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Due to the simultaneous existence of model uncertainties and external disturbances, designing automated ground vehicle path-following controllers is recognized as a challenging task. The $H_{∞}$ robust control methodology, as one of the accomplished strategies for controller robustification, has been commonly adopted by researchers to address the vehicle path-tracking problems. Nevertheless, despite its advantages, the $H_{∞}$ controller is only capable of limiting the total ``energy'' of the tracking errors. On the other hand, from a safety standpoint, constraining the ``peak'' of the tracking errors may carry an equal or more importance. To establish a guaranteed upper bound on the path-tracking errors, this paper proposes a novel methodology to synthesis the ground vehicle path-following controller in light of the energy-to-peak robust control theory. Additionally, to address the time-varying uncertainties presented in the tire dynamics, robust stabilization constraints based upon the small-gain theorem are also formulated into the overall controller design problem. Comparative study regarding the disturbance rejection performance between the proposed controller and the conventional $H_{∞}$ approach is conducted via CarSim-Simulink joint simulations. Furthermore, the robustness and disturbance attenuation ability of the energy-to-peak path-tracking controller is experimentally verified on a scaled car.

Various collision-avoidance assistance (CAA) systems, such as automatic emergency braking (AEB) and lane-keeping assistance (LKA), have been developed in the last decades to enhance the active safety of ground vehicles. Meanwhile, more electronic computing units (ECUs) have been embedded inside a vehicle to support the diversified CAA systems, which complicate the automotive electrical/electronic architecture and increase the cost. Instead of adding extra ECUs, we propose to allocate the existing implementation resources, i.e., the available processor time and memory space to the CAA systems, per individual driver's maneuver capabilities. As an illustrative example, we first show that two drivers can exhibit distinct maneuvers in a pre-crash situation on highway, according to which they can be classified as either steering-oriented or braking-oriented. Then, we design two CAA systems: an AEB and an LKA, based on the ultra-local model predictive control method. Furthermore, we show that by adjusting the prediction horizons of the two controllers, the implementation resources can be allocated to the two CAA systems in different fashions, which yields three control modes: standard mode, steering-enhanced mode, and braking-enhanced mode. Finally, by comparing the control performance of each driver-type/control-mode pair through both CarSim-Simulink joint simulations and driver-in-the-loop simulator experiments, we demonstrate that by allocating more resources to compensate for the weakness of a driver's maneuver, the CAA systems can provide enhanced driving safety by consuming the same overall amount of the implementation resources.

Affected by complex boundary conditions and redundant structural assembly, it is difficult to collect stress and strain information on key structures of complex mechanical equipment, resulting in difficult life monitoring and large calculation errors. The purpose of this study is to provide reliable strain data for accurate life prediction and solve the problem of obtaining critical structural strain under uncertain loads and other complex boundary conditions. In this paper, a neural network training dataset is established based on finite element analysis data and experimental data, and the neural network architecture and parameter optimization are carried out for each data set. Finally, the strain reconstruction model based on a neural network is established, which solves the two problems of accurate equivalence from structure to strain and equivalence from strain to load. Feature experimental samples were designed to verify the universality of the research method. The model is experimentally verified with an average error of less than 5%. It realizes the global strain reconstruction from the local measurement point to the overall structure, which can be used for real-time and full-cycle life monitoring of the structure

The self-assembly of a series of catalytically active polypeptides toward hydrolysis of glucoside compounds, namely, gastrodin, polydatin and esculin was investigated. These active peptides are composed of two functional fragments: one is the hydrophobic sequence LHLHLRL, which forms assembling segments in the presence of Zn ions (Zn

