Explicit Model Predictive Control Approach Research Articles

Complexity reduction of explicit MPC based on fuzzy reshaped polyhedrons for use in industrial controllers

The explicit model predictive control (EMPC) generates the rules of control defined for a set of polyhedral regions. Online EMPC calculations consist of searching a look-up table to find the appropriate control law according to a particular state. This paper discusses the complexity of online computation and the memory required to store data in an EMPC implementation. Therefore, a new reshaping method is applied to the active regions so that the definition of the polyhedron has regular boundaries. This approach has made some improvements. First, the usable memory will be a lot less for the actual implementation compared to the traditional EMPC approach. Second, the small number of new clusters reduces search time in explicit lookup tables and speeds up overall implementation. To this end, fuzzy clustering is used to introduce a novel method of transforming polyhedrons in the context of fuzzy explicit model predictive (FEMPC) control, followed by a new fuzzy-based piece-wise affine (PWA) explicit formulation for control law calculations. The stability of the proposed method is investigated using the Lyapunov stability criteria. The proposed algorithm has been tested on a nonlinear continuous stirred tank reactor (CSTR) benchmark system and simulation tests show that the proposed approach involves a compromise between storage space requirements and online efficiency.

Real-time energy-saving control for HEVs in car-following scenario with a double explicit MPC approach

The rapid growth of electrification, automation and connectivity in the transport industries puts forward higher requirements on control strategies to improve energy efficiency, traffic safety and driving comfort. Intense efforts have developed energy management strategies (EMS) in car-following scenarios for hybrid electric vehicles (HEVs) by adopting model predictive control (MPC). However, the computational complex online optimization intrinsic to MPC hinders its real-time implementation. This paper is thus proposed to develop a framework of energy-saving controller for HEVs based on explicit MPC, taking advantage of its online computational efficiency, to enable real-time control. To achieve this, the constrained finite-time optimization control (CFTOC) problems of car-following control and energy management strategy for a hybrid electric vehicle are formulated separately. The two problems are then shifted to explicit MPC by precomputing the explicit solutions offline and the control laws are coupled together to form the control framework. Numerical simulations show that the proposed controller can improve the energy efficiency, driving safety and comfort while reduce the online computational costs. Moreover, the result of the hardware-in-the-loop experiment demonstrates the real-time performance of the proposed controller.

A Low Voltage Ride Through Strategy of DFIG based on Explicit Model Predictive Control

In this paper, an improved demagnetization control, based on Explicit Model Predictive Control (E-MPC) is proposed to improve Low Voltage Ride Through (LVRT) capability of Doubly Fed Induction Generators (DFIGs). By injecting an additional rotor current component, the demagnetization control can efficiently eliminate the free and negative flux to avoid saturation of the rotor converter. The conventional demagnetization control is based on fixed scaling factors, whose control performance can’t be guaranteed for different fault conditions. The proposed E-MPC approach can fully explore the potential of rotor side converter. Besides, the control parameters of E-MPC are derived offline and very efficient for online control. In addition, the proposed E-MPC structure is simple and easy to be implemented. The mechanism of proposed E-MPC is presented in detail and verified in Matlab/Simulink. The results show that the proposed control scheme has a good performance and can improve the LVRT capability of DFIGs under various fault conditions, especially unbalanced faults.

Constrained linear parameter‐varying control using approximate multiparametric programming

SummaryWe develop an approximate multiparametric convex programming approach with its application to control constrained linear parameter‐varying systems. Recently, the application of the real‐time model predictive control (MPC) for various engineering systems has been significantly increased by using the multiparametric convex programming tool, known as explicit MPC approach. The main idea of explicit MPC is to move the major parts of the computations to offline phase and to provide an explicit piecewise affine solution of the constrained MPC problem, which is defined over a set of convex polyhedral partitions. In the proposed method, the idea of convex programming and partitioning is applied for linear parameter‐varying control systems. The feasible space of the time‐varying parameters is divided into simplices in which approximate solutions are calculated such that the approximation error is kept limited by solving sequences of linear programs. The approximate optimal solution within each simplex is obtained by linear interpolation of the optimal solutions in the simplex vertices, and then multiparametric programming tool is utilized to compute an explicit state feedback solution of linear quadratic optimal control problem for simplex vertices subject to state and input constraints. The proposed method is illustrated by a numerical example and the simulation results show the advantages of this approach.

A low-complexity explicit MPC controller for AFR control

Control of the air–fuel ratio in combustion engines is of imminent importance when aiming at reduction of the fuel consumption and mitigation of emissions. In this paper this is achieved by employing Model Predictive Control (MPC), which keeps the air-fuel ratio close to the stoichiometric value. The real-time implementation of MPC is accelerated by pre-computing the optimal solution and using the explicit MPC approach. The memory footprint of such a controller is subsequently reduced by employing a two-layered compression scheme. Quality of the regulation is demonstrated using real engine data and is compared to traditional control approach based on PID.

