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An enhanced lazy learning approach incorporated with relevance vector machine (ELL-RVM) is proposed for modeling of the fixed-bed intermittent gasification processes inside UGI gasifiers. The online measured temperature of produced crude gas plays a dominant role during gasification processes. However, it is difficult to formulate the dynamics of gasifier's temperature via first principles due to...
This paper addresses the problem of flow variation of timed continuous Petri Nets under infinite server semantics. Our goal is not only to demonstrate the impact of the weighting factor associated to the expression of the flow in the cost function, in reducing flow variation. It is also to find the optimal value and a control optimizing the defined cost function that drives the evolution of the system...
In this paper, evolution of a multi agent system (MAS) in n-D space where n+1 or more leader agents located on the boundary guide the MAS is studied. We consider the MAS as a deformable body whose motion can be prescribed by a homogeneous map determined by the initial and current positions of the leaders. Each follower agent learns this leader prescribed motion plan by local communication with adjacent...
This paper presents a minimum entropy approach for the design of an optimal bang-bang control for linear dynamical systems with parametric uncertainty. The curse of dimensionality is of major concern when the dimension of the uncertain parameter space increases while designing robust controllers. In this paper an alternative probabilistic approach is developed to account for parameter uncertainty...
Nowadays, the stereoscopic 3D (S3D) has become more and more popular. However, sometimes people can still feel uncomfort when watching S3D videos. This phenomenon, to some extent, comes from the non-synchronization of the left and right views. That is, when a S3D videos is shot by two individual cameras, the left frames can't match exactly with their corresponding right frames in time domain. If the...
This paper is concerned with a design of stabilizing model predictive control laws for discrete-time linear periodic systems with state and control constraints. Two algorithms are presented. The first one is based on interpolation between several unconstrained periodic controllers. Among them, one controller is chosen for the performance while the rest is used to extend the domain of attraction. The...
This paper addresses the problem of flow variation of Timed Continuous Petri Net (TCPN) systems under infinite server semantics. The problem is studied using Model Predictive Control. Our goal is to find an approach which limits the high flow variation by adding, in the cost function, a new term that takes in consideration these variations and that is pondered with a weighting factor. It has been...
A new numerical technique is presented for solving optimal control problems. This paper introduces a direct method that calculates optimal trajectories by discretizing the system dynamics using Galerkin numerical techniques and approximates the cost function with quadrature. We show that a weak enforcement of boundary conditions leads to improved solution accuracies. Also, we show that the Galerkin...
We present a differential particle swarm evolution (DPSE) algorithm which combines the basic idea of velocity and position update rules from particle swarm optimization (PSO) and the concept of differential mutation from differential evolution (DE) in a new way. With the goal of optimizing within a limited number of function evaluations, the algorithm is tested and compared with the standard PSO and...
This paper deals with mixed logical dynamical systems controlled via model predictive schemes. For this class of systems a centralized MPC approach is firstly introduced, in which the finite horizon optimal control problem is a mixed-integer quadratic programming problem aiming at minimizing the deviations of the system variables from their equilibrium points. Since the application in real time of...
Control system networks are increasingly being connected to enterprise level networks. These connections leave critical industrial controls systems vulnerable to cyber-attacks. Most of the effort in protecting these cyber-physical systems (CPS) from attacks has been in securing the networks using information security techniques. Effort has also been applied to increasing the protection and reliability...
This paper presents a robust performance analysis result for a class of uncertain quantum systems containing sector bounded nonlinearities arising from perturbations to the system Hamiltonian. An LMI condition is given for calculating a guaranteed upper bound on a non-quadratic cost function. This result is illustrated with an example involving a Josephson junction in an electromagnetic cavity.
Approximate dynamic programming is utilized in this study to develop solutions for optimal switching problems. The order of the active subsystems and the number of switches are free and an online solution is developed that produces optimal performance. Motivated by the development in the adaptive critics literature, the proposed method uses a critic and as many actors as the number of subsystems in...
This paper introduces the predictive vector field controller (PVF). This controller is designed for controlling nonlinear systems with constraints in order to follow artificial vector fields whose integral curves converge to and circulate about a desired path. This is achieved by predicting the state of the system at some future time horizon using a finite set of system inputs by uniformly sampling...
We investigate bandwidth allocation and scheduling in non-cooperative wireless networks as a mixed integer programming problem. Fast Vickrey-Clarke-Groves (VCG) auction-based bandwidth allocation (FABA), incorporating relaxation-based greedy algorithm (RGA) and split-flow-based algorithm (SFA), is proposed by modifying the traditional VCG auction to make it computationally feasible. With incentives...
This paper addresses the management of drinking water networks (DWNs) regarding a multi-objective cost function by means of economically-oriented model predictive control (EMPC) strategies. Specifically, assuming the water demand and the energy price as periodically time-varying signals, this paper shows that the EMPC framework is flexible to enhance the control of DWNs without relying on hierarchical...
In this paper, a new multi-objective model predictive control (MPC) algorithm is proposed for multi-objective control of constrained nonlinear systems. Due to the conflict of multiple objectives, the desired costs and compromise solutions of these objectives are first computed. Then a dual-mode tracking control law of control Lyapunov functions is constructed to design the multi-objective MPC controller...
We present in this paper a preliminary result on learning-based adaptive trajectory tracking control for nonlinear systems. We propose, for the class of nonlinear systems with parametric uncertainties which can be rendered integral Input-to-State stable w.r.t. the parameter estimation errors input, that it is possible to merge together the integral Input-to-State stabilizing feedback controller and...
In this paper, we present a new dynamic flow routing model on networks of parallel paths with capacities on edges and a regime of random queues at vertices. According to the introduced rules of waiting at vertices, we estimate the routing cost functions expressed by a mathematical expectation of a routing duration. For the corresponding routing game with nonatomic agents we prove the existence of...
In this paper, we present a factor graph framework to solve both estimation and deterministic optimal control problems, and apply it to an obstacle avoidance task on Unmanned Aerial Vehicles (UAVs). We show that factor graphs allow us to consistently use the same optimization method, system dynamics, uncertainty models and other internal and external parameters, which potentially improves the UAV...
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