DSpace Repository :: Browsing by Author "Johansen T.A."

Browsing by Author "Johansen T.A."

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A Computational approach to explicit feedback stochastic nonlinear model predictive control
(2010-01-01) Grancharova A.; Johansen T.A.
Nonlinear Model Predictive Control (NMPC) involves the solution at each sampling instant of a finite horizon optimal control problem subject to nonlinear system dynamics, and state and input constraints. Mathematical models of engineering systems usually contain some amount of uncertainty. In the robust NMPC problem formulation, the model uncertainty is taken into account. This paper presents an approximate multi-parametric Nonlinear Programming approach to explicit solution of feedback stochastic MPC problems for constrained nonlinear systems in the presence of stochastic uncertainty. It is assumed that the discrete probability distribution of the uncertainty is known. The mathematical expectation of the cost function is minimized subject to state and input constraints. The approximate explicit approach constructs a piecewise nonlinear approximation to the optimal sequence of feedback control policies. It is demonstrated by explicit feedback stochastic NMPC for a cart moving on a plane and attached to the wall via a spring. ©2010 IEEE.
Computational aspects of approximate explicit nonlinear model predictive control
(2007-11-19) Grancharova A.; Johansen T.A.; Tøndel P.
It has recently been shown that the feedback solution to linear and quadratic constrained Model Predictive Control (MPC) problems has an explicit representation as a piecewise linear (PWL) state feedback. For nonlinear MPC the prospects of explicit solutions are even higher than for linear MPC, since the benefits of computational efficiency and verifiability are even more important. Preliminary studies on approximate explicit PWL solutions of convex nonlinear MPC problems, based on multi-parametric Nonlinear Programming (mp-NLP) ideas show that sub-optimal PWL controllers of practical complexity can indeed be computed off-line. However, for non-convex problems there is a need to investigate practical computational methods that not necessarily lead to guaranteed properties, but when combined with verification and analysis methods will give a practical tool for development and implementation of explicit NMPC. The present paper focuses on the development of such methods. As a case study, the application of the developed approaches to compressor surge control is considered. © 2007 Springer-Verlag Berlin Heidelberg.
Distributed Nonlinear Model Predictive Control by Sequential Linearization and Accelerated Gradient Method
(2016-01-01) Grancharova A.; Johansen T.A.; Petrova V.
A suboptimal approach to distributed NMPC for nonlinear interconnected systems subject to constraints is proposed. The objective is to develop a computationally efficient approach. The suggested method is based on a sequential linearization of the nonlinear system dynamics and finding a suboptimal solution of the resulting Quadratic Programming problem by using distributed iterations of the dual accelerated gradient method. The benefits of the approach are reduced complexity of the on-line computations, and simple software implementation, which makes it appropriate for embedded distributed convex NMPC. The proposed method is illustrated with simulations on the model of a quadruple-tank system.
Distributed quasi-nonlinear model predictive control by dual decomposition
(2011-01-01) Grancharova A.; Johansen T.A.
A suboptimal approach to distributed NMPC for a class of systems consisting of nonlinear subsystems with linearly coupled dynamics subject to both state and input constraints is proposed. The approach applies a dual decomposition method to represent the original centralized NMPC problem into a distributed quasi-NMPC problem by linearization of the nonlinear system dynamics and taking into account the couplings between the subsystems. © 2011 IFAC.
Distributed Quasi-Nonlinear Model Predictive Control with Contractive Constraint
(2018-01-01) Grancharova A.; Johansen T.A.
An approach to low complexity distributed MPC of nonlinear interconnected systems with coupled dynamics subject to both state and input constraints is proposed. It is based on the idea of introducing a contractive constraint in the centralized NMPC problem formulation, which would guarantee the closed-loop system stability when using a small prediction horizon. Particularly, the one step ahead NMPC problem is considered. Further, a quasi-NMPC method is developed, which is based on a sequential linearization of the nonlinear system dynamics and finding distributedly a suboptimal solution of the resulting convex Quadratically Constrained Quadratic Programming problem. The suggested approach would be appropriate for distributed convex NMPC of some cyber-physical systems, since it will reduce the complexity of the on-line NMPC computations, simplify the software implementation, and reduce the requirements for available memory. The proposed method is illustrated with simulations on the model of a quadruple-tank system.
Dual-mode distributed Model Predictive Control of a quadruple-tank system
(2018-01-01) Grancharova A.; Johansen T.A.; Olaru S.
