| 1. |
- Li, Yuchao, et al.
(författare)
-
Performance Bounds of Model Predictive Control for Unconstrained and Constrained Linear Quadratic Problems and Beyond
- 2023
-
Konferensbidrag (refereegranskat)abstract
- We study unconstrained and constrained linear quadratic problems and investigate the suboptimality of the model predictive control (MPC) method applied to such problems. Considering MPC as an approximate scheme for solving the related fixed point equations, we derive performance bounds for the closed-loop system under MPC. Our analysis, as well as numerical examples, suggests new ways of choosing the terminal cost and terminal constraints, which are not related to the solution of the Riccati equation of the original problem. The resulting method can have a larger feasible region, and cause hardly any loss of performance in terms of the closed-loop cost over an infinite horizon.
|
|
| 2. |
- Maity, Dipankar, et al.
(författare)
-
Regret-Optimal Cross-Layer Co-Design in Networked Control Systems - Part II: Gauss-Markov Case
- 2023
-
Ingår i: IEEE Communications Letters. - : Institute of Electrical and Electronics Engineers (IEEE). - 1089-7798 .- 1558-2558. ; 27:11, s. 2879-2883
-
Tidskriftsartikel (refereegranskat)abstract
- In the first part of this two-letter series, we proposed a cross-layer framework for joint optimal Quality-of-Control (QoC) and Quality-of-Service (QoS) co-design for networked control systems. In this second part, we employ this framework to perform optimal co-design for networked control systems comprising multiple Gauss-Markov systems. We analytically derive the joint optimal policies based on the information couplings between the physical and the network layers. A numerical case study illustrates the framework.
|
|
| 3. |
- Mamduhi, Mohammad H., et al.
(författare)
-
Regret-Optimal Cross-Layer Co-Design in Networked Control Systems - Part I: General Case
- 2023
-
Ingår i: IEEE Communications Letters. - : Institute of Electrical and Electronics Engineers (IEEE). - 1089-7798 .- 1558-2558. ; 27:11, s. 2874-2878
-
Tidskriftsartikel (refereegranskat)abstract
- Performance of control systems interacting over a shared communication network is tightly coupled with how the network provides services and distributes resources. Novel networking technology such as 5G is capable of providing tailored services for a variety of network demands. Stringent control requirements and their critical performance specification call for online adaptable and control-aware network services. This perspective suggests a co-design of physical and network layers aiming to ensure that the necessary quality-of-service is provided to achieve the desired quality-of-control. An optimal co-design is in general challenging due to cross-layer couplings between the physical and network layers and their layer-specific functionalities. Furthermore, the complexity of the co-design depends on the level of actionable information the layers share with each other. In this Part I of a two-letter series, we present a general co-design of physical operations and service allocation aiming to minimize a social regret measure for networked control systems. We introduce an optimal networked co-design scenario using the regret index as the joint quality-of-control and quality-of-service (QoC-QoS) measure, and discuss the role of cross-layer awareness in the structure of optimization problems. We mainly focus on the finite-horizon case but we briefly present the infinite-horizon case as well. In Part II, we discuss regret-optimal cross-layer policies for Gauss-Markov systems and derive the optimal solutions based on the general problems introduced in Part I.
|
|
| 4. |
- Mattila, Robert
(författare)
-
Hidden Markov Models: Identification, Inverse Filtering and Applications
- 2020
-
Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- A hidden Markov model (HMM) comprises a state with Markovian dynamics that is hidden in the sense that it can only be observed via a noisy sensor. This thesis considers three themes in relation to HMMs, namely, identification, inverse filtering and applications.In order to employ an HMM, its parameters have first to be identified (or, estimated) from data. Traditional maximum-likelihood estimation procedures may, in practice, suffer from convergence to bad local optima and high computational cost. Recently proposed methods of moments address these shortcomings, but are less accurate. We explore how such methods can be extended to incorporate non-consecutive correlations in data so as to improve their accuracy (while still retaining their attractive properties).Motivated by applications in the design of counter-adversarial autonomous (CAA) systems, we then ask the question: Is it possible to estimate the parameters of an HMM from other data sources than just raw measurements from its sensor? To answer this question, we consider a number of inverse filtering problems. First, we demonstrate how HMM parameters and sensor measurements can be reconstructed from posterior distributions from an HMM filter. Next, we show how to estimate such posterior distributions from actions taken by a rational agent. Finally, we bridge our results to provide a solution to the CAA problem of remotely estimating the accuracy of an adversary’s sensor based on its actions.Throughout the thesis, we motivate our results with applications in various domains. A real-world application that we investigate in particular detail is how the treatment of abdominal aortic aneurysms can be modeled in the Markovian framework. Our findings suggest that the structural properties of the optimal treatment policy are different than those recommended by current clinical guidelines – in particular, that younger patients could benefit from earlier surgery. This indicates an opportunity for improved care of patients with the disease.
|
|
