Robust Model Predictive Control Method Research Articles

Predictive metamorphic control

In spite of its easy implementation, ability to handle constraints and nonlinearities, etc., model predictive control (MPC) does have drawbacks including tuning difficulties. In this paper, we propose a refinement to the basic MPC strategy by incorporating a tuning parameter such that one can move smoothly from an existing controller to a new MPC strategy. Each change of this tuning parameter leads to a new stabilising control law, therefore, allowing one to gradually move from an existing control law to a new and better one. For the infinite horizon case without constraints and for the general case with state and input constraints, stability results are established. We also examine the practical applicability of the proposed approach by employing it in the nominal prediction model of the tube-based output feedback robust MPC method. The merits of the proposed method are illustrated by examples.

Parameterized Tube Model Predictive Control

This paper develops a parameterized tube model predictive control (MPC) synthesis method. The most relevant novel feature of our proposal is the online use of a single tractable linear program that optimizes parameterized, Minkowski decomposable, state and control tubes and an associated, fully separable, nonlinear, control policy. The induced control policy enjoys a higher degree of nonlinearity than existing tube MPC and robust MPC using disturbance affine control policy. Our proposal offers greater generality than the state of the art robust MPC methods. It is conjectured, and also established in three cases, that our proposal is equivalent, feasibility-wise, to dynamic programming (DP). It is also shown that, under natural assumptions, our method is computationally efficient while it possesses rather strong system theoretic properties.

Robust MPC Method for BMI Based Wheelchair

In this paper, robust MPC (Model Predictive Control) with adaptive DA converter method for the wheelchair using EEG (Electroencephalogram) based BMI (Brain Machine Interface) is discussed. The method is developed to apply to the obstacle avoidance system of wheelchair. This paper is the 1st stage for the development of the BMI based wheelchair in practical use. The robust MPC method is realized by using the minimax optimization with bounded constraint conditions. Some numerical examples are also included to demonstrate the effectiveness of the proposed methodas the former stage of the real experiments.

Uncertainty Modeling and Robust Control in Urban Traffic

AbstractThe paper investigates the problem of uncertainty modeling and constrained robust control of urban traffic. Linear polytopic approach is used by state-space representations to describe the uncertain network system. In order to handle model mismatches, robust and infinite horizon model predictive control (MPC) method is proposed. The control strategy is an efficient method to reduce travel time and improve homogeneous traffic flow under changing model conditions. Centralized numerical solution has been carried out as a solution of Linear Matrix Inequalities (LMI) by using semidefinite programming (SDP). Closed-loop control results were tested in simulation environment taking alternative model uncertainty levels into account.

Robust supply chain performance via Model Predictive Control

This paper presents a novel robust Model Predictive Control (MPC) method for real-time supply chain optimization under uncertainties. This method optimizes the closed-loop economic performance of supply chain systems and addresses different sources of uncertainties located external to and within the feedback loop. The future system behavior is predicted by a closed-loop model, which includes both the open-loop system model and a controller model described by an optimization problem. The robust MPC formulation involves the solution of a constrained, bi-level stochastic optimization problem, which is transformed into a tractable problem involving a limited number of deterministic conic optimization problems solved reliably using an interior point method. The robust controller is applied to a real industrial multi-echelon supply chain optimization problem, and its performance is shown to reduce stock-outs without excessive inventories.

New Robust Model Predictive Control for Uncertain Systems with Input Constraints Using Relaxation Matrices

In this paper, we propose a new robust model predictive control (MPC) method for time-varying uncertain systems with input constraints. We formulate the problem as a minimization of the worst-case finite-horizon cost function subject to a new sufficient condition for cost monotonicity. The proposed MPC technique uses relaxation matrices to derive a less conservative terminal inequality condition. The relaxation matrices improve feasibility and system performance. The optimization problem is solved by semidefinite programming involving linear matrix inequalities (LMIs). A numerical example shows the effectiveness of the proposed method.