Explicit Model Predictive Control Approach for Low-Thrust Spacecraft Proximity Operations

The key role of autonomous systems in future space missions has made model predictive control a very attractive guidance and control technique. However, the capability of low-power spacecraft processors to handle the real-time computational load of this technique still needs to be fully established, especially for complex control problems. This paper introduces a method to improve the computational efficiency of model predictive control when applied to the problem of autonomous rendezvous and proximity maneuvering using low-thrust propulsion. To ensure safe trajectories in this scenario, a long control horizon is required and the control problem must be solved at a relatively fast sampling rate. The proposed design addresses such requirements by parameterizing the thrust profile with a set of Laguerre functions. In this setting, the number of control variables can be made significantly smaller than the length of the control horizon, as opposed to standard design methods. By exploiting this property, in combination with multiparametric programming techniques, an explicit control law is derived that is suitable for real-time implementation on simple hardware. The performance of this approach is demonstrated on a small spacecraft mission and compared with that of other control techniques.

An Explicit Model Predictive Control Framework for Turbocharged Diesel Engines

The turbocharged diesel engine is a typical multi-input multioutput system with strong couplings, actuator constraints, and fast dynamics. This paper addresses the exhaust emission regulation in turbocharged diesel engines using an explicit model predictive control (EMPC) approach, which allows tracking of the time-varying setpoint values generated by the supervisory level controller while satisfying the actuator constraints. The proposed EMPC framework consists of calibration, engine model identification, controller formulation, and state observer design. The proposed EMPC approach has a low computation requirement and is suitable for implementation in the engine control unit on board. The experimental results on a turbocharged Cat C6.6 diesel engine demonstrate that the EMPC controller significantly improves the tracking performance of the exhaust emission variables in comparison with the decoupled single-input single-output control methods.

EXPLICIT MODEL PREDICTIVE CONTROL OF FAST DYNAMIC SYSTEM

Explicit Model Predictive Control approach provides offline computation of the optimization law by Multi Parametric Quadratic Programming. The solution is Piece wise affine in nature. It is explicit representation of the system states and control inputs. Such law then can be solved using binary search tree and can be evaluated for fast dynamic systems. Implementing such controllers can be done on microcontroller or ASIC/FPGA. DC Motor Speed Control - one of the benchmark systems is discussed here in this context. Its PWA law obtained, simulation of closed loop e-MPC is presented and its implementation approach using MPT toolbox and other such toolboxes is shown in brief.

Real-Time Implementation of a Constrained MPC for Efficient Airflow Control in a PEM Fuel Cell

<para xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> Fuel cells represent an area of great industrial interest due to the possibility to generate clean energy for stationary and automotive applications. It is clear that the proper performance of these devices is closely related to the kind of control that is used; therefore, a study of improved control alternatives is fully justified. The air-supply control is widely used to guarantee safety and to achieve a high performance. This paper deals with this control loop, proposing and comparing two control objectives aimed at satisfying the oxygen starvation avoidance criterion and the maximum efficiency criterion, respectively. The control architecture is based on a constrained explicit model predictive control (MPC) law suitable for real-time implementation due to its low computational demands. The proposed controller is implemented and evaluated on a 1.2-kW polymer electrolyte membrane or proton exchange membrane fuel-cell test bench, thus obtaining real data which show that the maximum efficiency criterion does not conflict with the starvation avoidance criterion and allows system performance improvements of up to 3.46%. Moreover, experimental results utilizing the explicit MPC approach also show improved transient responses compared to those of the manufacturer's control law. </para>

Design and implementation of parameterized adaptive cruise control: An explicit model predictive control approach

The combination of different characteristics and situation-dependent behavior cause the design of adaptive cruise control (ACC) systems to be time consuming. This paper presents a systematic approach for the design of a parameterized ACC, based on explicit model predictive control. A unique feature of the synthesized ACC is its parameterization in terms of key characteristics, which, after the parameterization, makes it easy and intuitive to tune, even for the driver. The effectiveness of the design approach is demonstrated using simulations for relevant traffic scenarios, including Stop-&-Go. On-the-road experiments show the proper functioning of the synthesized ACC.

EXPLICIT MODEL PREDICTIVE CONTROL OF A HYBRID SYSTEM USING SUPPORT VECTOR MACHINES

The objective of this work is to study the capabilities of support vector machines for approximating the complex control law arising from model predictive control of a hybrid MIMO-system. By approximating the control law, an explicit formulation can be obtained, which is computationally less intensive for on-line use. The explicit model predictive control approach is applied to a simulated hybrid system consisting of two tanks in series. The system has real-valued, integer-valued, and binary-valued control inputs. A model predictive controller is first designed to control the entire system. This controller is then approximated using support vector machines, with separate approximators for each control input. Reasonable control results were achieved with the approximators.