nan Sutrisno

Model predictive control for obstacle avoidance as hybrid systems of small scale helicopter

In this paper, we apply model predictive control for obstacle avoidance of small scale helicopter as a hybrid system such that this UAV can determine the optimal trajectory from initial position to the target position and avoid an obstacle. The hybrid dynamics of this UAV can be interpreted as a piecewise affine (PWA) model that can be transformed into equivalent mixed logical dynamical model (MLD). The PWA model is triggered by state events (coordinate of the UAV for this case). MPC for MLD can be solved using mixed integer quadratic programming (MIQP). We simulate the model and the controller with several shapes of obstacles. From the simulation results, UAV avoids the obstacle optimally using the track generated by MPC.

Dynamic optimization approach for integrated supplier selection and tracking control of single product inventory system with product discount

In this paper, we propose a mathematical model in the form of dynamic/multi-stage optimization to solve an integrated supplier selection problem and tracking control problem of single product inventory system with product discount. The product discount will be stated as a piece-wise linear function. We use dynamic programming to solve this proposed optimization to determine the optimal supplier and the optimal product volume that will be purchased from the optimal supplier for each time period so that the inventory level tracks a reference trajectory given by decision maker with minimal total cost. We give a numerical experiment to evaluate the proposed model. From the result, the optimal supplier was determined for each time period and the inventory level follows the given reference well.

Optimal Strategy for Integrated Dynamic Inventory Control and Supplier Selection in Unknown Environment via Stochastic Dynamic Programming

In this paper, we propose a mathematical model in stochastic dynamic optimization form to determine the optimal strategy for an integrated single product inventory control problem and supplier selection problem where the demand and purchasing cost parameters are random. For each time period, by using the proposed model, we decide the optimal supplier and calculate the optimal product volume purchased from the optimal supplier so that the inventory level will be located at some point as close as possible to the reference point with minimal cost. We use stochastic dynamic programming to solve this problem and give several numerical experiments to evaluate the model. From the results, for each time period, the proposed model was generated the optimal supplier and the inventory level was tracked the reference point well.

Tracking control for hybrid system of unmanned small scale helicopter using predictive control

In this paper, we formulate the hybrid dynamic of unmanned small scale helicopter (Yamaha R-50) as piecewise affine (PWA) model and transform it into equivalent mixed logical dynamic (MLD) model using hybrid system description language (HYSDEL) integrated with hybrid toolbox for MATLAB. This hybrid model is triggered by the location of this unmanned aerial vehicle (UAV) which has two modes. By using the MLD model, we design the controller using model predictive control (MPC) to calculate the optimal control action so that this UAV flights and tracks a trajectory. Finally, we simulate this UAV and its controller to track a rectangular trajectory. From the simulation results, this unmanned small scale helicopter follows given trajectory very well.

Robust model predictive control for inventory system with uncertain demand using linear matrix inequalities

In this paper, we develop an optimal control strategy on inventory systems with uncertain demand. To deal with these uncertainties we use a synthesis of robust model predictive control with linear matrix inequalities. The goal is to minimize the difference between the prediction and the reference trajectory subject to the objective function of each period, based on the input and output constraints. Using standard techniques, the optimization problem that minimizes the difference between the prediction and the reference trajectory, is reduced to a convex optimization problem involving linear matrix inequalities (LMIs). We provide numerical simulations on this system using MATLAB and then observe how robust predictive control models produce optimized strategy at the inventory level. In the simulation results, robust predictive control models provide an optimal strategy for controlling inventory levels with minimum total cost and inventory levels following inventory levels on issues.

Expected value analysis for integrated supplier selection and inventory control of multi-product inventory system with fuzzy cost

The future cost in many industrial problem is obviously uncertain. Then a mathematical analysis for a problem with uncertain cost is needed. In this article, we deals with the fuzzy expected value analysis to solve an integrated supplier selection and supplier selection problem with uncertain cost where the costs uncertainty is approached by a fuzzy variable. We formulate the mathematical model of the problems fuzzy expected value based quadratic optimization with total cost objective function and solve it by using expected value based fuzzy programming. From the numerical examples result performed by the authors, the supplier selection problem was solved i.e. the optimal supplier was selected for each time period where the optimal product volume of all product that should be purchased from each supplier for each time period was determined and the product stock level was controlled as decided by the authors i.e. it was followed the given reference level.

Dynamic supplier selection problem considering full truck load in probabilistic environment

In this paper, we propose a mathematical model in a probabilistic dynamic optimization to solve a dynamic supplier selection problem considering full truck load in probabilistic environment where some parameters are uncertain. We determine the optimal strategy for this problem by using stochastic dynamic programming. We give some numerical experiments to evaluate and analyze the model. From the results, the optimal supplier and the optimal product volume from the optimal supplier were determined for each time period.

Hybrid mathematical model of inventory system with piecewise holding cost and its optimal strategy

In this paper, we formulate a hybrid mathematical model of an inventory system with piecewise holding cost. We write the hybrid model of this system in the piecewise-affine (PWA) form. We use model predictive control method to generate the optimal strategy i.e. the amount of the arriving product shipment so that the inventory level tracks some desired inventory level given by decision maker as close as possible and this inventory system meets the demand with minimum total holding cost. Firstly, we convert the model of the PWA form into mixed logical dynamic (MLD) form and then we apply the model predictive control for hybrid system to this model in the MLD form. We simulate the model with various desired inventory level. From the computational simulation results, we observed that the inventory level follows the given desired inventory level very well.

LQ cooperative distributed MPC to protect two connected buildings from seismic activities

In this paper, we study a linear quadratic (LQ) cooperative distributed MPC based on LQ cooperative game theory and apply it to reduce the vibration of two connected buildings caused by seismic activities. Two connected buildings is a systemwide contains two subsystems connected by a bridge. Dynamics of this systemwide can be written as a composite model that is the combination of decentralized model and interaction model between subsystems. The control purpose is reducing the vibration or regulating the displacements of buildings from their origin. The optimal control action of the LQ cooperative distributed MPC is determined by finding Pareto solution. We simulate this control design to view the response of the system from seismic activities. From the simulation results, the controller reduces the vibration of the system and brings the states to the origin faster than uncontrolled system.