Explicit predictive control laws with a nonlinear constraints handling mechanism

International audienceThis paper is dealing with the receding horizon optimal control techniques having as main goal the reduction of the computational effort inherent to the use of on-line optimization routines. The off-line construction of the explicit solution for the associated multiparametric optimization problems is advocated with a special interest in the presence of nonlinearities in the constraints description. The proposed approach is a geometrical one, based on the topology of the feasible domain. The resulting piecewise linear state feedback control law has to accept a certain degree of suboptimality, as it is the case for local linearizations or decompositions over families of parametric functions. In the presented techniques, this is directly related to the distribution of the extreme points on the frontier of the feasible domain

Explicit predictive control laws. On the geometry of feasible domains and the presence of nonlinearities.

International audienceThis paper is dealing with the receding horizon optimal control techniques having as main goal the reduction of the computational effort inherent to the use of on-line optimization routines. The off-line construction of the explicit solution for the associated multiparametric optimization problems is advocated with a special interest in the presence of nonlinearities in the constraints description. The proposed approach is a geometrical one, based on the topology of the feasible domain. The resulting piecewise linear state feedback control law has to accept a certain degree of suboptimality, as it is the case for local linearizations or decompositions over families of parametric functions. In the presented techniques, this is directly related to the distribution of the extreme points on the frontier of the feasible domain

Distributed model predictive control of leader-follower systems using an interior point method with efficient computations

Standard model predictive control strategies imply the online computation of control inputs at each sampling instance, which traditionally limits this type of control scheme to systems with slow dynamics. This paper focuses on distributed model predictive control for large-scale systems comprised of interacting linear subsystems, where the online computations required for the control input can be distributed amongst them. A model predictive controller based on a distributed interior point method is derived, for which every subsystem in the network can compute stabilizing control inputs using distributed computations. We introduce local terminal sets and cost functions, which together satisfy distributed invariance conditions for the whole system, that guarantees stability of the closed-loop interconnected system. We show that the synthesis of both terminal sets and terminal cost functions can be done in a distributed framework.Comment: 8 pages, Partially Accepted in the Proceedings of the 2013 American Control Conferenc

Sensor-fault tolerance using robust MPC with set-based state estimation and active fault isolation

In this paper, a sensor fault-tolerant control (FTC) scheme using robust model predictive control (MPC) and set theoretic fault detection and isolation (FDI) is proposed. The MPC controller is used to both robustly control the plant and actively guarantee fault isolation (FI). In this scheme, fault detection (FD) is passive by interval observers, while fault isolation (FI) is active by MPC. The advantage of the proposed approach consists in using MPC to actively decouple the effect of sensor faults on the outputs such that one output component only corresponds to one sensor fault in terms of FI, which can utilize the feature of sensor faults for FI. A numerical example is used to illustrate the effectiveness of the proposed scheme.Postprint (author’s final draft

Robust MPC for actuator-fault tolerance using set-based passive fault detection and active fault isolation

In this paper, an actuator fault-tolerant control (FTC) scheme is proposed, which is based on tube-based model predictive control (MPC) and set-theoretic fault detection and isolation (FDI). As a robust MPC technique, tube-based MPC, can effectively deal with system constraints and uncertainties with relatively low computational complexity. Set-based FDI can robustly detect and isolate actuator faults. Here, fault detection (FD) is passive by invariant sets, while fault isolation (FI) is active by tubes. Using the constraint-handling ability of MPC controllers, an active FI approach is implemented. A numerical example illustrates the effectiveness of the proposed approach.Postprint (author’s final draft

Development of a Data-Driven Soft Sensor for Multivariate Chemical Processes Using Concordance Correlation Coefficient Subsets Integrated with Parallel Inverse-Free Extreme Learning Machine

Nonlinearity, complexity, and technological limitations are causes of troublesome measurements in multivariate chemical processes. In order to deal with these problems, a soft sensor based on concordance correlation coefficient subsets integrated with parallel inverse-free extreme learning machine (CCCS-PIFELM) is proposed for multivariate chemical processes. In comparison to the forward propagation architecture of neural network with a single hidden layer, i.e., a traditional extreme learning machine (ELM), the CCCS-PIFELM approach has two notable points. Firstly, there are two subsets obtained through the concordance correlation coefficient (CCC) values between input and output variables. Hence, impacts of input variables on output variables can be assessed. Secondly, an inverse-free algorithm is used to reduce the computational load. In the evaluation of the prediction performance, the Tennessee Eastman (TE) benchmark process is employed as a case study to develop the CCCS-PIFELM approach for predicting product compositions. According to the simulation results, the proposed CCCS-PIFELM approach can obtain higher prediction accuracy compared to traditional approaches

Receding horizon climate control in metal mine extraction rooms

International audienceThis papers proposes a novel climate control strategy for mine extraction rooms based on the receding horizon optimal control scheme. Being a model-based procedure, the development of a pertinent prediction model is one of the keystones. According to recent technological advances, we consider that distributed measurements are available and provided by a wireless network. An enhanced modeling approach, based on stratification and sigmoid description of concentrations in the extraction rooms, is then proposed and allows for an optimal use of information provided by the wireless sensor network (WSN). The complexity of the resulting model, due to the nonlinearities, different time scales and time-delays, is handled by using an on-line shape prediction, included in the design of an optimal sequence of control actions over a finite horizon. Physical and communication constraints are successfully handled at the design stage and the resulting closed-loop system is robust with respect to variations in the pollutant dynamics

Improved actuator-fault detection and isolation strategy using interval observers and invariant sets

Trabajo presentado al 19th IFAC World Congress celebrado del 24 al 29 de agosto de 2014 en Cape Town (Sudafrica).In this paper, an improved algorithm for an actuator fault detection and isolation (FDI) framework using a bank of interval observers previously proposed by the authors is presented, where each interval observer matches one possible system mode. Under a set of FDI conditions, this new algorithm can accurately detect and isolate the considered actuator faults. The advantage of this new algorithm consists in that it extends the effectiveness of this FDI framework. At the end, a circuit example is used to illustrate the effectiveness of this approach.The work was supported by the DGR of Generalitat de Catalunya (AGAUR Doctorat Industrial 2013-DI-041), European Commission through contract i-Sense (FP7-ICT-2009-6-270428), and China Scholarship Council (File No.2011629170).Peer Reviewe

Interpolating Control with Periodic Invariant Sets

This paper presents a novel low-complexity interpolating control scheme involving periodic invariance or vertex reachability of target sets for the constrained control of LTI systems. Periodic invariance relaxes the strict one-step positively invariant set notion, by allowing the state trajectory to leave the set temporarily but return into the set in a finite number of steps. To reduce the complexity of the representation of the required controllable invariant set, a periodic invariant set is employed. This set should be defined within the controllable stabilising region, which is considered unknown during the design process. Since periodic invariant sets are not traditional invariant sets, a reachability problem can be solved off-line for each vertex of the outer set to provide an admissible control sequence that steers the system state back into the original target set after a finite number of steps. This work develops a periodic interpolating control (pIC) scheme between such periodic invariant sets and a maximal admissible inner set by means of an inexpensive linear programming problem, solved on-line at the beginning of each periodic control sequence. Theorems on recursive feasibility and asymptotic stability of the pIC are given. A numerical example demonstrates that pIC provides similar performance compared to more expensive optimization-based schemes previously proposed in the literature, though it employs a naive representation of the controllable invariant set