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In the context of dynamic optimization, Sequential Quadratic Programming (SQP) methods, which guarantee feasibility of the solution at convergence, or feasible SQP methods, which guarantee feasibility throughout the optimization, have shown to be efficient in the solution of large-scale constrained NLPs.
PHR requires an acceptance/rejection mechanism along a bidirectional walk to guarantee feasibility.
It is then refined to guarantee feasibility of the parameterized optimization.
The constraints originating from the use of a visual servoing controller are incorporated into the trajectory learning phase, to guarantee feasibility of the generated plan for task execution.
While the proposed approach works to guarantee feasibility for the single-constraint case, early sub-optimal convergence is noted for cases with multiple constraints.
The nominal operating point is chosen to avoid high sensitivity to disturbances and to guarantee feasibility when operation and design parameters are uncertain.
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We also provide a trajectory planning pipeline which explicitly accounts for the force/torque input constraint and therefore guarantees feasibility.
The optimization model considers a set of slack variables for guaranteeing feasibility.
The proposed control scheme guarantees feasibility under the presence of uncertainties by explicitly incorporating in the design stage a feasibility test and then by adapting on-line the origin according to the current disturbance realizations.
The proposed scheme shows a number of advantages such as a systematic and automatic generation of control recipes, design rationales usable throughout the lifecycle of batch process operation, a guaranteed feasibility of control recipes, improved modularity and transparency of auxiliary operations and an easy integration with a simulator.
This chapter investigates strategies for approaching the energy refurbishment of historic buildings, by proposing, beyond a general methodology aimed at guaranteeing feasibility of the design, its application to two cases studies in the context of South Italy, in a typical Mediterranean climate according to the Köppen classification.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com