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In hyperthermia treatments, cancer tissue is heated to enhance the desired effects of radio- and chemotherapies. A powerful technology for noninvasive feedback-controlled hyperthermia is magnetic-resonance-guided high-intensity focused ultrasound (MR-HIFU), which enables fast and millimeter-accurate heating inside the body. Electronic beam steering allows for volumetric heating, but due to its limited steering range can only be used to treat small tumors. For the treatment of larger tumors, the transducer itself must be mechanically relocated as well. Due to system limitations, however, the admissible transducer positions must be restricted to a finite set that is chosen a priori. Moreover, non-negligible time is needed for transducer relocation, during which no heating is possible. In this paper, we present a mixed-integer model predictive controller that simultaneously optimizes over the power deposition by electronic beam steering - a continuous subproblem - as well as the mechanical transducer motions - a discrete subproblem. By incorporating model knowledge of the tissue’s thermal response and of the transducer carrier motion system into the predictive algorithm, the controller optimizes treatment temperature while respecting temperature and actuation constraints. The performance of the proposed feedback control setup is demonstrated by means of simulation.

High-precision motion industrial systems must satisfy tight performance requirements. Both positioning accuracy and throughput demands are typically achieved through improvements in hardware, thereby raising the bill of materials. A cost saving alternative could be to strive for a reduction in the hardware components needed, in combination with advanced motion control, to still meet the desired specifications. Particularly, in this paper, the possibility is analyzed to allow for resource sharing among several actuators. This results in a switched system, for which we develop a real-time MPC algorithm for optimization of both the input and the switching signals. This implementation applies to a fairly general class of nonlinear systems and uses a novel offset-free formulation in velocity form for LTV prediction models, to realize good tracking performance under the resource sharing constraints. We provide a proof of concept for this MPC solution on a high fidelity model of an industrial SCARA robot, where it is proposed to use a single amplifier to serve two actuators. The MPC solution is compared to heuristically switched LTI controllers, and the potential of the proposed approach is shown in simulations.

In this paper, an offset-free MPC scheme is developed to improve performance and robustness of temperature control in magnetic resonance-guided high-intensity focused ultrasound hyperthermia cancer treatments. The proposed design effectively handles constraints and exploits the body’s thermal behavior to achieve faster and more evenly distributed heating of the tumor leading to improved treatment quality with respect to the methods currently applied in clinics. The proposed scheme incorporates disturbance estimation to remove the steady-state offset resulting from plant-model mismatch, which for the given healthcare application is inevitable in practice due to the large physiological diversity in patients and tumors. Simulation results are presented to demonstrate this algorithm’s capability to improve robust performance.