Switching Model Predictive Control Research Articles

Switched model predictive control for networked control systems with time delays and packet disordering

The switched model predictive control (MPC) strategy is investigated in this paper for a class of networked control systems (NCSs) with time delays and packet disordering. A new model is proposed to describe the NCS with packet disordering. State feedback controllers are considered and the resulting closed-loop NCS is modeled as a discrete-time switched system. By using the average dwell-time approach and switching between different cost functions, sufficient conditions for exponential stability of the NCS and design procedures for the stabilizing controllers are presented. Finally, an illustrative example is given to demonstrate the effectiveness of the proposed method.

Switching model predictive control of a pneumatic artificial muscle

In this article, a switching Model Predictive Controller (sMPC) scheme for the position control of a Pneumatic Artificial Muscle (PAM) is being presented. The control scheme is based on a constrained linear and PieceWise Affine (PWA) system model approximation that is able to capture the high nonlinearities of the PAM and improve the overall model accuracy, and is composed of: a) a feed-forward term regulating control input at specific reference set-points, and b) a switching Model Predictive Controller handling any deviations from the system’s equilibrium points. Extended simulation studies were utilized in order to investigate and evaluate the efficacy of the suggested controller in the positioning problem of a PAM.

Pneumatic artificial muscles: A switching Model Predictive Control approach

In this article, a switching Model Predictive Controller (sMPC) for a pneumatic artificial muscle (PAM) is presented. The control scheme is based on a switching PieceWise Affine (PWA) system model approximation that is able to capture the high nonlinearities of the PAM, while improving the overall model accuracy, and is composed of: (a) a feed-forward term regulating control input at specific reference set-points, and (b) a switching Model Predictive Controller handling any deviations from the system's equilibrium points. Extended experimental studies are being presented that prove the overall scheme's efficiency.

Hybrid approach to Networked Control System

Efficient control of Networked Control System (NCS) is a challenge, as the control methods need to deal with non-deterministic variable delays and data loss. This paper presents a novel hybrid approach to NCS where Model Predictive Control (MPC) is applied as a main controller and implicit switching MPC is used for data transmission control in event-driven shared communication medium, leading to complex control system with active data transmission mechanisms.

Nonlinear modelling and control for heart rate response to exercise

In order to accurately regulate cardiovascular response to exercise for individual exerciser, this study proposes a modelling and control integrated approach based on ε-insensitive Support Vector Regression (SVR) and switching control strategy. Firstly, a control oriented modelling approach is proposed to depict nonlinear behaviours of cardiovascular response at both onset and offset of treadmill exercises by using support vector machine regression. Then, based on the established nonlinear time-variant model, a novel switching Model Predictive Control (MPC) algorithm has been proposed for the optimisation of exercise efforts. The designed controller can take into account both coefficient drifting and parameter jump by embedding the identified model coefficient into the optimiser and adopting switching strategy during the transfer between onset and offset of exercises. The effectiveness of the proposed modelling and control approach was shown from the regulation of dynamical heart rate response to exercise through simulation using MATLAB.

Improving performance in model predictive control: Switching cost functionals under average dwell-time

In this paper, we propose a novel switched model predictive control (MPC) algorithm for nonlinear continuous-time systems, where we switch between different cost functionals in order to enhance performance. Thus, different performance criteria can be taken into account. In order to ensure stability of the resulting closed-loop system, we consider switching signals which exhibit a certain average dwell-time. When considering switching signals of this type, certain assumptions are common in the switched systems literature in order to ensure stability, like a matching condition for the different Lyapunov functions and the possibility to find Lyapunov functions with an exponential decay rate. In this paper, we show how these assumptions can be satisfied in the MPC context and thus stability of the proposed switched MPC algorithm can be established.

Tracking control of Wiener models with hierarchical and switching model predictive control

SUMMARY A tracking problem is considered for a Wiener model. A two-layer hierarchical control structure is designed: the upper level controller operates at a slow rate and computes the inputs to be ideally provided to the system; the effective control actions are provided by actuators placed at the lower layer and having faster dynamics. Model predictive control (MPC) laws are synthesized for both layers. In order to cope with the discrepancy between the ideal and the effective control action, a robust MPC controller is designed at the upper level. Such a controller can switch among different operating conditions ensuring different level of robustness. In doing so, the overall controller guarantees steady-state zero error regulation for constant reference signals while trading off robustness versus system performance. A numerical example is reported. Copyright © 2011 John Wiley & Sons, Ltd.

