18060023. LEARNING-MODEL PREDICTIVE CONTROL WITH MULTI-STEP PREDICTION FOR VEHICLE MOTION CONTROL simplified abstract (GM Global Technology Operations LLC)
Contents
- 1 LEARNING-MODEL PREDICTIVE CONTROL WITH MULTI-STEP PREDICTION FOR VEHICLE MOTION CONTROL
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 LEARNING-MODEL PREDICTIVE CONTROL WITH MULTI-STEP PREDICTION FOR VEHICLE MOTION CONTROL - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Potential Commercial Applications
- 1.9 Possible Prior Art
- 1.10 How does the system handle real-time data processing for quick decision-making in dynamic driving situations?
- 1.11 What are the potential limitations or challenges of implementing this technology in practical applications?
- 1.12 Original Abstract Submitted
LEARNING-MODEL PREDICTIVE CONTROL WITH MULTI-STEP PREDICTION FOR VEHICLE MOTION CONTROL
Organization Name
GM Global Technology Operations LLC
Inventor(s)
Amir Khajepour of Waterloo (CA)
Yubiao Zhang of Sterling Heights MI (US)
SeyedAlireza Kasaiezadeh Mahabadi of Novi MI (US)
LEARNING-MODEL PREDICTIVE CONTROL WITH MULTI-STEP PREDICTION FOR VEHICLE MOTION CONTROL - A simplified explanation of the abstract
This abstract first appeared for US patent application 18060023 titled 'LEARNING-MODEL PREDICTIVE CONTROL WITH MULTI-STEP PREDICTION FOR VEHICLE MOTION CONTROL
Simplified Explanation
The system described in the patent application is a learning-model predictive control (LMPC) system with multi-step prediction for motion control of a vehicle. The system includes sensors and actuators, as well as control modules with processors, memory, and input/output ports. The processors execute program code portions stored in the memory to obtain vehicle state information, receive driver input, generate desired dynamic output, estimate actions of the actuators, and select models of a physics-based vehicle model and a machine-learning model to adjust commands to the actuators.
- The system utilizes sensors and actuators to gather vehicle state information and generate dynamic outputs based on driver input.
- The system estimates actions of the actuators and selects models of a physics-based vehicle model and a machine-learning model to adjust commands to the actuators.
Potential Applications
The technology described in this patent application could be applied in various industries such as automotive, robotics, and autonomous systems for precise motion control and optimization.
Problems Solved
This technology solves the problem of efficiently controlling the motion of a vehicle by utilizing a combination of physics-based models and machine-learning models to adjust commands to the actuators.
Benefits
The benefits of this technology include improved accuracy in motion control, enhanced efficiency in vehicle operation, and the ability to adapt to changing driving conditions.
Potential Commercial Applications
The potential commercial applications of this technology include autonomous vehicles, industrial robotics, and smart transportation systems.
Possible Prior Art
One possible prior art for this technology could be traditional model predictive control systems used in the automotive industry for vehicle motion control.
Unanswered Questions
How does the system handle real-time data processing for quick decision-making in dynamic driving situations?
The patent application does not provide specific details on the real-time data processing capabilities of the system.
What are the potential limitations or challenges of implementing this technology in practical applications?
The patent application does not address any potential limitations or challenges that may arise when implementing this technology in real-world scenarios.
Original Abstract Submitted
A system for learning-model predictive control (LMPC) with multi-step prediction for motion control of a vehicle includes sensors and actuators. One or more control modules each having a processor, a memory, and input/output (I/O) ports are in communication with the sensors and actuators, the processor executing program code portions stored in the memory. The program code portions cause the sensors and actuators to obtain vehicle state information, receive a driver input, and generate a desired dynamic output based on the driver input and the vehicle state information. A program code portion estimates actions of the actuators based on the vehicle state information and the driver input, and utilizes the vehicle state information, the driver input, and the estimated actions of the actuators to select one or more models of a physics-based vehicle model and a machine-learning model of the vehicle to selectively adjust commands to the actuators.
