness of the proposed control algorithm is verified on the ModelingTech real-time simulation experimental platform for power electronics.

A practical finite-time neural pseudo inverse control (PFTNPIC) strategy is proposed to address the finite-time tracking control of fractional-order systems subject to asymmetric hysteresis and time-delay. To compensate for the asymmetric hysteresis effect, a novel pseudo inverse algorithm is developed based on the asymmetric shifted Prandtl–Ishlinskii model, thereby avoiding the need to construct an explicit hysteresis inverse model. Furthermore, a framework that integrates a radial basis function neural network with the finite cover lemma is employed to eliminate the time-delay issue. With the proposed PFTNPIC approach, all signals in the closed-loop system are shown to be semiglobally uniformly ultimately bounded and the tracking error is guaranteed to converge to a small neighborhood of origin within finite time. Finally, the effectiveness of the proposed strategy is validated through experiments conducted on a displacement platform driven by a giant magnetostrictive actuator.