Electric Truck Steer-by-Wire Control Under Random Network Time Lag
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Solution Overview
Problem
Existing electric truck steer-by-wire systems face challenges in coordinating tracking accuracy, system stability, and operation time due to random network time lag, which affects the robustness and stability of the control system.
Innovation Solution
An electric truck steer-by-wire system design incorporating a master control electric power module, slave control electric power module, road sensing motor module, and adaptive model predictive control algorithm to account for random network time lag, using a network uncertainty control method that establishes a trajectory tracking linear error dynamic model and constructs an adaptive prediction model to enhance system stability and tracking accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Stability of the object's composition
If robust control algorithm is used to handle time lag system, then system stability is improved, but control performance is compromised due to conservative control strategy
Solution Approach 1:
The patent applies predictive control by calculating future control actions based on predicted system states. The controller predicts the system state at future time points and determines control actions in advance, thereby compensating for the effects of time lag before they actually occur. This preliminary action approach allows the system to maintain both stability and control performance by proactively counteracting the delayed effects rather than reactively responding to them.
2Measurement precision
If model predictive control is used to solve constrained open-loop quadratic rolling optimization, then tracking accuracy is improved, but closed-loop system stability cannot be guaranteed
Solution Approach 1:
The patent implements a closed-loop model predictive control that continuously monitors the actual system state and compares it with the predicted state. Based on the prediction error and feedback information, the controller adjusts future control actions to ensure both tracking accuracy and stability. The feedback mechanism allows the system to correct deviations caused by time lag while maintaining the optimal control performance achieved through predictive optimization.
Solution Approach 2:
The patent employs dynamic predictive control where the prediction horizon and control parameters are adjusted in real-time based on system conditions. The controller dynamically adapts its predictive model and optimization parameters to account for varying time lag characteristics, ensuring both tracking accuracy and stability across different operating conditions. This dynamic approach allows the system to optimize performance while guaranteeing stability margins.
3Reliability
If electric truck steer-by-wire system is equipped with more sensors and actuators to achieve advanced driver assistance system, then driving safety is improved, but random network time lag increases due to network bandwidth limitations
Solution Approach 1:
The patent applies predictive control to compensate for network time lag in steer-by-wire systems. By predicting future steering wheel angles and calculating appropriate rack angles in advance, the controller compensates for the time delay introduced by increased sensor and actuator communication. This allows the system to maintain driving safety with multiple sensors and actuators while mitigating the network time lag they inevitably introduce.
Data Source
AI summary
An electric truck steer-by-wire system and a net-work uncertainty control method therefor. Said system comprises: a master control electric power module (14), a slave control electric power module (18), a road-sensing motor module (4), a steering wheel (1), an upper steering column (3), a lower steering column (10), a rack and pinion steering mechanism (12), wheels (13), a first steering angle sensor (2), a second steering angle sensor (11), a steering domain controller (8), and a vehicle-mounted CAN network (9). By means of the design of the master and slave controllers and actuating mechanisms, the system combines the advantages of steering angle tracking, torque tracking and current tracking, there-by satisfying the steering response requirements and power requirements of the electric truck, and further enhancing the robustness and fault-tolerant performance of the system under a random network time lag.