Omar A. Jasim

The Modular Multilevel Converter (MMC) is the prevailing topology used for high-voltage high-power converters. The energy management of the valves is key for the availability and controllability of the converter station. Moreover, managing the energy ensures the stress on the power electronics is kept within acceptable limits. Conventionally, the valve energy is controlled through the regulation of the mean submodules (SM) capacitor voltage. In this paper, a novel approach of controlling a selected ordered statistic, in particular the statistical median voltage, of the SM capacitor is presented. The simulation results of a full onshoreoffshore HVDC scheme verifies the feasibility of using the median voltage of the SM capacitor voltage as alternative to the mean voltage for MMC VSC energy management.

A control system verification framework is presented for unmanned aerial vehicles using theorem proving. The framework’s aim is to set out a procedure for proving that the mathematically designed control system of the aircraft satisfies robustness requirements to ensure safe performance under varying environmental conditions. Extensive mathematical derivations, which have formerly been carried out manually, are checked for their correctness on a computer. To illustrate the procedures, a higher-order logic interactive theorem-prover and an automated theorem-prover are utilised to formally verify a nonlinear attitude control system of a generic multi-rotor UAV over a stability domain within the dynamical state space of the drone. Further benefits of the procedures are that some of the resulting methods can be implemented onboard the aircraft to detect when its controller breaches its flight envelope limits due to severe weather conditions or actuator/sensor malfunction. Such a detection procedure can be used to advise the remote pilot, or an onboard intelligent agent, to decide on some alterations of the planned flight path or to perform emergency landing.

Quadcopter controllers are in use today and in practice they can often cope well in non adverse weather conditions such as lack of strong sudden gusts of wind around the corner of a building or no frequent demands of travel directions by remote control or a guidance law. Different payloads can alter the boundaries of the stable state space region of a drone, its flight envelop, beyond which its autopilot may not be able to regain stable control of the craft. For fixed gain autopilot controllers, reaching the boundary of the flight envelop can be caused by (1) external disturbance like gusts of wind and turbulence, (2) altered drone mass and its distribution and (3) reduction or misalignment of thrust output in the propulsion system caused ware after multiple uses of the drone. This paper introduces symbolic computation to map out the numerical boundaries of controller tolerances in terms of these three factors that affect the autopilots ability to retain stability of the craft. Proof theoretic methods are developed that can be applied to quadcopter of various nominal parameters.

Control theory can establish properties of systems which hold with all signals within the system and hence cannot be proven by simulation. The most basic of such property is the stability of a control subsystem or the overall system. Other examples are statements on robust control performance in the face of dynamical uncertainties and disturbances in sensing and actuation. Until now these theories were developed and checked for their correctness by control scientist manually using their mathematical knowledge. With the emergence of formal methods, there is now the possibility to derive and prove robust control theory by symbolic computation on computers. There is a demand for this approach from industry for the verification of practical control systems with concrete numerical values where the applicability of a control theorem is specialised to an application with given numerical boundaries of parameter variations. The paper gives an overview of the challenges of the area and illustrates them on a computer-based formal proof of the Small-gain theorem and conclusions are drawn from these initial experiences.