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Article
Publication date: 11 May 2015

Gonzalo Garcia, Shahriar Keshmiri and Thomas Stastny

Nonlinear model predictive control (NMPC) is emerging as a way to control unmanned aircraft with flight control constraints and nonlinear and unsteady aerodynamics…

Abstract

Purpose

Nonlinear model predictive control (NMPC) is emerging as a way to control unmanned aircraft with flight control constraints and nonlinear and unsteady aerodynamics. However, these predictive controllers do not perform robustly in the presence of physics-based model mismatches and uncertainties. Unmodeled dynamics and external disturbances are unpredictable and unsteady, which can dramatically degrade predictive controllers’ performance. To address this limitation, the purpose of this paper is to propose a new systematic approach using frequency-dependent weighting matrices.

Design/methodology/approach

In this framework, frequency-dependent weighting matrices jointly minimize closed-loop sensitivity functions. This work presents the first practical implementation where the frequency content information of uncertainty and disturbances is used to provide a significant degree of robustness for a time-domain nonlinear predictive controller. The merit of the proposed method is successfully verified through the design, coding, and numerical implementation of a robust nonlinear model predictive controller.

Findings

The proposed controller commanded and controlled a large unmanned aerial system (UAS) with unsteady and nonlinear dynamics in the presence of environmental disturbances, measurement bias or noise, and model uncertainties; the proposed controller robustly performed disturbance rejection and accurate trajectory tracking. Stability, performance, and robustness are attained in the NMPC framework for a complex system.

Research limitations/implications

The theoretical results are supported by the numerical simulations that illustrate the success of the presented technique. It is expected to offer a feasible robust nonlinear control design technique for any type of systems, as long as computational power is available, allowing a much larger operational range while keeping a helpful level of robustness. Robust control design can be more easily expanded from the usual linear framework, allowing meaningful new experimentation with better control systems.

Originality/value

Such algorithms allows unstable and unsteady UASs to perform reliably in the presence of disturbances and modeling mismatches.

Details

International Journal of Intelligent Unmanned Systems, vol. 3 no. 2/3
Type: Research Article
ISSN: 2049-6427

Keywords

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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

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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

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Article
Publication date: 3 October 2016

Ilker Murat Koc, Semuel Franko and Can Ozsoy

The purpose of this paper is to investigate the stability of a small scale six-degree-of-freedom nonlinear helicopter model at translator velocities and angular…

Abstract

Purpose

The purpose of this paper is to investigate the stability of a small scale six-degree-of-freedom nonlinear helicopter model at translator velocities and angular displacements while it is transiting to hover with different initial conditions.

Design/methodology/approach

In this study, model predictive controller and linear quadratic regulator are designed and compared within each other for the stabilization of the open loop unstable nonlinear helicopter model.

Findings

This study shows that the helicopter is able to reach to the desired target with good robustness, low control effort and small steady-state error under disturbances such as parameter uncertainties, mistuned controller.

Originality/value

The purpose of using model predictive control for three axes of the autopilot is to decrease the control effort and to make the close-loop system insensitive against modeling uncertainties.

Details

Aircraft Engineering and Aerospace Technology, vol. 88 no. 6
Type: Research Article
ISSN: 1748-8842

Keywords

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Article
Publication date: 5 September 2016

S. Vahid Naghavi, A.A. Safavi, Mohammad Hassan Khooban, S. Pourdehi and Valiollah Ghaffari

The purpose of this paper is to concern the design of a robust model predictive controller for distributed networked systems with transmission delays.

Abstract

Purpose

The purpose of this paper is to concern the design of a robust model predictive controller for distributed networked systems with transmission delays.

Design/methodology/approach

The overall system is composed of a number of interconnected nonlinear subsystems with time-varying transmission delays. A distributed networked system with transmission delays is modeled as a nonlinear system with a time-varying delay. Time delays appear in distributed systems due to the information transmission in the communication network or transport of material between the sub-plants. In real applications, the states may not be available directly and it could be a challenge to address the control problem in interconnected systems using a centralized architecture because of the constraints on the computational capabilities and the communication bandwidth. The controller design is characterized as an optimization problem of a “worst-case” objective function over an infinite moving horizon.