Adaptive receding horizon predictive control for constrained discrete-time linear systems with parameter uncertainties

This paper proposes a discrete-time model predictive control (MPC) scheme combined with an adaptive mechanism. To this end, first, an adaptive parameter estimation algorithm suitable for MPC is proposed, which uses the available input and output signals to estimate the unknown system parameters. It enables the prediction of a monotonically decreasing worst-case estimation error bound over the prediction horizon of MPC. These distinctive features allow for future model improvement to be explicitly considered in MPC. Thus, a less conservative adaptive-type MPC controller can be developed based on the proposed estimation method. Second, we show how the discrete-time adaptive-type state-feedback MPC controller is constructed by combining the on-line parameter estimation scheme with a modified robust MPC method based on the comparison model. The developed MPC controller guarantees feasibility and stability of the closed-loop system theoretically in the presence of input and state constraints. A numerical example is given to demonstrate its effectiveness.

Multi-model predictive control method for nuclear steam generator water level

The dynamics of a nuclear steam generator (SG) is very different according to the power levels and changes as time goes on. Therefore, it is an intractable as well as challenging task to improve the water level control system of the SG. In this paper, a robust model predictive control (RMPC) method is developed for the level control problem. Based on a multi-model framework, a combination of a local nominal model with a polytopic uncertain linear parameter varying (LPV) model is built to approximate the system’s non-linear behavior. The optimization problem solved here is based on a receding horizon scheme involving the linear matrix inequality (LMI) technique. Closed loop stability and constraints satisfaction in the entire operating range are guaranteed by the feasibility of the optimization problem. Finally, simulation results show the effectiveness and the good performance of the proposed method.

Adaptive model predictive control for a class of constrained linear systems based on the comparison model

This paper proposes an adaptive model predictive control (MPC) algorithm for a class of constrained linear systems, which estimates system parameters on-line and produces the control input satisfying input/state constraints for possible parameter estimation errors. The key idea is to combine the robust MPC method based on the comparison model with an adaptive parameter estimation method suitable for MPC. To this end, first, a new parameter update method based on the moving horizon estimation is proposed, which allows to predict an estimation error bound over the prediction horizon. Second, an adaptive MPC algorithm is developed by combining the on-line parameter estimation with an MPC method based on the comparison model, suitably modified to cope with the time-varying case. This method guarantees feasibility and stability of the closed-loop system in the presence of state/input constraints. A numerical example is given to demonstrate its effectiveness.

Robust Model Predictive Control with State Estimation for an Industrial Pressurizer System

Robust model predictive control of an industrial pressurizer is presented in this paper. The physical model of the system is based on first engineering principles and the model parameters have been previously identified from measured data. To satisfy the hard constraints on the state variables and the input even in the presence of disturbances, the so-called single policy robust model predictive control method is applied. The maximal admissible level set, the disturbance invariant set and the terminal sets are determined for the system. Simulation results show that the proposed controller satisfies all the requirements and shows good timedomain behavior.

Intelligent Control Structure for Control of Integrated Wastewater Systems

The paper proposes an approach to design a structure for control of integrated wastewater treatment plant-sewer system under full range of disturbance inputs. The control activities are hierarchically structured into a multilevel-multilayer form. A newly developed robust Model Predictive Control method for uncertain and output constrained dynamic systems is a key control technology employed at Optimising Control Level. This level is further decomposed into control layers operating at different time scales: slow, medium and fast. Appropriate models for control purposes is described Needed data are delivered by measuring system and Extended Kaiman Filter. A Supervisory Control Level is located at a top of the control hierarchy.

Traffic Signal Control Using an LMI-Based MPC Method

This paper first proposes a model of traffic signal control systems for avoiding traffic congestion in urban road networks. The proposed model is described as a linear system with polytopic uncertainties such that changes of traffic situations can be taken into account Next, a robust model predictive control method is applied to the proposed model so as to achieve robust stability and performance under the uncertainties. The proposed control method gives appropriate signal parameters of real-time and network-wide traffic signal systems by online solving a convex optimization problem involving linear matrix inequalities. A numerical example is given to show the effectiveness of the proposed method.

A new robust model predictive control method I: theory and computation

Abstract In this paper, we propose a new robust MPC method that guarantees stability and offset-free set point tracking in the presence of model uncertainty. A min-max optimization problem that explicitly accounts for model uncertainty is used to determine the optimal control action subject to the input and output constraints. The robust regulator uses a tree trajectory to forecast the time-varying model uncertainty. The controller design procedure uses integrators to reject non-zero disturbances and maintain the process at the optimal operating conditions (set points). Constraints may cause offset, which occurs when the set points are unreachable. In the feasible region where constraints are not active, the robust MPC theory we propose achieves offset-free non-zero set point tracking if there exists a control policy that robustly stabilizes all models in the uncertainty set.