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, features particularly pertinent to control applications. The proposed algorithms enjoy static and dynamic regret bounds in terms of the dynamics of the reference action sequence, gradient prediction error, and function prediction error, which are generalizations of known regularity measures from the literature. We present results for both convex and strongly convex costs. We validate the performance of the proposed algorithms in a trajectory tracking case study, as well as portfolio optimization using real-world datasets.

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 exact finite optimization-based framework to numerically solve an optimal bank of filters in which the contribution of measurement noise to the residual is minimized. The design problem is safely approximated through linear matrix inequalities and thus becomes tractable. We further propose a thresholding policy along with probabilistic false-alarm guarantees to estimate the active system mode in real-time. In comparison with the existing results, the guarantees improve from a polynomial dependency in the probability of false alarm to a logarithmic form. This improvement is achieved under the additional assumption of sub-Gaussianity, which is expected in many applications. The performance of the proposed approach is validated through a numerical example and an application of the building radiant system.

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 mappings. A tractable convex reformulation of the learning problem is also presented. The method is effective for learning input-action mapping in continuous input-action space with input-output constraints, typically present in control systems. The learning approach can be effectively transformed to learn a Model Predictive Control (MPC) behaviour and a case study to mimic an MPC is presented, which is a rather computationally heavy control strategy. Simulation and experimental results show the effectiveness of the proposed approach.

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 filters (that generate desired residuals) and definition of a certain type of residual/threshold-based diagnosis rules. The filters' construction is cast as linear feasibility problems. These feasibility problems enforce desirable diagnosis relationships between each subsystem's affine constant and each residual. The diagnosis rules are inspired by the well-known generalized observer scheme. Moreover, we provide a method to compute each mode's diagnosis time based on the diagnosis rules and properly chosen residual thresholds. A numerical example is presented to show the performance of the proposed approach.

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 centralized sensory node as the triggering mechanism can be evaluated locally at each individual sensor. From a geometrical viewpoint, the mechanism is a sequence of hyperrectangles surrounding the optimal state trajectory such that robust recursive feasibility and robust stability are guaranteed. The design of the triggering mechanism is cast as a constrained parametric-in-set optimization problem with the volume of the set as the objective function. Reparameterized in terms of the set vertices, we show that the problem admits a finite tractable convex program reformulation and a linear program relaxation. Several numerical examples are presented to demonstrate the effectiveness and limitations of the theoretical results.

Distinct advantages of the combination of the pneumatic actuated plants with “on/off” solenoid valves have motivated many researchers to conduct research in this scope. Pulse-width modulation (PWM) is a tool to use such combination in servo tasks. This paper studies the most frequent and well-reported PWM schemes. These PWM schemes have been applied for positioning tasks of the pneumatic actuators using solenoid on/off valves. In this study, the positioning performance of the servo pneumatic system, utilizing different PWM schemes, is investigated through several experiments with step and sinusoidal reference inputs. Rise time, overshoot, and steady-state error, in step input tests, and tracking performance in sinusoidal tests demonstrate the effectiveness of the deployed PWM schemes in the pneumatic system behaviors. Moreover, the closed-loop results demonstrate that the system robustness against the increase of the system’s mass is associated with the applied PWM scheme. Based on the open-loop results, two factors are found to be affecting PWM schemes performance, namely, the difference in the cross-sectional areas on each side of the piston and the piston position, in which these factors have not been considered in the design procedure of the former studies. Taking into account the former factor, a modified version of the PWM schemes is proposed. Steady-state errors in closed-loop tests verify the effectiveness of the developed modified PWM schemes.