Trajectory following of autonomous vehicle is a challenging task because of the multiple constraints imposed on the plant. Therefore, Model Predictive Control (MPC) is becoming prevail in vehicle motion control as it can explicitly handle system constraints. However, MPC, grounded in real-time iterative optimization, entails a considerable computational burden for current electronic control units. To mitigate the MPC execution load, a popular strategy is to linearize the original (nonlinear) system around the current working point and then design a Linear Time-Varying MPC (LTVMPC). Nevertheless, the successive linearization introduces extra modeling errors, which may impair the control performance. Indeed, if the plant model satisfies the ‘differential flatness’ condition, it can be exactly linearized to the Brunovsky’s canonical form. In contrast to the LTV model, this newly appeared linear form reserves all the nonlinear features of the native plant model. Based on this equivalent linear system, a Flatness Model Predictive Controller (FMPC) can be formulated. FMPC on the one hand, improves the control performance over an LTVMPC because it avoids extra modeling errors from the local linearization. On the other hand, it entails a much lighter computational load versus a nonlinear MPC thanks to its linear nature. Real-time simulations conducted on a hardware-in-the-loop system indicate the advantages of the proposed FMPC in autonomous vehicle trajectory following.

In this letter, a systematic synthesis of a new class of smooth parameter projection operators is presented. To elaborate such an approach, the adaptive control problem for a nth-order, single-input, linearly parametrizable, nonlinear system in the controllable canonical structure is considered. The stability of the closed-loop adaptive system, with the augmentation of such a class of smooth projection operators, is analyzed by a Lyapunov-like analysis. With this systematic construction, two novel smooth projection operators are devised as examples. A simulation study is performed to validate the proposed strategy and compare its performance against a non-smooth, parameter projection solution.

Heterocyclic amines (HCA) are the main mutagenic factors in cooked meat products and are considered to be hazardous chemicals in the field of food safety. In this study, inspired by the "host-guest" interaction mechanism, a new technique for detecting 2-amino-1-methyl-6-phenylimidazo [4,5

Driver's steering torque is an essential signal for automated and assistance driving systems to interpret the human driver's intention. In a steer-by-wire (SBW) apparatus, due to the inevitable couplings between the driver steering, electrical motor, and viscous frictional torques, such a vital signal cannot be directly measured via a torque transducer. Instead, model-based estimators are considered a more practicable route. Toward this end, this paper introduces a novel mixed L1/H2 observer to achieve a robust estimation respecting the human driver's steering torque in human-alone and human-automation shared driving. On the one hand, the L1 induced norm from the process disturbance to the estimation error is attenuated. On the other hand, the H2 synthesis seeks to suppress the effects of measurement noises from the steering angle encoder and the motor's current sensor. Driving simulator human subject experiments are effectuated to justify the proposed strategy and manifest its superiorities over a benchmark method.

In a Vehicular Cyber Physical System (VCPS), ensuring the real-time delivery of safety messages is an important research problem for Vehicle to Vehicle (V2V) communication. Unfortunately, existing work relies only on one or two pre-selected control channels for safety message communication, which can result in poor packet delivery and potential accident when the vehicle density is high. If all the available channels can be dynamically utilized when the control channel is having severe contention, then safety messages can have a much better chance to meet their real-time deadlines. In this article, we propose MC-Safe, a multi-channel V2V communication framework that monitors all the available channels and dynamically selects the best one for safety message transmission. During normal driving, MC-Safe monitors periodic beacons sent by other vehicles and estimates the communication delay on all the channels. Upon the detection of a potential accident, MC-Safe leverages a novel channel negotiation scheme that allows all the involved vehicles to work collaboratively, in a distributed manner, for identifying a communication channel that meets the delay requirement. MC-safe also features a novel coordinator selection algorithm that minimizes the delay of channel negotiation. Once a channel is selected, all the involved vehicles switch to the same selected channel for real-time communication with the least amount of interference. Our evaluation results both in simulation and on a hardware testbed with scaled cars show that MC-Safe outperforms existing single-channel solutions and other well-designed multi-channel baselines by having a 23.4% lower packet delay on average compared with other well-designed channel selection baselines.