Multimode diagnosis for switched affine systems with noisy measurement

We study a diagnosis scheme to reliably detect the active mode of discrete-time, switched affine systems in the presence of measurement noise and asynchronous switching. The proposed scheme consists of two parts: (i) the construction of a bank of filters, and (ii) the introduction of a residual/threshold-based diagnosis rule. We develop an...

Adaptive Composite Online Optimization: Predictions in Static and Dynamic Environments

In the past few years, online convex optimization (OCO) has received notable attention in the control literature thanks to its flexible real-time nature and powerful performance guarantees. In this article, we propose new step-size rules and OCO algorithms that simultaneously exploit gradient predictions, function predictions and dynamics,...

Learning for Control: An Inverse Optimization Approach

We present a learning method to learn the mapping from an input space to an action space, which is particularly suitable when the action is an optimal decision with respect to a certain unknown cost function. We use an inverse optimization approach to retrieve the cost function by introducing a new loss function and a new hypothesis class of...

Multimode Diagnosis for Switched Affine Systems

In this paper, we propose an approach to detect mode transitions and to isolate active modes in discrete-time, switched affine systems. The proposed approach is in particular constructed for systems in which the controller is oblivious of the switching signal. The diagnosis approach consists of two main parts: construction of a bank of output...

Learning for Control: An Inverse Optimization Approach

We present a learning method to learn the mapping from an input space to an action space, which is particularly suitable when the action is an optimal decision with respect to a certain unknown cost function. We use an inverse optimization approach to retrieve the cost function by introducing a new loss function and a new hypothesis class of...

A Decentralized Event-Based Approach for Robust Model Predictive Control

In this paper, we propose an event-based sampling policy to implement a constraint-tightening, robust MPC method. The proposed policy enjoys a computationally tractable design and is applicable to perturbed, linear time-invariant systems with polytopic constraints. In particular, the triggering mechanism is suitable for plants with no...

Continuous-Time Accelerated Methods via a Hybrid Control Lens

Treating optimization methods as dynamical systems can be traced back centuries ago in order to comprehend the notions and behaviors of optimization methods. Lately, this mindset has become the driving force to design new optimization methods. Inspired by the recent dynamical system viewpoint of Nesterov's fast method, we propose two classes...

Applications of trajectory-based analysis in optimization and control

The synergy between optimization and control is a long standing tradition. In fact, this synergy is becoming more and more apparent because of the multi-disciplinary character of the most pressing, current engineering problems along with constant developments of these two fields. <br/>Historically, optimization methods have helped the control...

A Linear Programming Approach to Design Online Triggering Mechanisms for Robust MPC

In this paper, an event-triggering approach is proposed for a robust model predictive control method. The approach is applicable to constrained, linear time-invariant systems with bounded, additive disturbances. At each triggering instant, the triggering mechanism is designed online using a linear programming approach. Intuitively, the...

Abstracted models for scheduling of event-triggered control data traffic

Event-Triggered control (ETC) implementations have been proposed to overcome the inefficiencies of periodic (time-triggered) controller designs, namely the over-exploitation of the computing and communication infrastructure. However, the potential of aperiodic Event-Triggered techniques to reuse the freed bandwidth, and to reduce energy...

A hybrid control framework for fast methods under invexity: Non-Zeno trajectories with exponential rate

In this paper, we propose a framework to design a class of fast gradient-based methods in continuous-time that, in comparison with the existing literature including Nesterov's fast-gradient method, features a state-dependent, time-invariant damping term that acts as a feedback control input. The proposed design scheme allows for a user...

Fast gradient-based methods with exponential rate: A hybrid control framework

Ordinary differential equations, and in general a dynamical system viewpoint, have seen a resurgence of interest in developing fast optimization methods, mainly thanks to the availability of well-established analysis tools. In this study, we pursue a similar objective and propose a class of hybrid control systems that adopts a 2nd-order...