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We present a technique to compute the explicit state-feedback solution to both the xnite and inxnite horizon linear quadratic optimal control problem subject to state and input constraints. We show that this closed form solution is piecewise linear and continuous. As a practical consequence of the result, constrained linear quadratic regulation becomes(More)
Control based on on-line optimization, popularly known as model predictive control (MPC), has long been recognized as the winning alternative for constrained systems. The main limitation of MPC is, however, its on-line computational complexity. For discrete-time linear time-invariant systems with constraints on inputs and states, we develop an algorithm to(More)
For people with type 1 diabetes, automatic controllers aim to maintain the blood glucose concentration within the desired range of 60–120 mg/dL by infusing the appropriate amount of insulin in the presence of meal and exercise disturbances. Blood glucose concentration outside the desired range can be harmful to an individual’s health but concentration below(More)
The paper presents a decomposition based global optimization approach to bilevel linear and quadratic programming problems. By replacing the inner problem by its corresponding KKT optimality conditions, the problem is transformed to a single yet non-convex, due to the complementarity condition, mathematical program. Based on the primal-dual global(More)
In this paper, we overview recent advances towards the integration of process design, process control and process oper-ability in separation and reaction/separation systems that were developed within our group at Imperial College. Based on novel mixed integer dynamic optimization algorithms, a simultaneous strategy is presented featuring high fidelity(More)