Observer-based Predictive Torque Control (OPC) with disturbance rejection is a control strategy used in multi-motor drives to achieve precise torque control while mitigating the effects of disturbances. This approach combines predictive control techniques with observer-based estimation to enhance the performance of the drive system. Here are the main principles of this control strategy:
Predictive Control: Predictive control is a model-based control strategy that makes future predictions of the system's behavior to optimize control actions. In the context of multi-motor drives, predictive torque control involves predicting the future behavior of each motor's torque output based on the current state of the system and the applied control inputs. This prediction horizon can be a few discrete time steps into the future.
Torque Prediction: The control algorithm calculates the predicted torque for each motor based on the current state variables, such as motor speeds, currents, and rotor positions. These predictions are made using a mathematical model of the motor's dynamics.
Disturbance Rejection: Disturbances, such as load variations or external forces, can affect the accuracy of torque control in multi-motor drives. The disturbance rejection aspect of the control strategy aims to estimate and compensate for these disturbances. This is crucial for maintaining accurate torque control and ensuring system stability.
Observer Design: Observers are mathematical algorithms used to estimate the unmeasured or difficult-to-measure states of a system based on available sensor measurements. In OPC with disturbance rejection, an observer is designed to estimate the states of the motor drives, including internal variables that might not be directly measurable. These estimated states are then used in the predictive control algorithm to make accurate predictions.
State Feedback: The observer's estimated states are used in the predictive control algorithm to compute the optimal control actions for each motor. These control actions are typically the voltages or currents that need to be applied to achieve the desired torque outputs.
Feedback Loop: The control system operates in a closed-loop fashion. The observer estimates the states, the predictive control algorithm uses these estimates to predict future behavior and compute control actions, and the motors execute the control actions. The actual motor outputs are measured, and the discrepancies between the predicted and actual behavior are used to update the observer's estimates, ensuring accuracy and adaptability to changing conditions.
Optimization: The predictive control algorithm optimizes the control actions by minimizing a cost function that includes terms related to desired torque performance, disturbance rejection, and possibly other control objectives. This optimization process ensures that the system operates in an optimal manner while adhering to the constraints of the motors and the control system.
Real-time Implementation: The entire control strategy, including state estimation, torque prediction, and optimization, is implemented in real time. This requires efficient algorithms and fast computation to ensure timely responses to changes in the system's operating conditions.
By combining predictive control techniques with observer-based state estimation and disturbance rejection, the OPC strategy enhances the performance of multi-motor drives by achieving accurate torque control while maintaining stability and robustness in the presence of disturbances.