Generalized predictive control—Part I. The basic algorithm
Automatica (Journal of IFAC)
Worst-case formulations of model predictive control for systems with bounded parameters
Automatica (Journal of IFAC)
Adaptive Optimal Control: The Thinking Man's G.P.C.
Adaptive Optimal Control: The Thinking Man's G.P.C.
Congestion control in high-speed communication networks using the Smith principle
Automatica (Journal of IFAC)
Congestion control as a stochastic control problem with action delays
Automatica (Journal of IFAC)
Survey Constrained model predictive control: Stability and optimality
Automatica (Journal of IFAC)
Brief A probabilistically constrained model predictive controller
Automatica (Journal of IFAC)
Robust constrained predictive control using comparison model
Automatica (Journal of IFAC)
Computer Networks: The International Journal of Computer and Telecommunications Networking
Automation and Remote Control
Improved state estimation of stochastic systems
MATH'07 Proceedings of the 12th WSEAS International Conference on Applied Mathematics
Explicit stochastic predictive control of combustion plants based on Gaussian process models
Automatica (Journal of IFAC)
Automatica (Journal of IFAC)
Improved estimation of state of stochastic systems via invariant embedding technique
WSEAS Transactions on Mathematics
Brief paper: An H∞ approach to the controller design of AQM routers supporting TCP flows
Automatica (Journal of IFAC)
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 3
Nonlinear model predictive formation flight
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Brief paper: Explicit use of probabilistic distributions in linear predictive control
Automatica (Journal of IFAC)
Stochastic MPC with inequality stability constraints
Automatica (Journal of IFAC)
Analysis of future measurement incorporation into unscented predictive motion planning
AIS'11 Proceedings of the Second international conference on Autonomous and intelligent systems
Stochastic receding horizon control with output feedback and bounded controls
Automatica (Journal of IFAC)
Brief paper: Stochastic tube MPC with state estimation
Automatica (Journal of IFAC)
A robust active queue management scheme for network congestion control
Computers and Electrical Engineering
A Probabilistically Robust Path Planning Algorithm for UAVs Using Rapidly-Exploring Random Trees
Journal of Intelligent and Robotic Systems
An integrated robust probing motion planning and control scheme: A tube-based MPC approach
Robotics and Autonomous Systems
A stochastic controller for a scalar linear system with additive Cauchy noise
Automatica (Journal of IFAC)
Hi-index | 22.16 |
Model predictive control (MPC) is of interest because it is one of the few control design methods which preserves standard design variables and yet handles constraints. MPC is normally posed as a full-state feedback control and is implemented in a certainty-equivalence fashion with best estimates of the states being used in place of the exact state. This paper focuses on exploring the inclusion of state estimates and their interaction with constraints. It does this by applying constrained MPC to a system with stochastic disturbances. The stochastic nature of the problem requires re-posing the constraints in a probabilistic form. Using a gaussian assumption, the original problem is approximated by a standard deterministically-constrained MPC problem for the conditional mean process of the state. The state estimates' conditional covariances appear in tightening the constraints. 'Closed-loop covariance' is introduced to reduce the infeasibility and the conservativeness caused by using long-horizon, open-loop prediction covariances. The resulting control law is applied to a telecommunications network traffic control problem as an example.