In this work, a multilayer-modified paper-based colorimetric sensing platform with improved color uniformity and intensity was developed for the sensitive and selective determination of uric acid and glucose with smartphone as signal readout. In detail, chitosan, different kinds of chromogenic reagents, and horseradish peroxidase (HRP) combined with a specific oxidase, e.g., uricase or glucose oxidase (GOD), were immoblized onto the paper substrate to form a multilayer-modified test paper. Hydrogen peroxide produced by the oxidases (uricase or GOD) reacts with the substrates (uric acid or glucose), and could oxidize the co-immoblized chromogenic reagents to form colored products with HRP as catalyst. A simple strategy by placing the test paper on top of a light-emitting diode lamp was adopted to efficiently prevent influence from the external light. The color images were recorded by the smartphone camera, and then the gray values of the color images were calculated for quantitative analysis. The developed method provided a wide linear response from 0.01 to 1.0 mM for uric acid detection and from 0.02 to 4.0 mM for glucose detection, with a limit of detection (LOD) as low as 0.003 and 0.014 mM, respectively, which was much lower than for previously reported paper-based colorimetric assays. The proposed assays were successfully applied to uric acid and glucose detection in real serum samples. Furthermore, the enhanced analytical performance of the proposed method allowed the non-invasive detection of glucose levels in tear samples, which holds great potential for point-of-care analysis. Graphical abstract ᅟ.
Differential steering is a unique steering technology for distributed drive vehicles, which cannot only be applied to steering power, but also be used as a backup steering scheme for distributed drive vehicles. When the road adhesion conditions are poor, differential steering will lead to wheel slip, reduce the tire lateral force margin, and then affect the vehicle stability. To solve this issue, it is necessary to integrate the differential steering and anti-skid drive control. In this paper, the four-wheel distributed drive electric vehicle (DDEV), tire and wheel dynamics models are firstly established. Then the coordinated control strategy is proposed for anti-skid driving and differential steering of DDEV, where a neural network is adopted for the weights regulation of the two controllers. Next, an improved slip rate estimation method avoiding the calculation of vehicle speed is proposed which can reduce the error when the wheel angular acceleration is less than zero. Then, the slip rate fuzzy threshold controller is designed to control the wheel slip rate, and the feed forward-feedback differential torque controller is designed to obtain the differential torque required in steering. Considering the vehicle stability, the torque distribution adopts the quadratic programing method and combines the constraints of wheel slip rate and tire load rate to optimize the distribution of each wheel’s driving torque. Finally, the joint simulation and hardware-in-the-loop (HIL) are carried out to verify the effectiveness of the proposed improved slip rate estimation method and control strategy. The results show that the calculation error is reduced to 5.13% with the improved slip rate estimation results, and the superiority of proposed control method is verified by both simulation and HIL test.
Distributed drive vehicles are prone to wheel slip during driving on low adhesion coefficient roads. Wheel slip will not only cause energy loss, but also the different driving states of the two sides of the wheels will lead to a sharp deterioration in vehicle stability, which will adversely affect the dynamics and safety of the vehicle. For the control characteristics of distributed drive vehicles, a slip rate controller is designed on the basis of slip rate estimation, and a stability control strategy adapted to straight and steering driving are proposed. Firstly, the road surface is identified based on the Burckhardt tire model, and the optimal slip rate of the current road surface is estimated. And the optimal wheel speed corresponding to the current vehicle speed is calculated. An active disturbance rejection controller (ADRC) is established, which controls the four-wheel speeds by adjusting the motor output torque and tracks the optimal wheel speed corresponding to the optimal slip rate. The sliding-mode controller is designed considering the stability requirements of the vehicle during high-speed steering. And the wheel output torque is optimally allocated based on the quadratic programing method. Finally, joint simulations and hardware-in-the-loop tests verify the effectiveness of the control strategy proposed in this paper.
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