Predictive directional compensator for systems with input constraints.

Area of Science:

• Control Systems Engineering
• Nonlinear Control Theory

Background:

• Actuator constraints introduce nonlinearity, degrading control system performance.
• Model predictive control (MPC) addresses constraints via optimization but demands high computational power.
• Iterative solutions in MPC lead to significant computational requirements.

Purpose of the Study:

• To propose a new method for handling actuator constraints in control systems.
• To reduce the computational burden associated with constrained model predictive control.
• To maintain closed-loop performance while minimizing computational cost.

Main Methods:

• Separating constraint handling from the predictive control task.
• Introducing a predictive directional compensator to manage input constraint effects.
• Utilizing directionality and predictive concepts within the compensator.
• Defining a new characteristic matrix for system directionality analysis.

Main Results:

• Significantly reduced computational requirements for the control system.
• Minimal degradation in closed-loop performance compared to traditional methods.
• Successful handling of input constraint effects through the predictive directional compensator.
• Development of a characteristic matrix to determine system directionality.

Conclusions:

• The proposed method effectively reduces computational load in constrained control systems.
• The predictive directional compensator offers an efficient way to manage actuator constraints.
• The new characteristic matrix provides insights into system directionality for SISO and nonminimum phase systems.