Youssoufi Touré

On nonlinear distributed parameter model predictive control strategy: On-line calculation time reduction and application to an experimental drying process

It is now recognized that model predictive control (MPC) is an interesting alternative for real-time control of industrial processes. In the meantime, some problems do still remain in progress: for theoretical aspects, the a priori guarantee of the stability and for the practical aspects, the guarantee of sufficient time to solve to optimization problem at each sampled time positions. In this paper, we propose a global method that aims to reduce the on-line calculation time due to the PDE model based optimization task resolution. It is addressed for a particular class of systems not very often studied in this context: systems described by partial differential equations (PDEs) which are, in the present case, nonlinear and parabolic. In order to decrease the computational burden, the nonlinear PDE system is solved off-line. Then, a linearized PDE model around the previous off-line behavior is used to find the optimal variations for the on-line predictive control. The real-time control application given is concerned with a infrared drying process of painting film.

Intelligent mobile manipulator navigation using adaptive neuro-fuzzy systems

The work presented in this paper deals with the problem of autonomous and intelligent navigation of mobile manipulator, where the unavailability of a complete mathematical model of robot systems and uncertainties of sensor data make the used of approximate reasoning to the design of autonomous motion control very attractive. A modular fuzzy navigation method in changing and dynamic unstructured environments has been developed. For a manipulator arm, we apply the robust adaptive fuzzy reactive motion planning developed in [J.B. Mbede, X. Huang, M. Wang, Robust neuro-fuzzy sensor-based motion control among dynamic obstacles for robot manipulators, IEEE Transactions on Fuzzy Systems 11 (2) (2003) 249-261]. But for the vehicle platform, we combine the advantages of probabilistic roadmap as global planner and fuzzy reactive based on idea of elastic band. This fuzzy local planner based on a computational efficient processing scheme maintains a permanent flexible path between two nodes in network generated by a probabilistic roadmap approach. In order to consider the compatibility of stabilization, mobilization and manipulation, we add the input of system stability in vehicle fuzzy navigation so that the mobile manipulator can avoid stably unknown and/or dynamic obstacles. The purpose of an integration of robust controller and modified Elman neural network (MENN) is to deal with uncertainties, which can be translated in the output membership functions of fuzzy systems.

Multivariable model predictive control of a catalytic reverse flow reactor

This paper is devoted to the multiple input multiple output (MIMO) model predictive control (MPC) of a catalytic reverse flow reactor (RFR). The RFR aims to reduce, by catalytic reaction, the amount of volatile organic compounds (VOCs) released in the atmosphere. The peculiarity of this process is that the gas flow inside the reactor is periodically reversed in order to trap the heat released during the reaction. The objective of this paper is to propose a solution to avoid the limitation seen in a previous study for the single input single output (SISO) control case. It was dealing with the impossibility to avoid degradation of the catalytic elements due to the excessive heating induced by the exothermic reaction. In order to overcome this issue, a ratio of the inlet (cold) gas flow is now bypassed into the central zone. This allows introducing a second manipulated variable: the dilution rate. The phenomenological model considered here for the MIMO MPC of the RFR is obtained from a rigorous first principles modeling. The resulting accurate nonlinear partial differential equation (PDE) model can be a drawback for the model based controller implementation. To overcome this issue, we use a MPC strategy previously developed: it combines a two phase approximation of the PDE model in an internal model control (IMC) structure. This strategy allows using a less accurate model and a less time-consuming control algorithm. Finally, efficiency of the control approach is shown in simulation.

Model predictive control of a catalytic reverse flow reactor

This paper deals with the control of a catalytic reverse flow reactor. The aim of this process is to reduce, by catalytic reaction, the amount of volatile organic compounds (VOCs) released into the atmosphere. The peculiarity of this process is that the gas flow inside the reactor is periodically reversed in order to trap the heat released during the reaction. This allows use of the reactor in a heat saving mode. The goal of this work is to provide a model predictive control (MPC) framework to significantly enhance the poor overall performance currently obtained through the actual control strategy. It is directly addressed for the nonlinear parabolic partial differential equations (PDEs) that describe the catalytic reverse flow reactor. In the context of the application of MPC to this particular distributed parameter system, we propose a method that aims to reduce the online computation time needed by the control algorithm. The nonlinear model is linearized around a given operating trajectory to obtain the model to be solved on-line in the approach. MPC strategy combined with internal model control (IMC) structure allows using less accurate and less time-consuming control algorithm. Method efficiency is illustrated in simulation for this single-input-single-output system.

