Autonomous Speed Controller Using Predictive Model for Vehicle Dynamics
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Solution Overview
Problem
Existing speed control systems in autonomous and semi-autonomous vehicles are inefficient due to the time-consuming tuning of PID feedback controllers and their insensitivity to changes in vehicle parameters, leading to sluggish speed control.
Innovation Solution
A speed controller system that uses a power train control unit and an autonomy unit to generate control signals based on a speed profile received from an external network, incorporating vehicle dynamic data and road conditions, dynamically adapting to changes in vehicle parameters through frequent updates and predictive control methods.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If PID feedback controllers are used for speed control, then the system structure is simple, but the tuning process is time-consuming and the response is sluggish
Solution Approach 1:
The patent changes the control parameters from traditional PID feedback to model predictive control parameters. The controller uses a vehicle dynamics model with parameters such as mass, drag coefficient, rolling resistance, and gravitational effects to predict future speed and generate optimal control signals, eliminating the need for time-consuming PID tuning while improving responsiveness.
Solution Approach 2:
The controller performs preliminary calculations by predicting future vehicle speed based on current state and road conditions. The model predictive control calculates the optimal throttle and brake signals in advance by solving an optimization problem that considers future error, fuel consumption, and brake wear, allowing the system to respond proactively rather than reactively.
2Device complexity
If traditional speed control systems are used, then the control logic is simple, but the system is insensitive to changes in vehicle parameters
Solution Approach 1:
The patent implements a dynamic control system where the vehicle dynamics model parameters (mass, drag coefficient, rolling resistance, gravitational effects) are continuously updated based on changing vehicle conditions. The controller adapts to parameter changes such as varying vehicle load, wind conditions, and road grade by recalculating the optimal control signals using the updated model parameters, making the system inherently sensitive to vehicle parameter changes.
Solution Approach 2:
The system uses feedback from actual vehicle speed measurements to update the model predictions and adjust control signals. The controller compares predicted speed with actual speed and uses this feedback to refine future control decisions, ensuring the system remains adaptive to changing vehicle parameters through continuous model updating and correction.
3Measurement precision
If frequent updates of control signals are implemented, then the speed control accuracy is improved, but the computational load increases
Solution Approach 1:
The controller implements periodic updates at optimized intervals rather than continuous calculation. The model predictive control solves the optimization problem at discrete time steps, predicting future speed and generating control signals at regular intervals. This periodic approach maintains high accuracy by frequently updating control signals while reducing computational load by avoiding continuous optimization calculations.
Solution Approach 2:
The controller performs partial optimization by focusing on the near-future horizon rather than optimizing over an extended period. The model predictive control calculates optimal throttle and brake signals for a limited prediction horizon, updating control signals frequently but only for the immediate future, which maintains accuracy while limiting computational requirements to manageable levels.
Data Source
AI summary
Method and apparatus are disclosed for a speed controller for a vehicle. An example disclosed vehicle includes a power train control unit and an autonomy unit. The example power train control unit controls a speed of the vehicle based on a control signal. The example autonomy unit (a) receives a speed profile based on a preview of traffic information from an profile generator on an external network, and (b) based on vehicle dynamic data and the speed profile, generate the control signal to control the speed of the vehicle according to the speed profile.