This paper proposes a switching-dependent prescribed performance adaptive control (SPPAC) scheme for a class of switched nonlinear systems to achieve prescribed tracking performance and practical finite-time stability. To accommodate the distinct performance requirements and dynamic characteristics of individual subsystems, switching-dependent prescribed performance bounds (PPBs) are constructed and adjusted according to the tracking error at switching instants. To address discontinuities in the transformed tracking error induced by these switching-dependent PPBs, a model transformation method is developed to reformulate the original constrained switched system as an equivalent unconstrained system. The SPPAC scheme is designed by using adaptive backstepping under an average dwell-time  switching condition. Sufficient conditions are derived to guarantee the boundedness of all closed-loop signals, satisfaction of the prescribed tracking bounds, and practical finite-time convergence. Furthermore,  an explicit upper bound on the settling time is established. Finally, two numerical examples are presented to demonstrate the effectiveness of the proposed SPPAC scheme.

The laser-directed energy deposition (L-DED) process involves complex thermal and material interactions, posing challenges for controlling microstructural evolution in metal additive manufacturing. To enable rapid, efficient process optimization, this study develops a physics-informed mechanism-data fusion framework that couples macroscopic finite element method (FEM) and microscopic phase-field method (PFM) with a dual-level long short-term memory (LSTM) neural network. A multiscale physical model is employed to investigate grain growth behavior and the influence of process parameters on microstructural morphology. To map this non-linear temporal evolution, a dual-level data-driven surrogate model is developed. Compared to a conventional multilayer perceptron (MLP), the LSTM effectively captures time-dependent thermal accumulation. In recursive predictions, four initial input layers were identified as the optimal initialization length, enabling stable prediction and mitigating potential layer-wise error propagation, maintaining average R² values of around 0.95. Using the average grain area as the primary microstructural control target, and incorporating the average grain width, a parameter range of 500–550 W and 13–14 mm/s was identified for achieving near-equiaxed β grains. The proposed framework is implemented as a modular computational pipeline that integrates finite element, phase-field, and surrogate models for reusable and extensible process-microstructure prediction in metal additive manufacturing.

In this paper, an adaptive neural asymptotic tracking control scheme with the event-triggered mechanism is presented for a quarter-car active suspension system (ASS) with unknown road inputs and input saturation. In the control design, an auxiliary system is constructed to compensate for the input saturation of the actuator, and a linear filter is introduced to solve the chattering problem of the suspension system under the zero dynamics. By integrating integral-bounded functions into the adaptive law and control law, asymptotic convergence of the tracking error is achieved with high precision. Using the adaptive backstepping control method and introducing the command filter, an event-triggered adaptive neural asymptotic tracking control algorithm is developed, in which the radial basis function neural networks (RBFNNs) are used to approximate the unknown model dynamics. Considering the waste of communication resources in the controller, the event-triggered control (ETC) law in the controller-to-actuator channel is designed. Benefiting from the minimal learning parameters (MLP) technique, the proposed scheme requires updating only one parameter, which reduces computational complexity and saves communication resources. By using the Lyapunov’s method and the Barbalat’s lemma, the asymptotic stability of the closed-loop system is proved, and the constraint conditions for vehicle ride comfort are also guaranteed. Finally, the effectiveness of the proposed method is further verified through simulations and experimental results.