A partial differential equation model predictive control strategy: application to autoclave composite processing

A general framework for a partial differential equation (PDE) model predictive control (MPC) problem is formulated. A first principle model of the system, described by a semi-linear PDE system with boundary control, is employed in a model predictive control (MPC) framework. Here, the aim is to determine, off-line (i.e. without process measurement), the theoretical optimal behavior of the process that will be used during on-line MPC. Input and output constraints are handled in the optimization task using a nonlinear programming method. This strategy is evaluated for the optimization of processing temperatures during the manufacture of thick-sectioned polymer composite laminates. The off-line optimization task consists of determining the optimal temperature profile, otherwise known as the cure cycle. Moreover, for this particular process, the existence of a feasible constrained optimization problem is discussed through the design of a constraint bound.

Infrared drying process of an experimental water painting: Model predictive control

Abstract This article deals with the experimental control of an infrared drying process of a water based epoxy-amine painting. This approach is based on a unidirectional diffusional modeling of infrared drying phenomena where both heat and mass transfers under shrinkage conditions are accounted for. The control problem is concerned with the tracking of any given trajectory for one of the characteristics (i.e., the temperature or the mean water content) during the drying cycle. This is solved using the well-known model predictive control framework where the nonlinear diffusional model is directly used in the control formulation. Experimental results show the efficiency of the trajectory tracking. This method can be extended for more general constrained control problem.

Real-time visual predictive controller for image-based trajectory tracking of a mobile robot

Abstract This paper deals with the design of a real-time controller for trajectory tracking in the image plane. The Image-Based Visual Servoing (IBVS) task is addressed by a visual predictive approach. The trajectory tracking is formulated into a nonlinear optimization problem in the image plane. The unavoidable constraints in experiments are easily taken into account in the design of the predictive control law. The global model, combining the mobile robot and camera model, is used to predict the behavior of the process. The flatness property of this global model is proved in the general case, that is whatever the camera posture. The flat model permits to reduce the computational time by a factor 2. Experiments are performed on a non holonomic mobile robot with a deported perspective camera. Experimental results show the efficiency and the robustness of the real-time control approach. Visibility constraints are added to point out the capability of the control to avoid obstacles.

Visual predictive control for manipulators with catadioptric camera

This paper deals with image based visual servoing (IBSV) by a visual predictive control (VPC) approach. Based on nonlinear model predictive control (NMPC), the visual servoing problem is formulated into a nonlinear constrained minimization problem in the image plane. A global model describing the behavior of the robotic system equipped with the camera is used to predict the evolution of the visual feature on a future horizon. The main interest of this method is the capability to easily take into account different constraints like mechanical limitations and/or visibility constraints. Simulation experiments are performed on a planar manipulator with an omnidirectional camera. Comparisons with the classical control law based on the interaction matrix highlight the efficiency and the robustness of the proposed approach, especially in difficult initial configurations and large displacements.

Multivariable Boundary Control Approach by Internal Model, applied to Irrigation Canals Regulation

Abstract This paper deals with the regulation problem of a class of irrigation canals using the Saint-Venant partial differential equations (pde). The Internal Model Boundary Control (IMBC) approach is used and the multireaches case is considered (several reaches in cascade). Perturbation theory of exponential semigroup used for control synthesis is extended here to nonhomogeneous hyperbolic systems, as the multireaches regulation model is described by hyperbolic pdes. Experimental results (on the Valence experimental canal) are encouraging for an extension to the real case. Additionally a multi-model approach was introduced to allow wider water level variations.

Robust neuro-fuzzy navigation of mobile manipulator among dynamic obstacles

To fit well the needs of autonomous mobile manipulator, two robust adaptive Neuro-Fuzzy motion controllers are developed. The first controller, based on a computational efficient processing scheme for fuzzy reactive navigation, is used to generate the commands for the servo-systems of robot arm so that, locally, it may choose its way to its goal autonomously. The second fuzzy reactive navigation is implemented in mobile platform so that it maintains a permanent flexible path between two nodes in network generated by a probabilistic roadmap approach. In order to consider the compatibility of stabilisation, mobilisation and manipulation, we derive a coordinated fuzzy local planner algorithm so that the mobile manipulator can avoid stably unknown and/or dynamic obstacles The purpose of an integration of robust controller and Modified Elman Neural Network is to deal with unmodeled bounded disturbances and/or unstructured unmodeled dynamics.