Mission-critical adaptive control for platforms that operate where failure is not an option.
Real-time thrust modulation across five discrete modes. Responds to environmental conditions, energy state, and proximity to objective in sub-second decision cycles.
Quadratic fuel dynamics modeling with non-stationary disturbance compensation. Achieves mission completion under energy budgets that defeat conventional controllers.
Converts infeasible missions into successful operations. 15:1 rescue-to-loss ratio validated across 1,000 randomized out-of-distribution threat environments.
Hybrid control pipeline separating directional planning from energy management. The planner sets heading. Our system modulates force — adapting to wind, fuel state, and mission geometry.
Discrete thrust states from harvest to maximum burn. Each triggered by local environmental sensing. The mode-switching mechanism is the causal driver of operational efficiency.
Under tight energy constraints, conventional systems fail. Our controller rescues 15 missions for every 1 traded. Validated under non-stationary, adversarial conditions.
Sensor noise, actuator lag, model mismatch, partial observability. Statistically significant gains under every perturbation category. The unknown is our operating environment.
Deploys downstream of existing planners. No system rearchitecture. Drop-in throttle intelligence that augments current capability.
Bootstrap confidence intervals. Paired effect sizes (Cohen's d = 1.30). Counterfactual fairness tests. Publication-grade methodology.
Every subsystem has a measured causal contribution. Systematic elimination testing ensures zero dead weight in the control pipeline.
Training environments are irrelevant to deployment. Validated against out-of-distribution worlds with non-stationary dynamics and corrupted sensors.
Explicit fuel-budget thresholds define the deployment envelope. Autonomous engagement and disengagement rules for safe, bounded operation.
Efficiency is a byproduct of mission success. Every design decision measured against one criterion: objective completion probability.
We work with defense organizations and autonomous platform developers operating under real-world constraints.
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