Search results

1 – 10 of 119
To view the access options for this content please click here
Article
Publication date: 4 January 2016

Rui Dou and Haibin Duan

The purpose of this paper is to propose a novel concept of model prediction control (MPC) parameter optimization method, which is based on pigeon-inspired optimization…

Abstract

Purpose

The purpose of this paper is to propose a novel concept of model prediction control (MPC) parameter optimization method, which is based on pigeon-inspired optimization (PIO) algorithm, with the objective of optimizing the unmanned air vehicles (UAVs) controller design progress.

Design/methodology/approach

The PIO algorithm is proposed for parameter optimization in MPC, which provides a new method to get the optimal parameter.

Findings

The PIO algorithm is a new swarm optimization method, which consists of two operators, so it can be better adapted for the optimal problems. The comparative consequences results with the particle swarm optimization (PSO) demonstrate the effectiveness of the PIO algorithm, and the superiority for global search is also verified in various cases.

Practical implications

PIO algorithm can be easily applied to practice and help the parameter optimization of the MPC.

Originality/value

In this paper, we first present the concept of using the PIO algorithm for parameter optimization in MPC so as to achieve the global best optimization. By using the PIO algorithm, the choice of the parameter could be easier and more effective. The authors also applied the algorithm to the designing of the MPC controller to optimize the response of the pitch rate of UAV.

Details

Aircraft Engineering and Aerospace Technology: An International Journal, vol. 88 no. 1
Type: Research Article
ISSN: 0002-2667

Keywords

To view the access options for this content please click here
Article
Publication date: 9 August 2021

Md Tariquzzaman, Md Habibullah and Amit Kumer Podder

Maintaining a balanced neutral point, reducing power loss, execution time are important criteria for the controlling of neutral point clamped (NPC) inverter. However, it…

Abstract

Purpose

Maintaining a balanced neutral point, reducing power loss, execution time are important criteria for the controlling of neutral point clamped (NPC) inverter. However, it is tough to meet all the challenges and also supplying the load current within the harmonic limit. This paper aims to maintain load current quality within the Institute of Electrical and Electronics Engineers 519 standard and meet the above-mentioned challenges.

Design/methodology/approach

The output load current of a three-level simplified neutral point clamped (3 L-SNPC) inverter is controlled in this paper using model predictive control (MPC). The 3 L-SNPC inverters is considered because fewer semiconductor devices are used in this topology; this will enhance the reliability of the system. MPC is used as a controller because it can handle the direct current-link capacitors’ voltage balancing problem in a very intuitive way. The proposed 3 L-SNPC yields similar current total harmonic distortion (THD), transient and steady-state responses, voltage stress and over current protection capability as the conventional NPC inverter. To reduce the computational burden of the proposed SNPC system, two simplified MPC strategies are proposed, namely, single voltage vector prediction-based MPC and selective voltage vector prediction-based MPC.

Findings

The system shows a current THD of 2.33% at 8.96 kHz. The overall loss of the system is reduced significantly to be useful in medium power applications. The required execution times for the simplified MPC strategies are tested on the hardware dSPACE 1104 platform. It is found that the single voltage vector prediction-based MPC and the selective voltage vector prediction-based MPC are computationally efficient by 8.28% and 62.9%, respectively, in comparison with the conventional MPC-based conventional NPC system.

Originality/value

Multiple system constraints are considered throughout the paper and also compare the SNPC to the conventional NPC inverter. Proper current tracking, over-current protection, overall power loss reduction especially switching loss and maintaining capacitor voltages balance at a neutral point are achieved. The improvement of execution time has also been verified and calculated using hardware-in-loop of the dSPACE DS1104 platform.

Details

World Journal of Engineering, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1708-5284

Keywords

To view the access options for this content please click here
Article
Publication date: 18 October 2011

Endra Joelianto, Edwina Maryami Sumarjono, Agus Budiyono and Dini Retnaning Penggalih

The purpose of this paper is to investigate the feasibility of controlling a small‐scale helicopter by using the model predictive control (MPC) method.

