Model predictive control for max-min-plus systems Export

@misc{citeulike:507584, abstract = {Model predictive control (MPC) is a widely used control design method in the process industry. Its main advantage is that it allows the inclusion of constraints on the inputs and outputs. Usually MPC uses linear discrete-time models. We extend MPC to max-min-plus discrete event systems. In general the resulting optimization problems are nonlinear and nonconvex. However, if the state equations are decoupled and if the control objective and the constraints depend monotonically on the states and...}, author = {De Schutter, B. and van den Boom, T.}, citeulike-article-id = {507584}, citeulike-linkout-0 = {http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.7.232}, keywords = {discrete\_event\_system}, posted-at = {2006-02-17 13:13:17}, priority = {2}, title = {Model predictive control for max-min-plus systems}, url = {http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.7.232}, year = {2000} }

TY - GEN ID - citeulike:507584 L3 - citeulike-article-id:507584 N2 - Model predictive control (MPC) is a widely used control design method in the process industry. Its main advantage is that it allows the inclusion of constraints on the inputs and outputs. Usually MPC uses linear discrete-time models. We extend MPC to max-min-plus discrete event systems. In general the resulting optimization problems are nonlinear and nonconvex. However, if the state equations are decoupled and if the control objective and the constraints depend monotonically on the states and... TI - Model predictive control for max-min-plus systems KW - discrete_event_system AU - De Schutter, B AU - van den Boom, T PY - 2000/// UR - http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.7.232 ER -