Dynamic Vehicle Control for Fleet Mission Deviation
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Solution Overview
Problem
Current methods for controlling multiple vehicles performing missions along a route struggle to maximize productivity while keeping operating costs low, especially in the face of unexpected events like traffic jams.
Innovation Solution
A method that dynamically adjusts balance parameter values for each vehicle based on mission completion deviation and progress, allowing for real-time control to reduce delays while minimizing operating costs, by determining a first balance parameter indicative of operating cost and progress, and a second balance parameter dependent on mission completion deviation, to optimize vehicle operation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional static scheduling methods are used to manage vehicle deliveries, then operational simplicity is maintained, but productivity cannot be maximized and operating costs cannot be optimized in real-time
Solution Approach 1:
The patent applies dynamics by transitioning from static scheduling to dynamic real-time control. The system continuously monitors vehicle positions, mission completion status, and operational parameters, then dynamically adjusts balance parameters and control commands to optimize productivity and costs during mission execution, rather than relying on pre-planned static schedules.
Solution Approach 2:
The patent implements feedback mechanisms by continuously obtaining actual vehicle data (positions, mission completion status, balance parameters) and using this information to determine control commands. The system compares actual mission completion against desired trajectories and adjusts control parameters in real-time based on the deviations detected, creating a closed-loop control system.
2Productivity
If real-time dynamic control is implemented to maximize productivity, then operating costs can be optimized, but the complexity of control systems and data processing increases
Solution Approach 1:
The patent uses parameter changes by dynamically adjusting balance parameters that weigh different objectives (e.g., mission completion speed vs. fuel consumption vs. operating cost). The control system modifies these parameters in real-time based on current mission status, vehicle conditions, and environmental factors, allowing flexible optimization without requiring complex control algorithms for each individual decision.
Solution Approach 2:
The patent applies segmentation by dividing the control system into modular components: data acquisition modules, parameter calculation modules, decision-making modules, and execution modules. Each module handles specific tasks independently, which simplifies the overall system architecture and makes the complex control problem more manageable through functional decomposition.
3Productivity
If aggressive speed increases are applied to reduce mission completion deviation, then productivity improves, but operating costs increase due to higher fuel consumption and wear
Solution Approach 1:
The patent dynamically changes the balance parameter that weighs mission completion speed against operating cost. When mission completion deviation is large and time is critical, the parameter shifts to favor faster completion even at higher cost. When deviation is small or costs are a primary concern, the parameter adjusts to favor fuel efficiency and lower operating costs, creating a flexible trade-off mechanism.
Solution Approach 2:
The system dynamically adjusts vehicle speed and control commands based on real-time conditions rather than applying fixed aggressive acceleration. The control system optimizes speed profiles to achieve mission objectives while considering fuel consumption characteristics, allowing smooth transitions between speed levels that minimize both time deviation and energy waste.
4Measurement precision
If multiple balance parameter values are calculated and adjusted in real-time, then control precision and mission completion accuracy improve, but computational requirements and processing time increase
Solution Approach 1:
The patent focuses on calculating and adjusting a limited set of key balance parameters rather than optimizing all possible control variables simultaneously. By identifying the most critical parameters (such as the balance between mission completion speed and operating cost), the system achieves high control precision with reduced computational burden compared to full-state optimization.
Data Source
AI summary
A method for controlling a plurality of vehicles performing missions along a respective route includes obtaining for each vehicle of the plurality of vehicles, a first value of a balance parameter, indicative of a balance between a cost for operating the respective vehicle along at least a part of the route, and a progress of the respective vehicle along at least a part of the route, establishing, in dependence on the first balance parameter values, a desired number of completed missions as a function of time, after an initial of the missions has started, and before all missions are completed, determining a mission completion deviation comprising a deviation of an actual number of completed missions from the desired number of completed missions, obtaining for each of one or more of the vehicles a second balance parameter value, different from the respective first balance parameter value, the respective second balance parameter value being dependent on the mission completion deviation, and controlling the one or more of the vehicles in dependence on the respective second balance parameter value.