Abstract

Purpose

The purpose of this paper is to investigate the feasibility of controlling a small‐scale helicopter by using the model predictive control (MPC) method.

Design/methodology/approach

The MPC control synthesis is employed by considering five linear models representing the flight of a small‐scale helicopter from hover to high‐speed cruise. The internal model principle is employed for the trajectory tracking design.

Findings

It is found that the MPC handles well the transition problems between the models, yields satisfactory tracking control performance and produces a suitable control signal. The performance of the tracking control of the helicopter is considerably influenced by the parameter selection in the states and inputs weighting matrices of the MPC. Simulation results also showed that faster dynamics, coupling problems, input and output constraints and changing linearized multi‐inputs multi‐outputs dynamics models in the small‐scale helicopter can be handled simultaneously by the MPC controller.

Research limitations/implications

The present study is limited for the application of MPC for the control of small‐scale helicopters with non‐aggressive maneuvers.

Practical implications

The result can be extended to design a full envelope controller for an autonomous small‐scale helicopter without the need to resort to a conventional gain scheduling technique.

Originality/value

Helicopter control system designs using MPC with a single either linear or non‐linear model have been studied and reported in numerous literatures. The main contribution of the paper is in the application of MPC to handle the control problems of a small‐scale helicopter defined as a mathematical model with several different modes during a flight mission.

Details

Aircraft Engineering and Aerospace Technology, vol. 83 no. 6
Type: Research Article
ISSN: 0002-2667

Keywords

To view the access options for this content please click here
Article
Publication date: 22 August 2008

Ben Nasr Hichem and M'Sahli Faouzi

The paper aims to present a new concept based on a multi‐agent approach in the area of nonlinear model predictive control (MPC) for fast systems.

Abstract

Purpose

The paper aims to present a new concept based on a multi‐agent approach in the area of nonlinear model predictive control (MPC) for fast systems.

Design/methodology/approach

A contribution to decentralized implementation of MPC is made. The control of the nonlinear system subject to constraints is achieved via a set of actions taken from different agents. The actions are based on an analytical solution and a neural network is used to monitor the closed system using a supervisory loop concept.

Findings

The high online computational need to solve an optimal control actions in nonlinear MPC, which results in a non‐convex optimization, is compared with the new proposed concept. Simulation results show that this approach has very remarkable performances in time computing and target arrival.

Research limitations/implications

In practice, each MPC problem of the individual agent in multi‐agent MPC can run in parallel at the same time, instead of in serial, one agent after another. A parallel processor can be useful for real time implementation. However, it is estimated that how much time can be gained by performing the computations in parallel instead of in serial.

Practical implications

The proposed concept discussed in the paper has the potential to be applied to systems with rapid dynamics.

Originality/value

The multi‐agent MPC compares favorably with respect to a numerical optimization routine and also offers a solution for non‐convex optimization problems in single‐input single‐output systems.

Details

International Journal of Intelligent Computing and Cybernetics, vol. 1 no. 3
Type: Research Article
ISSN: 1756-378X

Keywords

To view the access options for this content please click here
Article
Publication date: 19 September 2018

Weilin Yang, Wentao Zhang, Dezhi Xu and Wenxu Yan

Robotic arm control is challenging due to the intrinsic nonlinearity. Proportional-integral-derivative (PID) controllers prevail in many robotic arm applications. However…

Abstract

Purpose

Robotic arm control is challenging due to the intrinsic nonlinearity. Proportional-integral-derivative (PID) controllers prevail in many robotic arm applications. However, it is usually nontrivial to tune the parameters in a PID controller. This paper aims to propose a model-based control strategy of robotic arms.

Design/methodology/approach

A Takagi–Sugeno (T-S) fuzzy model, which is capable of approximating nonlinear systems, is used to describe the dynamics of a robotic arm. Model predictive control (MPC) based on the T-S fuzzy model is considered, which optimizes system performance with respect to a user-defined cost function.

Findings

The control gains are optimized online according to the real-time system state. Furthermore, the proposed method takes into account the input constraints. Simulations demonstrate the effectiveness of the fuzzy MPC approach. It is shown that asymptotic stability is achieved for the closed-loop control system.

