Model Predictive Control MCQs

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1. Model Predictive Control (MPC) is based on:
(A) Fixed gain feedback control
(B) Predicting future system behavior and optimizing control actions
(C) Frequency response analysis
(D) State-space representation

2. In MPC, the prediction model is used to:
(A) Forecast the future behavior of the system
(B) Design state observers
(C) Analyze frequency response
(D) Implement PID control

3. The primary objective of MPC is to:
(A) Implement adaptive control
(B) Design fixed controllers
(C) Analyze the system’s stability
(D) Minimize a cost function over a finite prediction horizon

4. In MPC, the “cost function” typically includes:
(A) A term for tracking error and a term for control effort
(B) Only tracking error
(C) Only control effort
(D) System frequency response

5. The “horizon” in Model Predictive Control refers to:
(A) The future time period over which predictions are made and optimization is performed
(B) The time period over which the system is observed
(C) The time needed to solve the optimization problem
(D) The time interval between control actions

6. “Constraint handling” in MPC refers to:
(A) Analyzing the system’s poles and zeros
(B) Designing controllers for specific conditions
(C) Managing system constraints like input and state limits within the optimization process
(D) Implementing frequency response analysis

7. The “state-space model” used in MPC is used to:
(A) Predict future system states based on current states and inputs
(B) Design fixed controllers
(C) Analyze system stability
(D) Implement PID control

8. “Open-loop MPC” refers to:
(A) MPC using a linear model
(B) MPC with feedback correction
(C) MPC with adaptive parameters
(D) MPC where the control actions are calculated without feedback correction

9. “Closed-loop MPC” involves:
(A) Designing fixed controllers
(B) Using feedback to update the control actions based on actual system performance
(C) Analyzing frequency response
(D) Implementing state-space models

10. The “control horizon” in MPC refers to:
(A) The future time period for predictions
(B) The time period over which the control actions are optimized
(C) The time period for observing system performance
(D) The duration of the optimization problem

11. The term “receding horizon” in MPC means:
(A) The horizon remains fixed throughout the control process
(B) The optimization is performed repeatedly with the horizon moving forward in time
(C) The system’s response is evaluated at a fixed time
(D) The optimization is performed only once

12. In MPC, the “prediction model” can be:
(A) Linear or nonlinear depending on the system’s dynamics
(B) Only linear
(C) Only nonlinear
(D) Fixed and not dependent on the system’s dynamics

13. “Reference Tracking” in MPC aims to:
(A) Minimize the deviation of the system output from a desired reference
(B) Design fixed controllers
(C) Analyze the system’s stability
(D) Implement PID control

14. “Input Constraints” in MPC are:
(A) Design parameters of the model
(B) Limits on the system outputs
(C) Limits on the state variables
(D) Limits on the control inputs to the system

15. “State Constraints” in MPC refer to:
(A) Design parameters of the model
(B) Limits on the control inputs
(C) Limits on the system outputs
(D) Limits on the state variables of the system

16. “Performance Index” in MPC is used to:
(A) Analyze system poles and zeros
(B) Evaluate the overall performance of the control strategy based on the cost function
(C) Implement frequency response analysis
(D) Design fixed controllers

17. “Disturbance Rejection” in MPC involves:
(A) Analyzing the system’s frequency response
(B) Designing the controller to handle and mitigate the effects of external disturbances
(C) Implementing state feedback
(D) Designing fixed gain controllers

18. The “prediction horizon” is typically:
(A) Unrelated to the control horizon
(B) Shorter than the control horizon
(C) Equal to the control horizon
(D) Longer than the control horizon

19. “MPC with Robustness” involves:
(A) Analyzing system stability
(B) Designing controllers for specific known conditions
(C) Designing the controller to perform well despite model inaccuracies and uncertainties
(D) Implementing PID control

20. “Linear MPC” is used when:
(A) The control actions are not optimized
(B) The system exhibits strong nonlinear behavior
(C) The model includes significant uncertainties
(D) The system can be accurately modeled with linear dynamics

21. “Nonlinear MPC” is applied when:
(A) The system’s dynamics are nonlinear and cannot be accurately represented by a linear model
(B) The system is linear
(C) The model is fixed and does not change
(D) The control actions are not optimized

22. The “computational burden” in MPC refers to:
(A) The number of constraints
(B) The physical load on the system
(C) The amount of computational resources required to solve the optimization problem
(D) The accuracy of the model

23. In “Explicit MPC,” the control law is:
(A) Computed in real-time
(B) Precomputed offline and implemented online based on the current state
(C) Based on adaptive control
(D) Implemented using frequency response

24. “Stochastic MPC” deals with:
(A) Linear time-invariant systems
(B) Fixed gain controllers
(C) Systems where uncertainty is modeled as stochastic processes
(D) Deterministic systems

25. “Economic MPC” focuses on:
(A) Analyzing frequency response
(B) Designing fixed controllers
(C) Optimizing the economic performance of the system by minimizing a cost function related to economic criteria
(D) Implementing PID control

26. “MPC in Discrete-Time” involves:
(A) Implementing fixed controllers
(B) Designing continuous-time controllers
(C) Applying MPC to systems sampled at discrete intervals
(D) Analyzing frequency response

27. The “constraint handling” in MPC is essential for:
(A) Analyzing system stability
(B) Designing fixed gain controllers
(C) Ensuring that control inputs and state variables stay within physically feasible limits
(D) Implementing adaptive control

28. “Multi-Objective MPC” involves:
(A) Optimizing multiple performance criteria simultaneously
(B) Designing single-objective controllers
(C) Analyzing system stability
(D) Implementing fixed gain controllers

29. In “MPC with Constraints,” the optimization problem includes:
(A) Only constraints on control inputs
(B) Only constraints on state variables
(C) Constraints on control inputs, state variables, and outputs
(D) No constraints

30. “Robust MPC” aims to:
(A) Analyze the system’s frequency response
(B) Design controllers for specific known conditions
(C) Ensure that the control performance is maintained despite model uncertainties and external disturbances
(D) Implement fixed gain controllers

31. “Adaptive MPC” adjusts:
(A) The physical structure of the system
(B) The control model and parameters in real-time based on observed system behavior
(C) The input constraints
(D) The system’s frequency response

32. The “terminal cost” in MPC refers to:
(A) The cost function evaluated at the end of the prediction horizon
(B) The initial cost of the system
(C) The cost related to system outputs
(D) The cost of control inputs

33. “MPC for Linear Time-Invariant (LTI) Systems” assumes:
(A) The system dynamics do not change over time
(B) The system dynamics vary with time
(C) The system has nonlinear characteristics
(D) The system exhibits chaotic behavior

34. “Output Constraints” in MPC are:
(A) Limits imposed on the system outputs to ensure safe operation
(B) Limits on control inputs
(C) Limits on state variables
(D) Design parameters of the model

35. “Feasibility” in the context of MPC means:
(A) Implementing frequency response analysis
(B) Designing fixed controllers
(C) Analyzing system poles and zeros
(D) The existence of a solution that satisfies all constraints and optimizes the cost function

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