Echtzeitoptimierung für Ausweichtrajektorien : mittels der Sensitivitätsanalyse eines parametergestörten nichtlinearen Optimierungsproblems

In this thesis an approach for the real-time computation of trajectories and controls for collision avoidance in cars traffic is presented. The approach handles the full complexity of a nonlinear model of the host car's dynamics and keeps it stable during the maneuver. A switching point optimization technique reduces the number of variables and constraints in the desired nonlinear optimization problem drastically. Via parametric sensitivity analysis of perturbed nonlinear optimization problems an approach for real-time computation of near-optimal trajectories is developed. This approach leads to the possibility of a rather simple implementation in a car but is still generic enough to cover numerous different collision scenarios. Examples from collision avoidance demonstrate the capabilities of the desired method

Optimalitätsbedingungen, parametrische Sensitivitätsanalyse und Echtzeitanpassung optimaler Regel- und Schätzverfahren

This dissertation covers the derivation of necessary and sufficient optimality condition for linear quadratic regulator (LQR) problems. Based on this, parametric sensitivity methods and real time adaption techniques from nonlinear optimization theory are applied to LQR problems. In addition to that, through dual theory, these methods are extended to linear quadratic estimator problems as well as combined regulator-estimator problems. All derived adaption techniques are numerically tested for two examples

Modellbasierte optimale Mehrgrößenregelung eines aufgeladenen Dieselmotors mittels Methoden der nichtlinearen Optimierung

Considering the increasingly restrictive emission standards for diesel engines, the automotive industry is in need of innovative ideas and solutions for engine control problems. This work addresses the challenge of determining optimal controllers for multiple, mutually dependent actuators in the engine gas system. The problem is posed as a parameter identification problem for a system of ordinary differential equations that model the gas system against measurements of an actual engine. The standard approach is to apply an NLP solver to a shooting technique, however this gives rise to several local minima when applied to large data sets. An alternative approach is studied, which performs the integration of the discretised ode system within the optimisation. The resulting high-dimensional optimisation problem is solved by a sparse NLP solver. Based on the nonlinear dynamic model a linear-quadratic regulator is designed and validated with a rapid prototyping system on a test vehicle