Parameter optimization of H∞ controller designed for tumor growth in the light of physiological aspects

According to the fact that cancer diseases are leading causes of death all around the world, development of cancer fighting therapies is necessary. Beside the medical knowledge, there is an extra need for engineering approach to solve this complex problem. The aim of this paper is to design controller for tumor growth under angiogenic inhibition, which on the one hand minimizes the input signal as far as possible (in order to have less side effects and greater cost-effectiveness) and on the other hand results in appropriately low tumor volume. Since the model contains uncertainties and measurement noise, the controller was designed using modern robust control methodology. Choosing of the ideal system and the weighting functions were done in the light of physiological aspects

Potential Benefits of Discrete-Time Controllerbased Treatments over Protocol-based Cancer Therapies

In medical practice, the effectiveness of fighting cancer is not only determined by the composition of the used drug, but determ ined by the administration method as well. As a result, having drugs with a suitable action profile is just a promising beginning, but without appropriate delivery method s , the therapy still can be ineffective. Finding the optimal biologic dose is an empir ical process in medical practice; however, using controllers, an automated optimal administration can be determined . In this paper , we evaluate the effectiveness of different drug delivery protocols; using in silico simulations (like bolus dose s, low - dose metron omic regimen and continuous infusion therapy ). In addition, we compare these results with discrete - time controller - based treatments containing state feedback, setpoint control, actual state observer and load estimation

Second-order and implicit methods in numerical integration improve tracking performance of the closed-loop inverse kinematics algorithm

A general approach to solve the inverse kinematics problem of series manipulators, i.e. finding the required joint motions for the desired end effector motions, is based on the linear approximation of the forward kinematics map and discretization of the continuous problem. Due to the linearization, first velocities are calculated, so numerical integration needs to be done to get the joint variables. This general solution is just a numerical approximation, thus improving the tracking performance of the inverse kinematics algorithm is of great importance. The application of several numerical integration techniques (implicit Euler, explicit trapezoid, implicit trapezoid) is analyzed, and a fix point iteration is given that can be used to calculate implicit solutions. The tracking performance of the spatial inverse positioning problem of a spatial manipulator is analyzed by checking the tracking error in the desired direction (i.e. along the derivative of the desired end effector path) and in the plane perpendicular to the desired direction. The application of the explicit and implicit trapezoid methods yielded much better tracking performance in the directions orthogonal to the desired direction when the end effector had to track a linear path, while the tracking performance in the desired direction was similar for all the methods. Simulations showed that the application of implicit and second-order methods in the numerical integration may greatly improve the tracking performance of the closed-loop inverse kinematics algorithm

Optimization of impulsive discrete-time tumor chemotherapy

Cancer therapies, like chemotherapy are generally based on heuristic approaches and expert knowledge. Introducing mathematical and engineering methods into the therapy design process has great potentials in therapy optimization. We investigate the application of a discrete time, impulsive therapy generation algorithm for a model that describes living tumor and dead tumor volume dynamics, drug level dynamics, using mixed-order pharmacokinetics and input saturation. We propose an algorithm that calculates low doses of injections that are required to reach or approximate the best results that can be achieved by the application of the drug. The algorithm is tested based on virtual patients (mice) whose parameters are identified based on measurement from experiments with pegylated liposomal doxorubicin as cytotoxic agent and breast cancer as tumor. The algorithm tested in silico shows much better performance than the protocol used in the experiments