Self-Tuning Generalized Predictive Controller

The self-tuning generalized predictive controller (GPC) developed by Clark et al. employs a parameter model-based predictive control algorithm that utilizes time-domain optimization performance indicators combined with system identification and self-correction mechanisms. This methodology effectively resolves challenges present in traditional self-tuning control systems. The implemented code provides a univariate GPC program derived through Diophantine equations, featuring key algorithmic components including: - Recursive least squares (RLS) parameter estimation for real-time model adaptation - Multi-step prediction computation using polynomial matrix operations - Rolling optimization with receding horizon control implementation - Diophantine equation solutions for systematic prediction derivation This implementation simplifies predictive control application through modular architecture, allowing straightforward configuration of prediction horizons and control weighting parameters. In practical applications, the algorithm enhances control system performance by improving dynamic response characteristics while ensuring stability and reliability. The combination of adaptive capability and predictive optimization makes this approach particularly valuable for industrial processes with time-varying dynamics, demonstrating broad application prospects across automotive, chemical, and energy management systems.