Findings

The aim is to propose control synthesis approach that depends on nonlinearity and time varying delay characteristics. The MPC problem is represented in a time varying delayed state feedback structure. Then the synthesis sufficient condition is provided in the form of a linear matrix inequality (LMI) optimization and is solved online at each time instant. In the rest, an LMI-based decentralized observer-based robust model predictive control strategy is proposed.

Originality/value

The authors develop RMPC strategies for a class of distributed networked systems with transmission delays using LMI-Based technique. To evaluate the applicability of the developed approach, the control design of a networked chemical reactor plant with two sub-plants is studied. The simulation results show the effectiveness of the proposed method.

Details

COMPEL - The international journal for computation and mathematics in electrical and electronic engineering, vol. 35 no. 5
Type: Research Article
ISSN: 0332-1649

Keywords

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Article
Publication date: 3 January 2017

Hamid Asgari, Mohsen Fathi Jegarkandi, XiaoQi Chen and Raazesh Sainudiin

The purpose of this paper is to develop and compare conventional and neural network-based controllers for gas turbines.

Abstract

Purpose

The purpose of this paper is to develop and compare conventional and neural network-based controllers for gas turbines.

Design/methodology/approach

Design of two different controllers is considered. These controllers consist of a NARMA-L2 which is an artificial neural network-based nonlinear autoregressive moving average (NARMA) controller with feedback linearization, and a conventional proportional-integrator-derivative (PID) controller for a low-power aero gas turbine. They are briefly described and their parameters are adjusted and tuned in Simulink-MATLAB environment according to the requirement of the gas turbine system and the control objectives. For this purpose, Simulink and neural network-based modelling is used. Performances of the controllers are explored and compared on the base of design criteria and performance indices.

Findings

It is shown that NARMA-L2, as a neural network-based controller, has a superior performance to PID controller.

Practical implications

This study aims at using artificial intelligence in gas turbine control systems.

Originality/value

This paper provides a novel methodology for control of gas turbines.

Details

Aircraft Engineering and Aerospace Technology, vol. 89 no. 1
Type: Research Article
ISSN: 1748-8842

Keywords

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Article
Publication date: 12 March 2018

Ning Xian and Zhilong Chen

The purpose of this paper is to simplify the Explicit Nonlinear Model Predictive Controller (ENMPC) by linearizing the trajectory with Quantum-behaved Pigeon-Inspired…

Abstract

Purpose

The purpose of this paper is to simplify the Explicit Nonlinear Model Predictive Controller (ENMPC) by linearizing the trajectory with Quantum-behaved Pigeon-Inspired Optimization (QPIO).

Design/methodology/approach

The paper deduces the nonlinear model of the quadrotor and uses the ENMPC to track the trajectory. Since the ENMPC has high demand for the state equation, the trajectory needed to be differentiated many times. When the trajectory is complicate or discontinuous, QPIO is proposed to linearize the trajectory. Then the linearized trajectory will be used in the ENMPC.

Findings

Applying the QPIO algorithm allows the unequal distance sample points to be acquired to linearize the trajectory. Comparing with the equidistant linear interpolation, the linear interpolation error will be smaller.

Practical implications

Small-sized quadrotors were adopted in this research to simplify the model. The model is supposed to be accurate and differentiable to meet the requirements of ENMPC.

Originality/value

Traditionally, the quadrotor model was usually linearized in the research. In this paper, the quadrotor model was kept nonlinear and the trajectory will be linearized instead. Unequal distance sample points were utilized to linearize the trajectory. In this way, the authors can get a smaller interpolation error. This method can also be applied to discrete systems to construct the interpolation for trajectory tracking.

