Multi-Input Multi-Output (MIMO) Predictive Control

Multi-Input Multi-Output (MIMO) predictive control is an advanced control strategy widely applied in industrial processes, particularly suitable for complex systems requiring simultaneous optimization of multiple inputs and outputs. Unlike traditional single-variable control, MIMO predictive control can coordinate interactions among multiple actuators and sensors, thereby improving overall system performance.

In MIMO predictive control, controller design relies on the system's dynamic model. Through predictive modeling, the controller can forecast input-output behavior over a future time horizon and optimize control actions to ensure the system meets constraints (such as input limits, output ranges, etc.). This approach is especially effective for multivariable systems with time delays, strong coupling, or nonlinear characteristics.

Using MATLAB for MIMO predictive control simulation offers significant advantages: Convenient Modeling: MATLAB provides powerful system identification and state-space modeling tools, such as the `ss` and `idss` functions, which can be used to construct predictive models for MIMO systems. Optimization Solutions: The Model Predictive Control Toolbox offers ready-made MPC controller design capabilities, allowing users to define objective functions, constraints, and automatically compute optimal control laws. Visual Validation: Simulation results can be intuitively displayed using MATLAB's plotting tools, facilitating analysis of multivariable system dynamic responses, coupling effects, and control performance.

Key steps in practical simulations include: Establishing a state-space model or transfer function matrix to describe dynamic relationships between inputs and outputs. Defining prediction horizons and control horizons to balance computational complexity and control performance. Configuring optimization objectives (such as minimizing tracking errors or suppressing input change rates) and constraint conditions. Running closed-loop simulations to observe output tracking capabilities and input regulation characteristics.

MIMO predictive control finds extensive applications in chemical processes, aircraft control, smart grids, and other fields, with MATLAB simulations providing an efficient platform for algorithm validation and parameter tuning.