Predictive Control Program Based on Controlled Auto-Regressive Integrated Moving Average (CARIMA) Model Control Objects

A predictive control program based on the Controlled Auto-Regressive Integrated Moving Average (CARIMA) model is a widely adopted control methodology that forecasts future states of controlled objects and adjusts control parameters accordingly to achieve precise regulation. The CARIMA model integrates characteristics of auto-regression, moving average, and integral components, demonstrating robust adaptability and stability in various control scenarios. In practical implementation, the algorithm typically involves: 1. System identification using historical data to determine CARIMA model parameters 2. Multi-step ahead prediction computation using recursive difference equations 3. Optimization of control signals through cost function minimization (often quadratic performance criteria) 4. Real-time parameter adaptation for handling system nonlinearities and disturbances Key computational aspects include: - Employing Diophantine equations for prediction decomposition - Implementing receding horizon control strategy - Solving optimization problems using numerical methods like quadratic programming - Incorporating integral action for zero steady-state error This control approach finds extensive applications in control systems, particularly in industrial automation domains, where it delivers significant practical value for process control, robotics, and automotive systems. The method's capability to handle constraints and process delays makes it suitable for complex industrial environments requiring high-precision control performance.