Analysis and Application of Advanced Control Strategies to a Heating Element Nonlinear Model

open4siSustainable control has begun to stimulate research and development in a wide range of industrial communities particularly for systems that demand a high degree of reliability and availability (sustainability) and at the same time characterised by expensive and/or safety critical maintenance work. For heating systems such as HVAC plants, clear conflict exists between ensuring a high degree of availability and reducing costly maintenance times. HVAC systems have highly non-linear dynamics and a stochastic and uncontrollable driving force as input in the form of intake air speed, presenting an interesting challenge for modern control methods. Suitable control methods can provide sustainable maximisation of energy conversion efficiency over wider than normally expected air speeds and temperatures, whilst also giving a degree of “tolerance” to certain faults, providing an important impact on maintenance scheduling, e.g. by capturing the effects of some system faults before they become serious.This paper presents the design of different control strategies applied to a heating element nonlinear model. The description of this heating element was obtained exploiting a data driven and physically meaningful nonlinear continuous time model, which represents a test bed used in passive air conditioning for sustainable housing applications. This model has low complexity while achieving high simulation performance. The physical meaningfulness of the model provides an enhanced insight into the performance and functionality of the system. In return, this information can be used during the system simulation and improved model based and data driven control designs for tight temperature regulation. The main purpose of this study is thus to give several examples of viable and practical designs of control schemes with application to this heating element model. Moreover, extensive simulations and Monte Carlo analysis are the tools for assessing experimentally the main features of the proposed control schemes, in the presence of modelling and measurement errors. These developed control methods are also compared in order to evaluate advantages and drawbacks of the considered solutions. Finally, the exploited simulation tools can serve to highlight the potential application of the proposed control strategies to real air conditioning systems.openTurhan, T.; Simani, S.; Zajic, I.; Gokcen Akkurt, G.Turhan, T.; Simani, Silvio; Zajic, I.; Gokcen Akkurt, G

Current Trends in Tactical Missile Guidance

The problem of tactical missile guidance is very challenging and has been treated using several basic metlfodologies in the past four decades. Major techniques can be grouped underclassical guidance laws, modern guidance laws, l'aws for manoeuvring targets, predictive guidance for endgame scenario, and guidance laws based on intelligent control methods. Each technique has some advantages and disadvantages while implementing in a practical system. Guidance law selection is dictated by nature of flight profile like boost, midcourse, terminal homing, etc, and also miss-distance and a single-shot kill probability. This paper presents a brief survey of the existing techniques and current trends in tactical missile guidance

Aeroelastic control and estimation with a minimal nonlinear modal description

Modal-based, nonlinear Moving Horizon Estimation (MHE) and Model Predictive Control(MPC) strategies for very flexible aeroelastic systems are presented. They are underpinned by an aeroelastic model built from a 1D intrinsic (based on strains and velocities) description of geometrically-nonlinear beams and an unsteady Vortex Lattice aerodynamic model. Construction of a nonlinear, modal-based, reduced order model of the aeroelastic system, employing a state-space realisation of the linearised aerodynamics around an arbitrary reference point, allows us to capture the main nonlinear geometrical couplings at a very low computational cost. Embedding this model in both MHE and MPC strategies, which solve the system continuous-time adjoints efficiently to compute sensitivities, lays the foundations for real-time estimation and control of highly flexible aeroelastic systems. Finally, the performance and versatility of the framework operating in the nonlinear regime is demonstrated on two very flexible wing models, with notably different dynamics, and on two different control setups: a gust-load alleviation problem on a very high aspect ratio wing with slower dynamics, which involves substantial deflections; and flutter suppression on a flexible wing with significantly faster dynamics, where an unconventional nonlinear stabilisation mechanism is unveiled

A Survey of path following control strategies for UAVs focused on quadrotors

The trajectory control problem, defined as making a vehicle follow a pre-established path in space, can be solved by means of trajectory tracking or path following. In the trajectory tracking problem a timed reference position is tracked. The path following approach removes any time dependence of the problem, resulting in many advantages on the control performance and design. An exhaustive review of path following algorithms applied to quadrotor vehicles has been carried out, the most relevant are studied in this paper. Then, four of these algorithms have been implemented and compared in a quadrotor simulation platform: Backstepping and Feedback Linearisation control-oriented algorithms and NLGL and Carrot-Chasing geometric algorithms.Peer ReviewedPostprint (author's final draft