Details

International Journal of Intelligent Computing and Cybernetics, vol. 11 no. 1
Type: Research Article
ISSN: 1756-378X

Keywords

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Article
Publication date: 14 June 2021

Xiaofeng Liu, Jiahong Xu and Yuhong Liu

The purpose of this research on the control of three-axis aero-dynamic pendulum with disturbance is to facilitate the applications of equipment with similar pendulum…

Abstract

Purpose

The purpose of this research on the control of three-axis aero-dynamic pendulum with disturbance is to facilitate the applications of equipment with similar pendulum structure in intelligent manufacturing and robot.

Design/methodology/approach

The controller proposed in this paper is mainly implemented in the following ways. First, the kinematic model of the three-axis aero-dynamic pendulum is derived in state space form to construct the predictive model. Then, according to the predictive model and objective function, the control problem can be expressed a quadratic programming (QP) problem. The optimal solution of the QP problem at each sampling time is the value of control variable.

Findings

The trajectory tracking and point stability tests performed on the 3D space with different disturbances are validated and the results show the effectiveness of the proposed control strategy.

Originality/value

This paper proposes a nonlinear unstable three-axis aero-dynamic pendulum with less power devices. Meanwhile, the trajectory tracking and point stability problem of the pendulum system is investigated with the model predictive control strategy.

Details

Assembly Automation, vol. 41 no. 3
Type: Research Article
ISSN: 0144-5154

Keywords

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Article
Publication date: 6 July 2015

Mohammad Ghesmat and Akbar Khalkhali

There are high expectations for reliability, safety and fault tolerance are high in chemical plants. Control systems are capable of potential faults in the plant…

Abstract

Purpose

There are high expectations for reliability, safety and fault tolerance are high in chemical plants. Control systems are capable of potential faults in the plant processing systems. This paper proposes is a new Fault Tolerant Control (FTC) system to identify the probable fault occurrences in the plant.

Design/methodology/approach

A Fault Diagnosis and Isolation (FDI) module has been devised based on the estimated state of system. An Unscented Kalman Filter (UKF) is the main innovation of the FDI module to identify the faults. A Multi-Sensor Data Fusion algorithm is utilized to integrate the UKF output data to enhance fault identification. The UKF employs an augmented state vector to estimate system states and faults simultaneously. A control mechanism is designed to compensate for the undesirable effects of the detected faults.

Findings

The performance of the Nonlinear Model Predictive Controller (NMPC) without any fault compensation is compared with the proposed FTC scheme under different fault scenarios. Analysis of the simulation results indicates that the FDI method is able to identify the faults accurately. The proposed FTC approach facilitates recovery of the closed loop performance after the faults have been isolated.

Originality/value

A significant contribution of the paper is the design of an FTC system by using UKF to estimate faults and enhance the accuracy of data. This is done by applying a data fusion algorithm and controlling the system by the NMPC after eliminating the effects of faults.

Details

COMPEL: The International Journal for Computation and Mathematics in Electrical and Electronic Engineering, vol. 34 no. 4
Type: Research Article
ISSN: 0332-1649

Keywords

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Article
Publication date: 4 December 2017

Halim Merabti and Khaled Belarbi

Rapid solution methods are still a challenge for difficult optimization problems among them those arising in nonlinear model predictive control. The particle swarm…

Abstract

Purpose

Rapid solution methods are still a challenge for difficult optimization problems among them those arising in nonlinear model predictive control. The particle swarm optimization algorithm has shown its potential for the solution of some problems with an acceptable computation time. In this paper, we use an accelerated version of PSO for the solution of simple and multiobjective nonlinear MBPC for unmanned vehicles (mobile robots and quadcopter) for tracking trajectories and obstacle avoidance. The AµPSO-NMPC was applied to control a LEGO mobile robot for the tracking of a trajectory without and with obstacles avoidance one.

Design/methodology/approach

The accelerated PSO and the NMPC are used to control unmanned vehicles for tracking trajectories and obstacle avoidance.

Findings

The results of the experiments are very promising and show that AµPSO can be considered as an alternative to the classical solution methods.

Originality/value

The computation time is less than 0.02 ms using an Intel Core i7 with 8GB of RAM.

Details

World Journal of Engineering, vol. 14 no. 6
Type: Research Article
ISSN: 1708-5284

Keywords

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