Originality/value

The T-S fuzzy model is discussed in the modeling of robotic arm dynamics. Fuzzy MPC is used for robotic arm control, which can optimize the transient performance with respect to a user-defined criteria.

Details

Assembly Automation, vol. 38 no. 5
Type: Research Article
ISSN: 0144-5154

Keywords

To view the access options for this content please click here
Article
Publication date: 6 March 2017

Halit Firat Erdogan, Ayhan Kural and Can Ozsoy

The purpose of this paper is to design a controller for the unmanned aerial vehicle (UAV).

Abstract

Purpose

The purpose of this paper is to design a controller for the unmanned aerial vehicle (UAV).

Design/methodology/approach

In this study, the constrained multivariable multiple-input and multiple-output (MIMO) model predictive controller (MPC) has been designed to control all outputs by manipulating inputs. The aim of the autopilot of UAV is to keep the UAV around trim condition and to track airspeed commands.

Findings

The purpose of using this control method is to decrease the control effort under the certain constraints and deal with interactions between each output and input while tracking airspeed commands.

Originality/value

By using constraint, multivariable (four inputs and seven outputs) MPC unlike the relevant literature in this field, the UAV tracked airspeed commands with minimum control effort dealing with interactions between each input and output under disturbances such as wind.

Details

Aircraft Engineering and Aerospace Technology, vol. 89 no. 2
Type: Research Article
ISSN: 1748-8842

Keywords

To view the access options for this content please click here
Article
Publication date: 11 February 2021

Houda Laabidi, Houda Jouini and Abdelkader Mami

The purpose of this paper is to propose an efficient current control technique based on model predictive control (MPC) for grid-connected wind conversion system. This…

Abstract

Purpose

The purpose of this paper is to propose an efficient current control technique based on model predictive control (MPC) for grid-connected wind conversion system. This nonlinear strategy is applied for the chopper circuit and grid-tied inverter and compared with other two conventional schemes; a traditional proportional-integral (PI) and sliding mode controller (SMC) using the same switching frequency.

Design/methodology/approach

Firstly, the MPC scheme uses the mathematical model to predict future behaviors of the controlled converter outputs for possible switching states. After that, the optimal voltage vector is selected by minimizing a cost function, which is defined as a sum of the absolute values of the controlled current errors. Then, the corresponding switching signals are applied to the converter switches in the next sampling period to track correctly the reference current. Thus, the MPC scheme ensures a minimal error between the predicted and reference trajectories of the considered variables.

Findings

The MPC-based algorithm presents several benefits in terms of high accuracy control, reduced DC-link voltage ripples during steady-state operation, faster transient response, lower overshoots and disturbance rejection and acceptable total harmonic distortion.

Originality/value

The authors introduce several simulation case studies, using PSIM software package, which prove the reliability and effectiveness of the proposed MPC scheme. Therefore, the MPC performances, during dynamic and steady-state condition, are compared with those obtained by a PI regulator and SMC to highlight the improvements, specifically the transfer of smooth power to the grid.

To view the access options for this content please click here
Article
Publication date: 3 August 2020

Ramanjaneyulu Alla and Anandita Chowdhury

A new control method is proposed for grid integration of improved hybrid three quasi z source converter (IHTQZSC). The proposed controller provides a constant switching…

Abstract

Purpose

A new control method is proposed for grid integration of improved hybrid three quasi z source converter (IHTQZSC). The proposed controller provides a constant switching frequency with an improved dynamic response with fewer computations. The proposed constant switching frequency predictive controller (CSF-PC) does not need weighting factors and reduces the complexity of the control circuit.

Design/methodology/approach

A single PI controller is intended to control voltage across dc-link by generating the necessary shoot-through duty ratio. The predictive controller produces the modulating signals required to inject the desired grid current. The performance of the proposed controller is validated with MATLAB/Simulink software.

Findings

The discrete-time instantaneous model on the grid side in the proposed controller influences the inductor current with minimum ripples. Dynamic response and computational complexity of the converter with the PI controller, finite set model predictive controller (FS-MPC) and the proposed controller are discussed.

