Andrea Lecchini-Visintini

Validating Controllers for Internal Stability Utilizing Closed-Loop Data

We introduce novel tests utilizing a limited amount of experimental and possibly noisy data obtained with an existing known stabilizing controller connected to an unknown plant for verifying that the introduction of a proposed new controller will stabilize the plant. The tests depend on the assumption that the unknown plant is stabilized by a known controller and that some knowledge of the closed-loop system, such as noisy frequency response data, is available and on the basis of that knowledge, the use of a new controller appears attractive. The desirability of doing this arises in iterative identification and control algorithms, multiple-model adaptive control, and multi-controller adaptive switching. The proposed tests can be used for SISO and/or MIMO linear time-invariant systems.

Sequential Monte Carlo for Model Predictive Control

This paper proposes the use of Sequential Monte Carlo (SMC) as the computational engine for general (non-convex) stochastic Model Predictive Control (MPC) problems. It shows how SMC methods can be used to find global optimisers of non-convex problems, in particular for solving open-loop stochastic control problems that arise at the core of the usual receding-horizon implementation of MPC. This allows the MPC methodology to be extended to nonlinear non-Gaussian problems. We illustrate the effectiveness of the approach by means of numerical examples related to coordination of moving agents.

Stochastic Optimization on Continuous Domains With Finite-Time Guarantees by Markov Chain Monte Carlo Methods

We introduce bounds on the finite-time performance of Markov chain Monte Carlo (MCMC) algorithms in solving global stochastic optimization problems defined over continuous domains. It is shown that MCMC algorithms with finite-time guarantees can be developed with a proper choice of the target distribution and by studying their convergence in total variation norm. This work is inspired by the concept of finite-time learning with known accuracy and confidence developed in statistical learning theory.

Verifying stabilizing controllers via closed-loop noisy data: MIMO case

This article introduces novel tests which utilize a limited amount of experimental and possibly noisy data obtained from a stable closed-loop system, i.e. an interconnection of an existing known stabilizing controller and an unknown plant, to infer if the introduction of a prospective controller will stabilize the unknown plant. This extends our earlier results to include the MIMO systems.

On the stability of receding horizon control for continuous-time stochastic systems

Abstract We study the stability of receding horizon control for continuous-time non-linear stochastic differential equations. We illustrate the results with a simulation example in which we employ receding horizon control to design an investment strategy to repay a debt.

On the approximate domain optimization of deterministic and expected value criteria

We define the concept of approximate domain optimizer for deterministic and expected value optimization criteria. Roughly speaking, a candidate optimizer is an approximate domain optimizer if only a small fraction of the optimization domain is more than a little better than it. We show how this concept relates to commonly used approximate optimizer notions for the case of Lipschitz criteria. We then show how random extractions from an appropriate probability distribution can generate approximate domain optimizers with high confidence. Finally, we discuss how such random extractions can be performed using Markov Chain Monte Carlo methods.

Iterative Feedback Tuning for the joint controllers of a 7-DOF Whole Arm Manipulator

This paper presents the tuning of the Proportional Integral Derivative (PID) controllers of the joints of a 7 degrees of freedom (DOF) manipulator with friction using the Iterative Feedback Tuning method. In the procedure both experimental data and model simulations are used and two different approaches to the approximation of the Hessian are tested. Friction identification is also performed for the implementation of friction compensation over the pre-configured joint Proportional Derivative (PD) control of the manipulator. The responses of the system when using joint PID control and joint PD control with gravity and friction compensation are compared.

A simplified model of a fueldraulic actuation system with application to load estimation

In this work, a simplified model of the compressor variable stator vane fueldraulic actuation system of a jet engine is presented. The actuation system is a sub-assembly of the engine’s hydro-mechanical unit. A unique characteristic of the actuator is an internal cooling flow which prevents the overheating of fuel. It is shown that the effect of the cooling flow is well represented by a static input nonlinearity. The resulting model is of the Hammerstein structure. It is then shown that the model can be used for the estimation of the actuator’s external load. The results are validated using an accurate real system simulator.

Optimisation of restricted complexity control for wave energy conversion

We address the design of the Power Takeoff (PTO) device of a wave energy conversion system through direct optimisation of the parameters of a mechanical network according to an optimisation criterion linked to power absorption performance. The results are illustrated through simulations and the behaviours of different PTO realisations are compared.

Modelling for control of a jet engine compressor variable stator vanes hydraulic actuator

In this paper we develop a control oriented model of a hydraulic servo system for the actuation of the variable stator vanes of a jet engine compressor. We first present a non-linear model derived from physical equations. We then develop a simpler piecewise-affine switching linear model which is suitable for the purpose of controller optimisation, system identification, and fault detection. The switching linear model is assessed by comparison with the response of a full nonlinear simulator.