Timothy Broomhead

Robust periodic economic MPC for linear systems

Economic Model Predictive Control differs from conventional tracking model predictive control by directly addressing a plants economic cost as the stage cost, consequently leading to better economic performance. This paper extends current economic model predictive control theory to linear time-invariant systems with periodic disturbances and cost functions, under mild assumptions. To ensure an increased region of attraction and to continuously guarantee feasibility of the controller despite changing economic conditions, a periodic terminal condition is used in place of terminal constraints. The approach draws on constraint tightening techniques in order to guarantee robust satisfaction of constraints as well as convergence of the controller. A Lyapunov based approach is used to show stability of the proposed controller and characterise a region about the optimal trajectory to which the system converges.

Counter availability and characteristics for feed-forward based synchronization

The availability of hardware counters in computers is essential both to the applications in charge of timekeeping, and those in need of accurate timestamping. Newer counters are now supported by open source operating systems, but the access interfaces are unnecessarily restricted, and in particular fail to satisfy the needs of feed-forward based synchronization algorithms. In this paper we present modifications to the Linux and FreeBSD kernels to enable any application to access all available counters in an unrestricted way, and then evaluate their stability, latency and robustness to stress. We demonstrate how the feed-forward based RADclock can, through this interface, make use of any of several counters, and achieve the same microsecond synchronization with each.

Economic Model Predictive Control and Applications for Diesel Generators

When developing control systems for diesel generators, tuning of the controller’s parameters to achieve acceptable performance is a significant challenge, particularly while satisfying input, emission, and safety constraints in the face of unknown system disturbances. Robust economic model predictive control (EMPC) can simplify this process by directly addressing the generator’s objectives, while systematically handling constraints in a robust way. This paper details how robust EMPC can be implemented as the control solution for diesel generators. To illustrate the process, two distinct generator applications are considered. The first application is a power tracking diesel generator, operating under emissions constraints. Such an application is found in series hybrid electric vehicles. The second application concerns diesel generators onboard submarines. In this application, engine speed and exhaust temperatures must be kept constant, despite significant system disturbances. An experimental study highlights the effectiveness of the EMPC as a solution for both applications.

Robust stable economic MPC with applications in engine control

Economic Model Predictive Controllers have shown to improve a plants economic performance using state dependant economic stage costs. Recent extensions have provided continual feasibility guarantees, despite changes in economic parameters, however, perfect plant models have been assumed. This assumption is invalid in practise due to modelling errors or un-modelled disturbances and can therefore lead to infeasibility of the optimisation problem. This paper proposes a robust economic model predictive controller, which takes advantage of constraint tightening techniques to guarantee feasibility despite modelling errors. Input-to-state stability is proven using a Lyapunov function. The advantages of this method are highlighted against alternative control structures in the application of power tracking for diesel engines in series hybrid type applications.

A Robust Model Predictive Control Framework for Diesel Generators

Abstract A constraint tightened linear-time-varying MPC framework is proposed with applications in power tracking for variable and fixed speed generators. Current constraint tightening approaches are extended to allow for practical applications where future system representations are unknown. The resulting control structure is shown to be robustly feasible under given conditions. Knowledge about the geometry of system constraints is exploited to obtain a computationally efficient method of computing tightened sets online. A simulation study is presented demonstrating the ability of the controller to handle modelling error and demonstrate tracking of a commanded power profile.