Jean-Pierre Kenné

Identification and Real-Time Control of an Electrohydraulic Servo System Based on Nonlinear Backstepping

This paper studies the identification and the real-time control of an electrohydraulic servo system. The control strategy is based on the nonlinear backstepping approach. Emphasis is essentially on the tuning parameters effect and on how it influences the dynamic behavior of the errors. While the backstepping control ensures the global asymptotic stability of the system, the tuning parameters of the controller, nonetheless, do greatly affect the saturation and chattering in the control signal, and consequently, the dynamic errors. In fact, electrohydraulic systems are known to be highly nonlinear and non-differentiable due to many factors, such as leakage, friction, and especially, the fluid flow expression through the servo valve. These nonlinear terms appear in the closed loop dynamic errors. Their values are so large that in the presence of a poor design, they can easily overwhelm the effect of the controller parameters. Backstepping is used here because it is a powerful and robust nonlinear strategy. The experimental results are compared to those obtained with a real-time proportional-integral-derivative (PID) controller, to prove that classic linear controllers fail to achieve a good tracking of the desired output, especially, when the hydraulic actuator operates at the maximum load. Before going through the controller design, the system parameters are identified. Despite the nonlinearity of the system, identification is based on the recursive least squares method. This is done by rewriting the mathematical model of the system in a linear in parameters (LP) form. Finally, the experimental results will show the effectiveness of the proposed approach in terms of guaranteed stability and zero tracking error

Production and preventive maintenance rates control for a manufacturing system: An experimental design approach

In this paper, a multiple-identical-machine manufacturing system with random breakdowns, repairs and preventive maintenance activities is considered. The objective of the control problem is to find the production and the preventive maintenance rates of the machines so as to minimize the total cost of inventory/backlog. By combining analytical formalism and simulation based statistical tools such as experimental design and response surface methodology (RSM), an approximation of the optimal control policy is obtained. A numerical example is presented to illustrate the usefulness of the proposed approach. It is found that the complexity of the experimental design problem remains constant (33 factorial design) when the number of machines increases. Based on the obtained results, the extension of the proposed approach to more complex manufacturing systems is discussed.

Maintenance scheduling and production control of multiple-machine manufacturing systems

This paper deals with the production and preventive maintenance control problem for a multiple-machine manufacturing system. The objective of such a problem is to find the production and preventive maintenance rates for the machines so as to minimize the total cost of inventory/backlog, repair and preventive maintenance. A two-level hierarchical control model is presented, and the structure of the control policy for both identical and nonidentical manufacturing systems is described using parameters, referred to here as input factors. By combining analytical formalism with simulation-based statistical tools such as experimental design and response surface methodology, an approximation of the optimal control policies and values of input factors are determined. The results obtained extend those available in existing literature to cover non-identical machine manufacturing systems. A numerical example and a sensitivity analysis are presented in order to illustrate the robustness of the proposed approach. The extension of the proposed production and preventive maintenance policies to cover large systems (multiple machines, multiple products) is discussed.

Age-dependent production planning and maintenance strategies in unreliable manufacturing systems with lost sale

In this paper, we formulate an analytical model for the joint determination of an optimal age-dependent buffer inventory and preventive maintenance policy in a production environment that is subject to random machine breakdowns. Traditional preventive maintenance policies, such as age and periodic replacements, are usually studied based on simplified and non-realistic assumptions, as well as on the expected costs criterion. Finished goods inventories and the age-dependent likelihood of machine breakdowns are usually not considered. As a result, these policies could significantly extend beyond the anticipated financial incomes of the system, and lead to crises. In order to solve this problem, a more realistic analysis model is proposed in this paper to consider the effects of both preventive maintenance policies and machine age on optimal safety stock levels. Hence, a unified framework is developed, allowing production and preventive maintenance to be jointly considered. We use an age-dependent optimization model based on the minimization of an overall cost function, including inventory holdings, lost sales, preventive and corrective maintenance costs. We provide optimality conditions for the manufacturing systems considered, and use numerical methods to obtain an optimal preventive maintenance policy and the relevant age-dependent threshold level production policy. In this work, this policy is called the multiple threshold levels hedging point policy. We include numerical examples and sensitivity analyses to illustrate the importance and the effectiveness of the proposed methodology. Compared with other available optimal production and maintenance policies, the numerical solution obtained shows that the proposed age-dependent optimal production and maintenance policies significantly reduce the overall cost incurred.

Control of production and corrective maintenance rates in a multiple-machine, multiple-product manufacturing system

This paper presents the analysis of the optimal production control and corrective maintenance planning problem for a failure prone manufacturing system consisting of several identical machines. Machines are subject to breakdowns and repairs and can produce several parts of products. At any given time, each machine can only produce one type of product. The introduction of the corrective maintenance will increase the availability of the production system which guarantees the improvement of the systems productivity if the production planning is well done. The decision variables are the production and the machine repair rates which influence the inventory levels and the system capacity, respectively. The objective of the work is to minimize the cost of surplus and repair activities. A computational algorithm, based on numerical methods, is given for solving the optimal control problem. Finally, a numerical example is presented to illustrate the usefulness of the proposed approach and extensions to more complex manufacturing systems are discussed.

