Event-Triggered Control Scheduling for Unknown Control Laws
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Solution Overview
Problem
Traditional event-triggered control systems rely on known control law structures, which are inadequate for modern cyber-physical systems with opaque or non-deterministic processes, leading to instability and inefficiency.
Innovation Solution
A control structure-agnostic framework that schedules control data transmissions based on error signals and directional alignment metrics, using feedback matrices to ensure stability without requiring knowledge of the control law structure, applicable to both continuous-time and discrete-time systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by moving object
If traditional Lyapunov-based event-triggering is used, then communication load is reduced and energy efficiency is improved, but applicability is limited to systems with known control law structures
Solution Approach 1:
The patent introduces a directional alignment metric as an intermediary between the error signal and control signal comparison. This metric serves as a mediator that enables event-triggering decisions without requiring knowledge of the control law structure, thus bridging the gap between energy efficiency goals and applicability to unknown control laws.
Solution Approach 2:
The patent transforms the traditional event-triggering parameter comparison (direct error vs. threshold) into a new parameter space by introducing the directional alignment metric. This parameter transformation allows the system to maintain energy efficiency while extending applicability to systems with unknown or non-deterministic control laws.
2Loss of energy
If control updates are suppressed to reduce communication load, then energy efficiency is improved, but system responsiveness may deteriorate
Solution Approach 1:
The patent incorporates feedback through the directional alignment metric that continuously monitors the relationship between error signals and control signals. This feedback mechanism ensures that control updates are suppressed only when the alignment indicates stability, maintaining system responsiveness while reducing unnecessary communications.
Solution Approach 2:
The event-triggering threshold is made dynamic through the directional alignment metric, which adapts to the current system state and control signal characteristics. This dynamic adjustment allows the system to respond quickly when needed while suppressing updates during stable periods, balancing energy efficiency and responsiveness.
3Reliability
If event-triggering rules are designed for known control structures, then stability can be guaranteed, but the system cannot handle opaque or non-deterministic control processes
Solution Approach 1:
The directional alignment metric acts as an intermediary that decouples the stability guarantee from the control law structure. By measuring alignment in a way that does not require knowledge of the control law internals, the system can maintain reliability while handling opaque or non-deterministic control processes.
Solution Approach 2:
The patent creates a universal event-triggering mechanism through the directional alignment metric that can handle multiple types of control laws (known, unknown, deterministic, non-deterministic) with a single approach. This universal mechanism maintains stability guarantees across diverse control scenarios without requiring structure-specific designs.
Data Source
AI summary
Technology is disclosed for scheduling control data transmissions in a control system that operates independently of specific control structures. The method begins by receiving the current system state of a controlled electronic device. Using a model of the device, a nominal system state and corresponding control signal are estimated. An error signal is computed as the difference between the actual and nominal states. This error signal is used to adjust the nominal control signal via a feedback matrix. A previously transmitted control signal is retrieved, and an alignment metric is calculated by comparing the direction of the error signal with the change in control signals. The alignment metric is then evaluated against a condition derived from the magnitude of the error signal. If the condition is satisfied, the adjusted control signal is transmitted. This approach enables efficient and responsive control data scheduling, enhancing system performance while reducing unnecessary transmissions.