57 citations · 142 across the 25 of their papers we have counts for
32 papers
Data-driven robust MPC of tiltwing VTOL aircraft
Martin Doff-Sotta, Mark Cannon, Marko Bacic
This paper investigates robust tube-based Model Predictive Control (MPC) of a tiltwing Vertical Take-Off and Landing (VTOL) aircraft subject to wind disturbances and model uncertai…
Learning-based Rigid Tube Model Predictive Control
Yulong Gao, Shuhao Yan, Jian Zhou +3
This paper is concerned with model predictive control (MPC) of discrete-time linear systems subject to bounded additive disturbance and mixed constraints on the state and input, wh…
Robust trajectory optimisation for transitions of tiltwing VTOL aircraft
Martin Doff-Sotta, Mark Cannon, Marko Bacic
We propose a method to generate robust and optimal trajectories for the transition of a tiltwing Vertical Take-Off and Landing (VTOL) aircraft leveraging concepts from convex optim…
A successive convexification approach for robust receding horizon control
Yana Lishkova, Mark Cannon
A novel robust nonlinear model predictive control strategy is proposed for systems with nonlinear dynamics and convex state and control constraints. Using a sequential convex appro…
Adaptive robust predictive control with sample-based persistent excitation
Xiaonan Lu, Mark Cannon
We propose a robust adaptive Model Predictive Control (MPC) strategy with online set-based estimation for constrained linear systems with unknown parameters and bounded disturbance…
Robust adaptive model predictive control with persistent excitation conditions
Xiaonan Lu, Mark Cannon
For constrained linear systems with bounded disturbances and parametric uncertainty, we propose a robust adaptive model predictive control strategy with online parameter estimation…
Safeguarded Anderson acceleration for parametric nonexpansive operators
Michael Garstka, Mark Cannon, Paul Goulart
This paper describes the design of a safeguarding scheme for Anderson acceleration to improve its practical performance and stability when used for first-order optimisation methods…
A Multirate Variational Approach to Nonlinear MPC
Yana Lishkova, Mark Cannon, Sina Ober-Blöbaum
A multirate nonlinear model predictive control (NMPC) strategy is proposed for systems with dynamics and control inputs evolving on different timescales. The proposed multirate for…
Fast optimal trajectory generation for a tiltwing VTOL aircraft with application to urban air mobility
Martin Doff-Sotta, Mark Cannon, Marko Bacic
We solve the minimum-thrust optimal trajectory generation problem for the transition of a tiltwing Vertical Take-Off and Landing (VTOL) aircraft using convex optimisation. The meth…
Scenario Model Predictive Control for Data-based Energy Management in Plug-in Hybrid Electric Vehicles
Sebastian East, Mark Cannon
One of the major limitations of optimization-based strategies for allocating the power flow in hybrid powertrains is that they rely on predictions of future power demand. These pre…
Predictive energy management for hybrid electric aircraft propulsion systems
Martin Doff-Sotta, Mark Cannon, Marko Bacic
We present a Model Predictive Control (MPC) algorithm for energy management in aircraft with hybrid electric propulsion systems consisting of gas turbine and electric motor compone…
A multirate variational approach to simulation and optimal control for flexible spacecraft
Yana Lishkova, Sina Ober-Blöbaum, Mark Cannon +1
We propose an optimal control method for simultaneous slewing and vibration control of flexible spacecraft. Considering dynamics on different time scales, the optimal control probl…
Stochastic output feedback MPC with intermittent observations
Shuhao Yan, Mark Cannon, Paul J. Goulart
This paper designs a model predictive control (MPC) law for constrained linear systems with stochastic additive disturbances and noisy measurements, minimising a discounted cost su…
Stochastic MPC with Dynamic Feedback Gain Selection and Discounted Probabilistic Constraints
Shuhao Yan, Paul J. Goulart, Mark Cannon
This paper considers linear discrete-time systems with additive disturbances, and designs a Model Predictive Control (MPC) law incorporating a dynamic feedback gain to minimise a q…
Output feedback stochastic MPC with packet losses
Shuhao Yan, Mark Cannon, Paul Goulart
The paper considers constrained linear systems with stochastic additive disturbances and noisy measurements transmitted over a lossy communication channel. We propose a model predi…
Optimal energy management for hybrid electric aircraft
Martin Doff-Sotta, Mark Cannon, Marko Bacic
A convex formulation is proposed for optimal energy management in aircraft with hybrid propulsion systems consisting of gas turbine and electric motor components. By combining a po…
Optimal Power Allocation in Battery/Supercapacitor Electric Vehicles using Convex Optimization
Sebastian East, Mark Cannon
This paper presents a framework for optimizing the power allocation between a battery and supercapacitor in an electric vehicle energy storage system. A convex optimal control form…
Infinite-Horizon Differentiable Model Predictive Control
Sebastian East, Marco Gallieri, Jonathan Masci +2
This paper proposes a differentiable linear quadratic Model Predictive Control (MPC) framework for safe imitation learning. The infinite-horizon cost is enforced using a terminal c…
A clique graph based merging strategy for decomposable SDPs
Michael Garstka, Mark Cannon, Paul Goulart
Chordal decomposition techniques are used to reduce large structured positive semidefinite matrix constraints in semidefinite programs (SDPs). The resulting equivalent problem cont…
Robust Adaptive Model Predictive Control: Performance and Parameter Estimation
Xiaonan Lu, Mark Cannon, Denis Koksal-Rivet
For systems with uncertain linear models, bounded additive disturbances and state and control constraints, a robust model predictive control algorithm incorporating online model ad…
Convergence of stochastic nonlinear systems and implications for Stochastic Model Predictive Control
Diego Muñoz-Carpintero, Mark Cannon
The stability of stochastic Model Predictive Control (MPC) subject to additive disturbances is often demonstrated in the literature by constructing Lyapunov-like inequalities that…
Fast Optimal Energy Management with Engine On/Off Decisions for Plug-in Hybrid Electric Vehicles
Sebastian East, Mark Cannon
In this paper we demonstrate a novel alternating direction method of multipliers (ADMM) algorithm for the solution of the hybrid vehicle energy management problem considering both…
On the convergence of stochastic MPC to terminal modes of operation
Diego Munoz-Carpintero, Mark Cannon
The stability of stochastic Model Predictive Control (MPC) subject to additive disturbances is often demonstrated in the literature by constructing Lyapunov-like inequalities that…
Parallel ADMM for robust quadratic optimal resource allocation problems
Zawar Qureshi, Sebastian East, Mark Cannon
An alternating direction method of multipliers (ADMM) solver is described for optimal resource allocation problems with separable convex quadratic costs and constraints and linear…