Switching model predictive attitude control for a quadrotor helicopter subject to atmospheric disturbances

In this article a switching model predictive attitude controller for an unmanned quadrotor helicopter subject to atmospheric disturbances is presented. The proposed control scheme is computed based on a piecewise affine (PWA) model of the quadrotor's attitude dynamics, where the effects of the atmospheric turbulence are taken into consideration as additive disturbances. The switchings among the PWA models are ruled by the rate of the rotation angles and for each PWA system a corresponding model predictive controller is computed. The suggested algorithm is verified in experimental studies in the execution of sudden maneuvers subject to forcible wind disturbances. The quadrotor rejects the induced wind disturbances while performing accurate attitude tracking.

Explicit hybrid model predictive control: discontinuous piecewise-affine approximation and FPGA implementation

AbstractIn this paper we introduce a digital architecture implementing the explicit solution of a switched model predictive control problem. Given a mixed-logic dynamical system, we derive an explicit controller in the form of a possibly discontinuous piecewise-affine function. This function is then approximated by resorting to piecewise-affine simplicial functions, which can be implemented on a circuit by extending the representation capabilities of a previously proposed architecture to evaluate the control action. The architecture has been implemented on FPGA and validated on a benchmark example related to an air conditioning system.

Switching Model Predictive Control for a Quadrotor Helicopter under Severe Environmental Flight Conditions

AbstractIn this article a switching Model Predictive Controller for the attitude and hovering control of a prototype Unmanned quadrotor Helicopter (UqH) subject to atmospheric disturbances is presented. The control scheme is computed based on Piecewise Affine (PWA) models of the UqH's attitude and altitude dynamics, where the effects of the atmospheric turbulence are taken into consideration as additive disturbances. The switching of the MPC scheme is ruled by the estimation and the rate of the rotation angles, for the altitude and attitude control correspondingly. Extended experimental studies indicate the overall scheme's efficiency in hovering scenarios despite the applied wind–disturbances.

Steering Vehicle Control by Switched Model Predictive Control

We propose a switching Model Predictive Control (MPC) strategy to control vehicle steering by actuating active front steering (AFS) and electronic stability control (ESC). After describing the piecewise affine prediction model used for MPC design, where the nonlinearities arise from the relation between sideslip angles and tire forces, a switching MPC strategy is implemented, where different local MPC controllers are used depending on the current tire force conditions. The designed controller maintains most of the benefits of a previously designed hybrid model predictive controller, but it has lower complexity and allows more flexible design. The controller stability is verified and the controller behavior during challenging step steering maneuvers is tested in closed-loop simulations against a nonlinear vehicle model.

Switched model predictive control for performance enhancement

This article presents a switched model predictive control (MPC) algorithm for non-linear discrete time systems where the weights on the state and control variables in the cost function to be minimised depend on the current value of the state. In so doing, with a reduced computational burden one can easily include, in the problem formulation, a number of control specifications, such as the requirement to avoid critical regions in the state space or to reduce as much as possible the use of some actuators in other zones of the state space. The proposed MPC method has stability properties and is applied for control of a thermal system. The reported simulation results witness its favourable characteristics with respect to a standard MPC implementation.

INVARIANT SET-BASED ROBUST SOFTLY SWITCHED MODEL PREDICTIVE CONTROL

It is common that an efficient constrained plant operation under full range of disturbance inputs requires meeting different sets of control objectives. This calls for application of multiple model predictive controllers each of them being best fit into specific operating conditions. It is inevitable then to switch between the controllers during the plant operation. A simple immediate hard switching may introduce unwanted transients and also it may not achieve robustly feasible controller operation. A soft switching strategy distributes the switching process over a certain time period and consequently it smoothes the transient processes and achieves the robust feasibility. The softly switched model predictive control scheme based on the invariant set theory is proposed for the additive set bounded and polytopic models of uncertainty. Simulation examples are given.

Softly Switched Model Predictive Control for Control of Integrated Wastewater Treatment Systems at Medium Time Scale

Difficulties occur when one universal control strategy attempts controlling a plant that is under wide range of operating conditions. In order to best adopt the control strategy to existing conditions three operational states: normal, disturbed and emergency are distinguished and the corresponding control strategies are designed. Appearance of different control strategies at hand enforces finding a mechanism to switch between them. It can be done in a hard or a soft manner. In case of wastewater systems the second solution is preferable. The paper describes two methods of soft switching between control strategies based on Model Predictive Control (MPC) as a control technology Supervisory Control Level (SuCL) mounted at the top of hierarchical multilevel-multilayer control structure is responsible for the conducting the switching process. The switching instants, switching time and way of switching are parameters to be selected by the SuCL. The soft switching concepts were examined based on wastewater system in Kartuzy, Poland.