What is Predictive Control?
Predictive control refers to the concept where the current control action is based on a prediction of the system output a number of time steps into the future. Originated from chemical process engineering, several predictive control methods have emerged, including Model Algorithmic Control, Dynamic Matrix Control, Extended Prediction Self-Adaptive Control, Extended Horizon Adaptive Control, Multistep Multivariable Adaptive Regulator, and the well-known Generalized Predictive Control. Sharing the same common philosophy, the details of these controllers are different from each other due to different choices of cost functions, constraints, and dynamic models. Recently, the predictive control concept has made its way into the aerospace control community.
The main reasons for recent popularity
of predictive control are: (1) the underlying concept is intuitively
appealing, (2) the theory is relatively easy to understand, and
(3) it actually works in practice. From the input-output perspective,
one starts out with an one-step ahead input-output model, turns
it into a multi-step ahead prediction model, specifies a cost
function, and minimizes it to obtain a predictive control law.
Since the dynamic model is in input-output form, and so is the
cost function, it follows naturally that the controller assumes
a dynamic output feedback form. There is no state-space model
to consider, no observer to design, no Riccati equation to solve.
There has been considerable effort to interpret predictive control
from the state-space perspective, but this understanding is far
from complete. Our research focuses on closing this gap, and the
development of a general formulation of predictive control that
subsumes both the input-output and state-space perspectives. We
seek comprehensive answers to questions such as: If the starting
point is a state-space representation in a general coordinate
system, what is the simplest way to justify the existence and
structures of various input-output predictive models? How does
one arrive at an input-output controller if the starting point
of the derivation is a state-space model? Can explicit state-space
model identification be avoided? What is an efficient strategy
to synthesize a predictive controller from input-output data directly
without having to resort to model identification? What is the
role of predictive control in the disturbance rejection problem?
The last question is motivated by the fact that much emphasis
in the predictive control literature has been on the tracking
control problem (chemical process applications), and to a lesser
extent, the feedback stabilization problem. We have made significant
progress in providing comprehensive answers to these questions.
Recent Highlights:
Please click on the following item for additional details:
Phan, M.Q., Juang, J.N., and Eure, K., "Design of Predictive Controllers for Simultaneous Feedback Stabilization and Disturbance Rejection from Disturbance-Corrupted Data," Journal of the Acoustical Society of America, Vol. 105, No. 2, 1999, pp. 972 (abstract). Proceedings of the 137th ASA Meeting and the 2nd EAA Convention, Berlin, Germany.
Phan, M.Q. and Juang, J.N., "Predictive Controllers for Feedback Stabilization" Journal of Guidance, Control, and Dynamics, Vol. 21, No. 5, 1998, pp. 747-753.
Juang, J.-N., and Phan, M. Q., "Deadbeat Predictive Controllers," Journal of the Chinese Society of Mechanical Engineers, Vol. 19, No. 1, Jan.-Feb. 1998, pp. 25-37.
Juang, J.-N., and Phan, M. Q., "Recursive Deadbeat Controller Design," Journal of Guidance, Control, and Dynamics, Vol. 21, No. 4, July-August, 1998, pp. 624-631.
Phan, M.Q., Lim, R.K., and Longman, R.W., "Unifying Input-Output and State-Space Perspectives of Predictive Control," Department of Mechanical and Aerospace Engineering Technical Report No. 3044, Princeton University, Sept. 1998.
Lim, R.K., and Phan, M.Q., "Identification of a Multistep-Ahead Observer and Its Application to Predictive Control," Journal of Guidance, Control, and Dynamics, Vol. 20, No. 6, November-December 1997, pp. 1200-1206.
For additional references on this topic, please refer to List of Publications.