Adaptive Integral Sliding Mode Control Using Fully Connected Recurrent Neural Network for Position and Attitude Control of Quadrotor

Compound Learning-Based Model Predictive Control Approach for Ducted-Fan Aerial Vehicles

Designing an efficient learning-based model predictive control (MPC) framework for ducted-fan unmanned aerial vehicles (DFUAVs) is a difficult task due to several factors involving uncertain dynamics, coupled motion, and unorthodox aerodynamic configuration. Existing control techniques are either developed from largely known physics-informed models or are made for specific goals. In this regard, this article proposes a compound learning-based MPC approach for DFUAVs to construct a suitable framework that exhibits efficient dynamics learning capability with adequate disturbance rejection characteristics. At the start, a nominal model from a largely unknown DFUAV model is achieved offline through sparse identification. Afterward, a reinforcement learning (RL) mechanism is deployed online to learn a policy to facilitate the initial guesses for the control input sequence. Thereafter, an MPC-driven optimization problem is developed, where the obtained nominal (learned) system is updated by the real system, yielding improved computational efficiency for the overall control framework. Under appropriate assumptions, stability and recursive feasibility are compactly ensured. Finally, a comparative study is conducted to illustrate the efficacy of the designed scheme.

Self-Constructing Chebyshev Fuzzy Neural Complementary Sliding Mode Control and its Application

In this article, a complementary sliding mode (CSM) controller using a self-constructing Chebyshev fuzzy recurrent neural network (SCCFRNN) is proposed for harmonic suppression control of an active power filter (APF). The SCCFRNN whose structure can be automatically learned through the designed structure self-learning algorithm is introduced to approximate the unknown nonlinear term in the APF dynamic model, so as to improve modeling accuracy and reduce the burden of CSM control (CSMC). The SCCFRNN combines the advantages of a fuzzy neural network (FNN), recurrent neural network (RNN), and Chebyshev neural network (CNN), and all parameters can be adjusted according to the designed adaptive laws. Eventually, through detailed simulation, hardware experiments, and fair comparison, the feasibility and superiority of the proposed control algorithm were verified.

Modelling, Analysis and Validation of Hydraulic Self-Adaptive Bearings for Elevated Floating Bridges

Conventional floating bridge systems used during emergency repairs, such as during wartime or after natural disasters, typically rely on passive rubber bearings or semi-active control systems. These methods often limit traffic speed, stability, and safety under dynamic conditions, including varying vehicle loads and fluctuating water levels. To address these challenges, this study proposes a novel Hydraulic Self-Adaptive Bearing System (HABS). The system integrates real-time position closed-loop control and a flexible support compensation method to enhance stability and adaptability to environmental changes. A modified three-variable controller is introduced to optimise load response, while a multi-state observer control strategy effectively reduces vibrations and improves traffic smoothness. A 1:15 scale prototype was constructed, and a co-simulation model combining MATLAB/Simulink and MSC Adams was developed to simulate various operational conditions. Results from both experiments and simulations demonstrate the HABS's ability to adapt to varying loads and environmental disturbances, achieving a 72% reduction in displacement and a 54% reduction in acceleration. These improvements enhance traffic speed, stability, and safety, making the system a promising solution for emergency and floating bridges, providing superior performance under challenging and dynamic conditions.

Adaptive Robust Control of Uncertain Euler-Lagrange Systems Using Gaussian Processes

This article proposes a novel adaptive robust control approach based on Gaussian processes (GPs) for the high-precision tracking problem of uncertain Euler-Lagrange (EL) systems with time-varying external disturbances. Given a prior dynamic model, the GP regression (GPR) technique is employed to obtain a nonparametric data-based uncertainty model, including its probabilistic confidence intervals. Based on the adaptive sliding mode control (ASMC) framework, the posterior means of GPs are utilized for dynamic compensation, whereas the posterior variances are applied to adjust the feedback gains. This proposed control strategy is robust against significant system uncertainty with low feedback gains. A novel adaptive law for updating hyperparameters based on tracking error feedback is presented, thereby improving the performance of both tracking control and GP modeling simultaneously. Compared to existing likelihood-based optimization methods, this hyperparameter adaptive law enables data-efficient and fast uncertainty learning for control applications. The proposed control strategy guarantees the semiglobal asymptotic convergence to zero tracking error with a specified probability. Simulations using an underwater robot model demonstrate that the utilization of GPs and hyperparameter adaptive law significantly improves the performance of tracking control and uncertainty learning.

Predefined-Time Hierarchical Coordinated Neural Control for Hypersonic Reentry Vehicle

This paper investigates the predefined-time hierarchical coordinated adaptive control on the hypersonic reentry vehicle in presence of low actuator efficiency. In order to compensate for the deficiency of rudder deflection in advantage of channel coupling, the hierarchical design is proposed for coordination of the elevator deflection and aileron deflection. Under the control scheme, the equivalent control law and switching control law are constructed with the predefined-time technology. For the dynamics uncertainty approximation, the composite learning using the tracking error and the prediction error is constructed by designing the serial-parallel estimation model. The closed-loop system stability is analyzed via the Lyapunov approach and the tracking errors are guaranteed to be uniformly ultimately bounded in a predefined time. The tracking performance and the learning accuracy of the proposed algorithm are verified via simulation tests.

Unknown System Dynamics Estimator-Based Anti-Disturbance Attitude Funnel Control for Quadrotors with Experimental Verifications

In the article, an unknown system dynamics estimator-based anti-disturbance attitude funnel control technique is considered for quadrotors to ensure tracking performance when experiencing parametric uncertainties and external perturbations. To reinforce the anti-disturbance ability, an unknown system dynamics estimator was established by constructing the filtering operation-based invariant manifold, resulting in a more concise design framework, lower computational consumption and an asymptotic error convergence. Additionally, a funnel control policy was employed to regulate angle-tracking errors within a minor overshoot, a faster convergence time and a lower steady-state error by devising the funnel variables, where an exponential decaying function was used to construct the funnel boundary. The great improvements beyond the available quadrotor control policies are related to satisfied disturbance mitigation and performance guarantees as a priority despite uncertainties. The error arguments comprising of angle and angular rate for quadrotors were ultimately uniformly bounded and the angles controlling the deviations were restricted to the funnel boundary. Finally, the simulations and experiments verified the superiority of the proposed control technique in terms of reduced control actions and higher precision, as well as shorter settling time.