Browse Topic: Electronic control systems
A switched adaptive flight control architecture is proposed for fixed-wing carrier-sub UAVs to handle abrupt mass and inertia variations, as well as parameter uncertainties induced by transient aerodynamic interference, during sequential deployment. The separation process is rigorously modeled as a switched linear system, and a combined direct and indirect adaptive control scheme featuring an online Recursive Least Squares (RLS) modification is designed to compensate for model uncertainties. Furthermore, a dwell-time admissible switching law is established by solving Linear Matrix Inequalities (LMIs), ensuring the stability of the switching process through theoretically guaranteed Lyapunov energy drops at switching instants. Numerical simulations verify that transient tracking error spikes are significantly suppressed, and high-frequency oscillations are effectively attenuated compared to baseline controllers.
Commercial vehicle fleets frequently operate with tractors that connect to different trailers and dollies, resulting in combinations with varying brake pad wear across wheel ends. Traditional brake-force distribution strategies do not consider these pad-life differences, which can lead to uneven brake utilization, irregular maintenance intervals, and increased total cost of ownership (TCO) in mixed-trailer operations [7, 9]. While modern electronically controlled braking systems (EBS) already incorporate pad wear based braking for the tractor itself [5], these capabilities do not extend across the entire vehicle combination because trailer-side communication is typically limited to standardized CAN protocols such as ISO 11992 and J1939 [1, 2, 3]. As braking systems become more software defined and rely heavily on distributed electronic communication, ensuring the authenticity and integrity of trailer originated brake information becomes essential for both functional safety and cybersecurity [6]. In the proposed architecture, trailers and dollies communicate brake related data to the tractor over the ISO 11992 Tractor-Trailer CAN (TT-CAN) network [1, 2], allowing the tractor Brake Control ECU to securely validate the source of the information and register each towed unit for health aware braking. Once authenticated pad life data is available, the tractor constructs a combination level brake health map covering every wheel end in the configuration. During normal braking, a supervisory allocator computes wheel end specific brake pressure targets that bias braking toward wheel ends with greater remaining pad life while ensuring full compliance with stopping distance regulations and stability requirements [4, 7]. By integrating authenticated pad wear information with tractor hosted supervisory control, the system improves braking consistency across mixed combinations, harmonizes pad utilization, enhances maintenance predictability, and reduces TCO while meeting the safety and cybersecurity expectations of modern commercial vehicle fleets.
This paper proposes a nonlinear and robust State-Dependent Riccati Equation (SDRE) combined with H∞ control architecture for brake- by-wire systems, specifically designed to handle severe tire-road friction variations and μ-split scenarios. The primary objective is to maximize deceleration capabilities while rigorously maintaining yaw stability, trajectory tracking, and passenger comfort through jerk limitation. Situated within the domain of active safety, this research addresses robustness against real-world uncertainties by utilizing a high-fidelity 14-degree-of-freedom vehicle model that accounts for longitudinal, lateral, and yaw dynamics, suspension-induced pitch and roll effects, and nonlinear tire behavior with explicit load transfer. To ensure near-optimal slip tracking under variable surface conditions, the system employs online friction estimation via Extended and Unscented Kalman Filters (EKF/UKF) fusing wheel and IMU data to adaptively adjust slip targets. The control strategy is bifurcated: the SDRE component manages dominant nonlinearities through state-dependent gains to prevent wheel lock-up, while the H∞ component provides robust disturbance rejection against parametric uncertainties such as mass variations and sensor noise. Control efforts are distributed via a Quadratic Programming (QP) torque allocator featuring anti-windup mechanisms and explicit saturation handling to compensate for lateral drift during μ-split braking. Validation is conducted through a Model- in-the-Loop (MIL) to Software-in-the-Loop (SIL) pipeline using scenarios including wet surfaces and panic braking. Simulation results demonstrate enhanced yaw stability and controlled deceleration profiles compared to conventional baselines, ensuring computational feasibility for automotive Electronic Control Units (ECUs).
Designing, manufacturing, and controlling a high-precision gantry positioning system is a complex, multidisciplinary task demanding expertise in structural mechanics, thermal modeling, mechatronics, and advanced motion control.
Automakers and suppliers have been embedding electronic control systems and software into vehicles since the mid-1970s. But Tesla fundamentally changed the game in 2012 with the introduction of the Model S. That car was arguably the first software-defined vehicle (SDV) and it led to a fundamental shift in how the industry designed new vehicles and how financial markets thought about the industry. However, 14 years later, as the Model S is about to drive off into the sunset, the legacy auto industry is still struggling to build vehicles with modern software and find a way to make money from them. As a reminder, prior to the Model S, software and the underlying electronics, sensors and actuators were always tightly coupled together. Aside from addressing recalls for regulatory compliance, the software was not designed to be updated once the vehicle was shipped from the factory. For the most part, a car had the same functionality after 200,000 miles of use as it did when the owner drove it off the dealer lot, and there was no expectation that manufacturers would ever push out new capabilities during the car's life.
Deep Reinforcement Learning (DRL) for quadrotor flight control typically relies on Domain Randomization (DR) for sim-to-real transfer, resulting in overly conservative policies that struggle with dynamic disturbances. To overcome this, we propose a novel adaptive control architecture that actively perceives and reacts to instantaneous perturbations. First, we train an optimal outer-loop policy, then replace its reliance on ground-truth disturbance data with a Residual Dynamics Predictor (RDP). The RDP estimates the external forces and moments acting on the aircraft in flight online using only the history of states and control actions. For seamless hardware transfer, we introduce a data-efficient linear calibration bridge and an online thrust correction mechanism that align the simulated latent space with reality using mere seconds of flight data. Real-world validations on a Crazyflie micro-quadrotor demonstrate that our adaptive controller significantly outperforms baselines, maintaining precise trajectory tracking under severe uncertainties including mass variations, asymmetric payloads, and dynamic slung loads.
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