To address time delays commonly observed in industrial thermal processes, this article proposes an observer-based Robin boundary control strategy for a class of convection-coupled thermal models. Traditional infinite-dimensional control approaches are often constrained by the simultaneous presence of delayed Robin boundary actuation and convection-coupled dynamics, which prevents the direct use of standard modal decoupling and predictor-based compensation techniques. To overcome this challenge, a lifting function and coordinate transformation are introduced to convert the system into a homogeneous form. This transformation ensures the orthogonality of the weighted Sturm–Liouville eigenfunctions, thereby facilitating the analytical computation of system coefficients through spectral decomposition. Additionally, a finite-dimensional observer is constructed using measurements from a limited number of points, and an integral predictor is designed as the boundary controller to compensate for time delays, enabling precise temperature regulation. The closed-loop stability of the system is established via an explicit Artstein transformation and a carefully designed Lyapunov function. Simulation and experimental results demonstrate that the proposed approach significantly outperforms traditional methods by mitigating actuator saturation and reducing temperature overshoot by 12%, thereby validating its practical value in complex heat transfer processes.
In the above article [1], there were errors on pages 2874 and 2875. They are explained below, and the corrections are provided.
A constrained adaptive actor-critic (AAC)-based policy iteration controller for vision-based shape servoing (VSS) of a deformable object is presented in this article. A finite point-based representation of the deformable object is used, and the deformation Jacobian matrix is approximated using Fourier-series basis functions. An integral concurrent learning (ICL)-based parameter update law is designed to update the deformation model parameters. The high-dimensional state representation of the object requires a large number of parameters to learn the deformation model, value function, and the optimal policy. To address this, a principal component analysis (PCA)-based dimensionality reduction method is used to compute a low-dimensional representation of the object, which is then used to design the constrained AAC controller. A barrier transform (BT) method is used to transform part of the state into a constrained state, thereby ensuring that the physical constraints are satisfied. The proposed controller is extensively tested in simulations across several desired configurations, and through Monte Carlo runs on an ABB IRB120 robot platform.
Although existing methods can steer unmanned surface vehicle (USV) to stably maintain their position at the desired point, poor robustness and slow convergence can be observed in the presence of fast time-varying environmental disturbance. In this article, novel robust positioning laws based on a heading parameter tuning method are proposed to enhance robustness and accelerate convergence of positioning control. By taking the amplitude of resultant environmental disturbance force as the coefficients of the rotation angle adaptive law and the desired heading law, robust positioning laws are developed to weaken the impact of external disturbance. Additionally, the heading parameter tuning method is designed to determine the bounds of both heading controller parameter and the desired heading law parameter. When these parameters approach their boundary values, the sensitivity of the heading error controller can be enhanced, enabling rapid minimization of track errors. Both numerical simulations and field experiments demonstrate that the proposed methodology can achieve rapid convergence of track errors and maintain stable positioning at the desired location under fast time-varying environmental disturbance.
Direct teleoperation of vehicles faces critical technical bottlenecks: communication latency and the operator's inability to physically perceive unmodeled environmental disturbances (e.g., aerodynamic drag, bank angles) coupled with highly nonlinear tire-road dynamics. To address these challenges, we propose a tailored unilateral teleoperation framework. The system integrates the Wave Variable (WV) approach to passively guarantee stability under stochastic delays, and an adaptive Radial Basis Function Network (RBFN) to actively compensate for vehicle-specific uncertainties. Unlike existing WV-neural network architectures designed for bilateral robotic arms, our framework features decoupled adaptive laws specifically designed for vehicle longitudinal and lateral dynamics. Furthermore, compared to model-heavy predictive controllers, the model-free RBFN offers rapid online adaptation without heavy computational overhead. Building upon our preliminary theoretical formulation, this brief paper presents comprehensive comparative analyses and real-world hardware validations. Simulation benchmarks against PID, LQR, MPC, and NMPC demonstrate that the RBFN achieves superior robustness against unmodeled disturbances while requiring orders of magnitude less execution time than MPC and NMPC, making it ideal for resource-constrained vehicle edge computing. Finally, hardware-in-the-loop experiments using a 1/10th scale vehicle over a 4G network validate the system's practical feasibility, safety, and robust trajectory tracking under physical road uncertainties.
