Genetic Algorithm Engine Calibration with Penalty Functions

The patent applies preliminary action by using genetic algorithms to perform a global search before refining results with gradient-based methods. The genetic algorithm phase prepares the calibration by exploring the parameter space broadly and identifying promising regions, which then serves as the starting point for more efficient local optimization, thereby avoiding local optima while maintaining computational efficiency.

The patent replaces the mechanical gradient search mechanism with a biological inspiration-based genetic algorithm system. Instead of relying on gradient descent that follows local slopes, the system uses genetic operators (selection, crossover, mutation) to explore the parameter space, substituting a more robust search mechanism that doesn't get trapped in local optima.

The patent applies parameter changes by systematically optimizing the parameters of the scheduled linear model through genetic algorithms. The calibration process adjusts model parameters and controller gains to achieve desired stability characteristics while maintaining the simplicity of the linear model structure. Penalty functions are used to enforce stability constraints during the optimization process.

The patent implements feedback through the genetic algorithm optimization loop that continuously evaluates candidate solutions against performance criteria including stability and non-oscillatory behavior. The algorithm uses feedback from cost function evaluations to guide the search toward parameter sets that ensure stable control while maintaining model simplicity.