Indirect Adaptive Control of an Electrohydraulic Servo System Based on Nonlinear Backstepping

This paper studies the real-time position control of an electrohydraulic system using indirect adaptive backstepping. Electrohydraulic systems are known to be highly nonlinear and nondifferentiable. Backstepping is used for being a powerful, nonlinear control strategy and for its ability to ensure an asymptotic stability of the controlled system without canceling useful nonlinearities. On the other hand, hydraulic parameters are prone to variations; it is, therefore, useful to employ an adaptive control strategy in order to update the controller with the parameters variation. In such a case, indirect adaptive control is highly recommended, among other adaptive controller types, as it has the benefit of identifying the real system parameters value. Since not much literature is available for the indirect method as applied to the hydraulic systems, because of its implementation complexity, this paper shows how efficiently this method can handle the parameter estimates.

Optimal production control problem in stochastic multiple-product multiple-machine manufacturing systems

Abstract This paper deals with the issue of production control in a manufacturing system with multiple machines which are subject to breakdowns and repairs. The control variables considered are the production rates for different products on the machines. Our objective is to minimize the expected total discounted cost due to the finished good inventories and backlogs. Based on the structure of the hedging point policy, a parameterized near-optimal production policy for a multiple-product manufacturing system is proposed. The analytical formalism is combined with simulation-based statistical tools, such as experimental design and response surface methodology. The aim of such a combination is to provide an approximation of the optimal control policy. In the proposed approach, the parameterized near-optimal control policy is used as an input for the simulation model. For each entry consisting of a combination of parameters, the cost incurred is obtained. The significant effects of the control variables are determined by the experimental design. The relationship between the cost and these input factors is obtained through a response surface model. It is from the obtained relationship that the best values of control factors are determined. Extensive computational experience is reported for two-part-type and five-part-type production systems. Finally, simulation experiments on several examples are concentrated on the sensitivities of the control policy obtained.

Hierarchical control of production and maintenance rates in manufacturing systems

This paper deals with the production and preventive maintenance planning control problem for a multi‐machine flexible manufacturing system (FMS). A two‐level hierarchical control model is developed according to the discrepancy between the time scale of the discounting cost event and one of the machine state processes. The proposed model extends the classical singular perturbation approach by considering age‐dependent machine failure rates and controlling both production and preventive maintenance rates. We replace the stochastic optimal control problem by a deterministic one termed limiting control problem. With this approach, we compute an age‐dependent near‐optimal control policy of the stochastic initial control problem from the optimal solution of the equivalent limiting control problem. A numerical example is used to illustrate the procedure and to show the reduction of the control problem size.

Experimental design in production and maintenance control problem of a single machine, single product manufacturing system

This paper describes an approach to control the production and preventive maintenance rates of a manufacturing system using a computer simulation experiment. The system consists of one machine producing one part type which is subject to failures, repairs and preventive maintenance activities. The production and maintenance planning problem in such a system is a highly complex control problem. It has been shown that, when the transition rates are constant, the hedging point policy is optimal. Owing to the machine age dependent failure distribution, a practical conjecture is made on the dependence of the control variables on the machine age. The hedging point policy is then age dependent. In this paper, we determine the optimal parameters of a modified hedging point policy. The experimental design is used to prove the significance of the control variables compared with the dependent variable or incurred cost. A second-order response surface is fitted in the presence of a random block effect to estimate the ...

Development of a whole arm wearable robotic exoskeleton for rehabilitation and to assist upper limb movements

To assist physically disabled people with impaired upper limb function, we have developed a new 7-DOF exoskeleton-type robot named Motion Assistive Robotic-Exoskeleton for Superior Extremity (ETS-MARSE) to ease daily upper limb movements and to provide effective rehabilitation therapy to the superior extremity. The ETS-MARSE comprises a shoulder motion support part, an elbow and forearm motion support part, and a wrist motion support part. It is designed to be worn on the lateral side of the upper limb in order to provide naturalistic movements of the shoulder (vertical and horizontal flexion/extension and internal/external rotation), elbow (flexion/extension), forearm (pronation/supination), and wrist joint (radial/ulnar deviation and flexion/extension). This paper focuses on the modeling, design, development, and control of the ETS-MARSE. Experiments were carried out with healthy male human subjects in whom trajectory tracking in the form of passive rehabilitation exercises (i.e., pre-programmed trajectories recommended by a therapist/clinician) were carried out. Experimental results show that the ETS-MARSE can efficiently perform passive rehabilitation therapy.