In stochastic optimal control, a key issue is the fact that “solutions” are searched for in terms of “feedback” over available information and, as a consequence, a major potential difficulty is the fact that present control may affect future available information. This is known as the “dual effect” of control. Given a minimal framework (that is, an observation mapping from the product of a cont...

This paper proposes an approach to implement optimal control laws of nonlinear systems in real time. Our methodology does not require solving two-point boundmy value problems online and may not require it off-line either. The optimal control law is learned using the original Sugeno controller (OSC) from a family of optimal trajectories. Wb compare the trajectories generated by the OSC and the t...

33 A model predictive control (MPC) algorithm that differs from prior MPC algorithms has been developed for controlling an uncertain nonlinear system. This algorithm guarantees the resolvability of an associated finite-horizon optimal-control problem in a recedinghorizon implementation. Given a feasible solution to the finite-horizon optimal control problem at an initial time, resolvability imp...

The recognition that optimal control trajectories for batch processes can be highly sensitive to model uncertainties has motivated the development of methods for explicitly addressing robustness during batch processes. This study explores the incorporation of robust performance analysis into open-loop and closed-loop optimal control design. Several types of robust performance objectives are inv...

The problem of reducing the fragility of digital controllers and filters implemented using finite-precision, floating-point arithmetic is considered. Floating-point arithmetic parameter uncertainty is multiplicative, unlike parameter uncertainty resulting from fixedpoint arithmetic. Based on first-order eigenvalue sensitivity analysis, an upper bound on the eigenvalue perturbations is derived. ...

33 A model predictive control (MPC) algorithm that differs from prior MPC algorithms has been developed for controlling an uncertain nonlinear system. This algorithm guarantees the resolvability of an associated finite-horizon optimal-control problem in a recedinghorizon implementation. Given a feasible solution to the finite-horizon optimal control problem at an initial time, resolvability imp...

33 A model predictive control (MPC) algorithm that differs from prior MPC algorithms has been developed for controlling an uncertain nonlinear system. This algorithm guarantees the resolvability of an associated finite-horizon optimal-control problem in a recedinghorizon implementation. Given a feasible solution to the finite-horizon optimal control problem at an initial time, resolvability imp...

conclusions according to the findings of the current study, both open-loop and closed-loop neuromuscular control mechanisms altered in patients with cai. methods it was a case-control study. twenty-two subjects with unilateral cai and 22 healthy subjects stood on a force plate and initiated gait with maximal velocity under 2 conditions: i) planned (initiated gait after hearing the “all set” sig...

The purpose of this paper is to optimize the function of a batch reactor operating in open and closed loops. A simultaneous optimization method is employed. This optimization method provides optimal solution at a finite number of points in time. However, the actual batch reactor operation may deteriorate if the computed optimal input is improperly applied to the process. In the open-loop operat...

Builded on earlier work on the Open-Loop Intermittent Feedback Optimal (OLIFO) controller, a practical continuous time Generalised Predictive Controller (GPC) is introduced. On-line integration of the model equation is used for the prediction. Only two parameters of a bounded and smooth open-loop control structure have to be opti-mised each time instant. An intermittent receding horizon strateg...