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Flexible joint robots (FJR) systems are used in many aspects of actual production due to its high compliance, low energy consumption, human-computer interaction safety and other characteristics. A fuzzy observer-based command filtered adaptive control method is applied to make FJR systems with time-varying output constraints (TVOC) and model uncertainties operate safely in a complex environment in this brief. Chiefly, a fuzzy observer is developed to estimate the link’s angle velocity and motor angle velocity of the FJR. Next, by combining time-varying barrier Lyapunov function (TVBLF) with fuzzy logic systems, the uncertainties of the FJR model are approximated without violating the TVOC. Besides, the command filtered method with error compensation signal resolves the issue of “explosion of complexity” and removes the impacts of filtering errors. The stability of the FJR system is verified by Lyapunov stability theory. Simulation shows that the devised approach can insure the TVOC, the validity of the observer and position tracking accuracy of the system.

This article deals with the tracking control problem for a class of multi-input and multioutput (MIMO) nonlinear systems with uncertain dynamics under the premise of feedback path transmitted by dynamic event-trigger mechanism. The neural network adaptive plant model is designed to generate predictive system states for controllers. Command filters are introduced to fix the jumping problem of virtual controllers while avoiding the issue of “explosion of complexity” caused by the recursive differentiate behavior in conventional event-triggered backstepping controllers design. Moreover, dynamic event-trigger conditions are constructed to decide the feedback path aperiodically transmit plant states instants. Simulation results indicate that this proposal can reduce the communication times considerably without degrading system performance.

This article presents an event-triggered adaptive acrlong NN command-filtered control for a class of multi-input and multi-output (MIMO) nonlinear systems with unknown rate-dependent hysteresis in the actuator and the constraints on full states. The acrlong ETM is used to reduce the communication frequency between controller and actuator. The command filter technique is first employed to solve the dilemma between the nondifferentiable control signal at triggering instants and rate-dependent hysteresis input premise while avoiding the “explosion of complexity” problem. During the backstepping design, the barrier Lyapunov functions are utilized to guarantee that system states will stay in certain regions and the unknown nonlinear items are approximated by adaptive neural networks. The compensating signals are constructed to eliminate filtering errors. The estimates of unknown hysteresis parameters are updated by adaptive laws. The stability analysis is given and the effectiveness of the proposed method is verified by simulation.

The peculiar structure, restricted payload ability, and inevitable landing position deviation of the unmanned aerial vehicles (UAVs) have become the bottleneck, restricting their wireless power transfer (WPT) applications. To overcome these problems, a dynamic diffusion magnetic field, based on the two-phase transmitting circular coils arranged at intervals, is established by the design of the magnetic structure and excitation current phase. At this time, the receiver can be simplified as a rectangular coil and small magnetic core, which only picks up the horizontal magnetic flux to achieve a compact design. Then, the transmitter self-coupling is analyzed and a decoupling transformer is built for the circulation active power feedback. Finally, a UAV WPT prototype is constructed, and the experimental result indicates that it can transfer 286.7 W with a dc–dc efficiency of 80.3%, and maximum fluctuation of output dc current and dc–dc efficiency are only 0.48 A and 2.1% under the UAV dislocation.

This brief is concerned with trajectory tracking control for $n$ -link flexible-joint robots (FJR) with time-varying full-state constraints and uncertain dynamics. The barrier Lyapunov functions are constructed to avoid the violation of time-varying constraints. The fuzzy logic systems are used to deal with unknown nonlinearities in FJR systems. The “explosion of complexity” problem in the traditional backstepping method is overcomed by introducing command filtered control with error compensation, and an adaptive fuzzy command filtered control scheme based on time-varying barrier Lyapunov function is designed. The stability of the closed-loop system is analyzed using Lyapunov theory. Simulation results show that the proposed method can guarantee the full-state constraints and position tracking accuracy of the system.