Practical implications

The converter belongs to impedance source converters (ISC) family, delivers higher voltage gain in a single-stage power conversion process, extract the energy from the intermittent nature of renewable energy conversion systems. Implementing CSF-PC for ISC is simple, as it has a single PI controller.

Originality/value

Grid integration of high voltage gain IHTQZSC is accomplished with PI, FS-MPC and CSF-PC. Though the FS-MPC exhibits superior dynamic response under input voltage disturbance and grid current variation, total harmonic distortion (THD) in the grid current is high. CSF-PC provides better THD with a good dynamic response with reduced inductor current ripples.

Details

COMPEL - The international journal for computation and mathematics in electrical and electronic engineering , vol. 39 no. 4
Type: Research Article
ISSN: 0332-1649

Keywords

To view the access options for this content please click here
Article
Publication date: 3 November 2014

Adel Taeib, Moêz Soltani and Abdelkader Chaari

The purpose of this paper is to propose a new type of predictive fuzzy controller. The desired nonlinear system behavior is described by a set of Takagi-Sugeno (T-S) model

Abstract

Purpose

The purpose of this paper is to propose a new type of predictive fuzzy controller. The desired nonlinear system behavior is described by a set of Takagi-Sugeno (T-S) model. However, due to the complexity of the real processes, obtaining a high quality control with a short settle time, a periodical step response and zero steady-state error is often a difficult task. Indeed, conventional model predictive control (MPC) attempts to minimize a quadratic cost over an extended control horizon. Then, the MPC is insufficient to adapt to changes in system dynamics which have characteristics of complex constraints. In addition, it is shown that the clustering algorithm is sensitive to random initialization and may affect the quality of obtaining predictive fuzzy controller. In order to overcome these problems, chaos particle swarm optimization (CPSO) is used to perform model predictive controller for nonlinear process with constraints. The practicality and effectiveness of the identification and control scheme is demonstrated by simulation results involving simulations of a continuous stirred-tank reactor.

Design/methodology/approach

A new type of predictive fuzzy controller. The proposed algorithm based on CPSO is used to perform model predictive controller for nonlinear process with constraints.

Findings

The results obtained using this the approach were comparable with other modeling approaches reported in the literature. The proposed control scheme has been show favorable results either in the absence or in the presence of disturbance compared with the other techniques. It confirms the usefulness and robustness of the proposed controller.

Originality/value

This paper presents an intelligent model predictive controller MPC based on CPSO (MPC-CPSO) for T-S fuzzy modeling with constraints.

Details

Kybernetes, vol. 43 no. 9/10
Type: Research Article
ISSN: 0368-492X

Keywords

To view the access options for this content please click here
Article
Publication date: 3 January 2017

Piotr Serkies and Krzysztof Szabat

The purpose of this paper is to design and test a linear predictive control algorithm with elements of fuzzy logic in the non-linear speed region of a two-mass system with…

Abstract

Purpose

The purpose of this paper is to design and test a linear predictive control algorithm with elements of fuzzy logic in the non-linear speed region of a two-mass system with a flexible shaft.

Design/methodology/approach

To compensate the non-linearity of friction in the low-speed region, the elements of the Q matrix have been retuned with the use of fuzzy logic. First, the influence of the Q matrix on the dynamics of the drive has been discussed. On the basis of these findings a fuzzy system has been developed.

Findings

It has been demonstrated that applying a relatively simple fuzzy system can reduce unwanted non-linear phenomena in the low-speed region; at the same time, the dynamics of the drive in the other regions is not deteriorated.

Originality/value

The solutions presented in the paper are original and have not been published so far. The influence of non-linear friction on the work of the drive in the low-speed region at different values of the matrix Q has been shown. Also, a novel system of online adjustment of the values of the Q matrix in a predictive speed controller has been introduced. Besides, the system has been compared against the classical predictive regulator.

Details

COMPEL - The international journal for computation and mathematics in electrical and electronic engineering, vol. 36 no. 1
Type: Research Article
ISSN: 0332-1649

Keywords

1 – 10 of 119