In this paper, a dual-mode distributed Model Predictive Control (MPC) approach is proposed in order to reduce the on-line computational complexity of the distributed optimal control of nonlinear interconnected systems. It consists in using a nonlinear distributed MPC approach when the state variables of the overall system are far from the origin and applying a linear distributed MPC method in a neighborhood of the origin. The nonlinear distributed approach is based on first-principles (nonlinear) models of the interconnected systems dynamics. It includes a sequential linearization of these models and finding distributedly a suboptimal solution of the resulting quadratic programming problem. In order to apply the linear distributed MPC method, it is necessary first to obtain a linearized model of the overall nonlinear system in a neighborhood of the origin. The benefit of the suggested dual-mode distributed MPC approach is the reduced complexity of the on-line computations in comparison to the entirely nonlinear approach when the current overall system state is in a neighborhood of the origin. The proposed method is illustrated with simulations on the model of a quadruple-tank system.
Explicit approaches to constrained model predictive control: A survey
(2004-01-01) Grancharova A.; Johansen T.A.
This paper presents a review of the explicit approaches to constrained model predictive control. The main motivation behind the explicit solution is that it avoids the need for real-time optimization, and thus allows implementation at high sampling frequencies in real-time systems with high reliability and low software complexity. The paper is organized as follows. Section 1 includes formulation of the constrained linear quadratic regulation (LQR) problem, summary of the implicit approaches, and the basics of the model predictive control (MPC). Sections 2 and 3 consider respectively the exact and the approximate approaches to explicit solution of constrained MPC problems, together with several examples.
Explicit approximate model predictive control of constrained nonlinear systems with quantized input
(2009-06-15) Grancharova A.; Johansen T.A.
In this paper, a Model Predictive Control problem for constrained nonlinear systems with quantized input is formulated and represented as a multi-parametric Nonlinear Integer Programming (mp-NIP) problem. Then, a computational method for explicit approximate solution of the resulting mp-NIP problem is suggested. The proposed approximate mp-NIP approach is applied to the design of an explicit approximate MPC controller for a clutch actuator with on/off valves. © 2009 Springer Berlin Heidelberg.
Explicit min-max model predictive control of constrained nonlinear systems with model uncertainty
(2005-01-01) Grancharova A.; Johansen T.A.
This paper presents an approximate multi-parametric nonlinear programming approach to explicit solution of constrained nonlinear model predictive control (MPC) problems in the presence of model uncertainty. The case of time-invariant parameter uncertainty is considered. The explicit MPC controller is based on an orthogonal search tree structure of the state space partition and is designed by solving a min-max optimization problem. It is robust in the sense that all constraints are satisfied for all possible values of the uncertain parameters. The approach is applied to design an explicit min-max MPC controller for a continuous stirred tank reactor, where the heat transfer coefficient is an uncertain parameter. Copyright © 2005 IFAC.
Explicit stochastic predictive control of combustion plants based on Gaussian process models
(2008-06-01) Grancharova A.; Kocijan J.; Johansen T.A.
Energy production is one of the largest sources of air pollution. A feasible method to reduce the harmful flue gases emissions and to increase the efficiency is to improve the control strategies of the existing thermoelectric power plants. This makes the Nonlinear Model Predictive Control (NMPC) method very suitable for achieving an efficient combustion control. Recently, an explicit approximate approach for stochastic NMPC based on a Gaussian process model was proposed. The benefits of an explicit solution, in addition to the efficient on-line computations, include also verifiability of the implementation, which is an essential issue in safety-critical applications. This paper considers the application of an explicit approximate approach for stochastic NMPC to the design of an explicit reference tracking NMPC controller for a combustion plant based on its Gaussian process model. The controller brings the air factor (respectively the concentration of oxygen in the flue gases) on its optimal value with every change of the load factor and thus an optimal operation of the combustion plant is achieved. © 2008 Elsevier Ltd. All rights reserved.
Rotary-wing UAVs trajectory planning by distributed linear MPC with reconfigurable communication network topologies
(2013-01-01) Grancharova A.; Grøtli E.I.; Johansen T.A.
In this paper, a distributed approach to Model Predictive Control (MPC)-based trajectory planning for rotary-wing UAV (Unmanned Aerial Vehicle) communication network topologies under radio path loss constraints is proposed. The goal is to find trajectories that are safe with respect to grounding and collision, fuel efficient and satisfy criteria for communication such that the UAVs form chains to multiple targets with given radio communication capacities. The MPC-based optimization sub-problems are computed autonomously within each UAV, using convex quadratic programming, with the requirement that each UAV communicates its current measured position to all other UAVs. In addition, a simple coordination between UAVs allows for the communication network topology to be reconfigured in case of failures or radio path loss changes. The control performance of the distributed linear MPC trajectory planning is studied based on a simulation case with four UAVs and two targets. Copyright © 2013 IFAC.

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