This brief concerns the fixed-time backstepping trajectory tracking control problem for uncertain AUVs subject to unknown input saturation. By making use of the command filter technique, the adaptive control method and neural networks, a low-complexity nonlinear controller that contains only three dynamic update laws is proposed. And the new fixed-time stabilizing function is proposed to avoid the singularity problem. System performance analysis shows that the fixed-time stability is guaranteed for the AUV closed-loop system without violating the input saturation. The simulation result is given to demonstrate the effectiveness of our developed strategy.

The classification and subtyping of ischemic stroke play a significant role in therapeutic decision-making, which leads to higher requirements on the infarct lesion location. This study established a novel deep learning system (DLS) integrating four different models, blood supply, border zone, brain structure atlas, and cortex, to provide a multidimensional delineation of ischemic stroke lesions. Based on the atlas segmentation logic of each model, the output channels of the convolutional neural networks were rearranged, and the image preprocessing parameters were optimized to achieve faster convergence speed. The proposed multidimensional DLS is applied to the magnetic resonance diffusion weighted imaging (MRI-DWI) image examinations to accurately locate the infarction lesions in four specific types of territories. Two experienced neuroradiologists evaluate the performance of the system. About 90% of the results are acceptable in clinical evaluation tests, proving the established system’s feasibility in comprehensive ischemic stroke classification and subtyping and its further potential in diagnosing other brain disorders.

The article deals with synthesis of observers for a linearized water wave system (LWWS) confined in a bounded rectangular fluid domain. To estimate the state of LWWS, including velocity potential and wave elevation, infinite-dimensional state observers are designed by using two different observation measurements. The first observer is designed by measuring the distributed free-surface wave elevation. The convergence rate of this observer is proved to be exponential and fast. The second observer is designed by using only the surface measurement of wave elevation at a fixed point. We show that the second observer converges strongly and more slowly than the first one. The performances of the two designed observers are validated by our numerical simulations.

In this brief, we study the boundary stabilization problem of a rotating body-beam system (RBBS). The system consists of a flexible Euler-Bernoulli beam connected at its upper end to a payload and at its lower boundary to a rigid disk. The control action of the system is composed of an external force acting on the payload and a torque exerted on the disk. Furthermore, the unknown disturbances affecting the motion of both the payload and the disk are considered. We design an integral barrier Lyapunov function (IBLF)-based boundary controller for such an RBBS to stabilize the movement of the flexible beam while regulating the system’s angular velocity to track a predefined desired value exponentially. By applying Lyapunov’s second method, we prove that our designed controller achieves exponential stabilization of the infinite-dimensional flexible beam’s vibration, exponential convergence of the angular velocity towards the desired value, and satisfies a specific constraint condition on the boundary displacement output.

This brief presents a modified event-triggered command filter backstepping tracking control scheme for a class of uncertain nonlinear systems with unknown input saturation based on the adaptive neural network (NN) technique. First, the virtual control functions are reconstructed to address the uncertainties in subsystems by using command filters. A piecewise continuous function is employed to deal with the unknown input saturation problem. Next, an event-triggered tracking controller is developed by utilizing the adaptive NN technique. Compared with standard NN control schemes based on multiple-function-approximators, our controller only requires a single NN. The closed-loop system stability is analyzed based on the Lyapunov stability theorem, and it is shown that the Zeno behavior is also avoided under the designed event-triggering mechanism. Simulation studies are performed to validate the effectiveness of our controller.

The control design method for a class of non-strict feedback nonlinear systems is studied in this brief considering uncertain nonlinearities and unknown non-symmetrical input dead-zone. Combining with the finite-time command filtered backstepping (FCFB) technique, a novel finite-time adaptive control approach is proposed in which a neural network-based methodology is adopted to cope with the uncertain nonlinearities in the non-strict feedback form. The input dead-zone model is transformed into a simple linear system with unknown gain and bounded disturbance which is estimated by an adaptive factor. Using the finite-time Lyapunov theory, the system convergence is proved. And the effectiveness of the proposed control scheme is verified through comparative numerical simulations.