Sum-of-Squares approach to feedback control of laminar wake flows

A novel nonlinear feedback control design methodology for incompressible fluid flows aiming at the optimisation of long-time averages of flow quantities is presented. It applies to reduced-order finite-dimensional models of fluid flows, expressed as a set of first-order nonlinear ordinary differential equations with the right-hand side being a polynomial function in the state variables and in the controls. The key idea, first discussed in Chernyshenko et al. 2014, Philos. T. Roy. Soc. 372(2020), is that the difficulties of treating and optimising long-time averages of a cost are relaxed by using the upper/lower bounds of such averages as the objective function. In this setting, control design reduces to finding a feedback controller that optimises the bound, subject to a polynomial inequality constraint involving the cost function, the nonlinear system, the controller itself and a tunable polynomial function. A numerically tractable approach to the solution of such optimisation problems, based on Sum-of-Squares techniques and semidefinite programming, is proposed. To showcase the methodology, the mitigation of the fluctuation kinetic energy in the unsteady wake behind a circular cylinder in the laminar regime at Re=100, via controlled angular motions of the surface, is numerically investigated. A compact reduced-order model that resolves the long-term behaviour of the fluid flow and the effects of actuation, is derived using Proper Orthogonal Decomposition and Galerkin projection. In a full-information setting, feedback controllers are then designed to reduce the long-time average of the kinetic energy associated with the limit cycle. These controllers are then implemented in direct numerical simulations of the actuated flow. Control performance, energy efficiency, and physical control mechanisms identified are analysed. Key elements, implications and future work are discussed

Trajectory-Following and Off-Tracking Minimization of Long Combination Vehicles: A Comparison Between Nonlinear and Linear Model Predictive Control

In this paper, we compared the linear and nonlinear motion prediction models of a long combination vehicle (LCV). We designed a nonlinear model predictive control (NMPC) for trajectory-following and off-tracking minimisation of the LCV. The used prediction model allowed coupled longitudinal and lateral dynamics together with the possibility of a combined steering, propulsion and braking control of those vehicles in long prediction horizons and in all ranges of forward velocity. For LCVs where the vehicle model is highly nonlinear, we showed that the control actions calculated by a linear time-varying model predictive control (LTV-MPC) are relatively close to those obtained by the NMPC if the guess linearisation trajectory is sufficiently close to the nonlinear solution, in contrast to linearising for specific operating conditions that limit the generality of the designed function. We discussed how those guess trajectories can be obtained allowing off-line fixed time-varying model linearisation that is beneficial for real-time implementation of MPC in LCVs with long prediction horizons. The long prediction horizons are necessary for motion planning and trajectory-following of LCVs to maintain stability and tracking quality, e.g. by optimally reducing the speed prior to reaching a curve, and by generating control actions within the actuators limits

Duality-Based Nonlinear Quadratic Control: Application to Mobile Robot Trajectory-Following

(c) 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.[EN] This paper presents noniterative linearizationbased controllers for nonlinear unconstrained systems, coined as extended Rauch Tung Striebel (ERTS) and unscented Rauch Tung Striebel (URTS) controllers, derived from the duality between optimal control and estimation. The proposed controllers use a Rauch Tung Striebel forward backward smoother as an state estimator to compute the original optimal control problem. The new controllers are applied to trajectory-following problems of differential-drive mobile robots and compared with iterative linear quadratic regulator controller, nonlinear model predictive control, and approximate inference approaches. Simulations show that ERTS and URTS controllers produce almost optimal solutions with a significantly lower computing time, avoiding initialization issues in the other algorithms (in fact, they can be used to initialize them). This paper validates ERTS controller with an experiment of a Pioneer 3-DX mobile robot.This work was supported in part by the PrometeoII/2013/004 through the Generalitat Valenciana, in part by the Spanish Government under Project DPI2011-27845-C02-01, in part by the VALi+d Program through the Generalitat Valenciana, in part by the European Regional Development Fund through the Ministry of Education, Youth and Sports, Czech Republic, under Project CZ.1.05/2.1.00/03.0094, in part by the Regional Innovation Centre for Electrical Engineering, and in part by the Czech Science Foundation under Project GACR P 102/11/0437. Recommended by Associate Editor A. G. Aghdam.Armesto Ángel, L.; Girbés, V.; Sala, A.; Miroslav Zima; Václav mídl (2015). Duality-Based Nonlinear Quadratic Control: Application to Mobile Robot Trajectory-Following. IEEE Transactions on Control Systems Technology. 23(4):1494-1504. https://doi.org/10.1109/TCST.2014.2377631S1494150423

Identifiability and parameter estimation of the single particle lithium-ion battery model

This paper investigates the identifiability and estimation of the parameters of the single particle model (SPM) for lithium-ion battery simulation. Identifiability is addressed both in principle and in practice. The approach begins by grouping parameters and partially non-dimensionalising the SPM to determine the maximum expected degrees of freedom in the problem. We discover that, excluding open circuit voltage, there are only six independent parameters. We then examine the structural identifiability by considering whether the transfer function of the linearised SPM is unique. It is found that the model is unique provided that the electrode open circuit voltage functions have a known non-zero gradient, the parameters are ordered, and the electrode kinetics are lumped into a single charge transfer resistance parameter. We then demonstrate the practical estimation of model parameters from measured frequency-domain experimental electrochemical impedance spectroscopy (EIS) data, and show additionally that the parametrised model provides good predictive capabilities in the time domain, exhibiting a maximum voltage error of 20 mV between model and experiment over a 10 minute dynamic discharge.Comment: 16 pages, 9 figures, pre-print submitted to the IEEE Transactions on Control Systems Technolog