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The invention of Unmanned Aerial Vehicle (UAV) in the early 1900 for military purposes and UAV applications in commercial purposes afterwards in the early 2000 accelerate its research in many engineering fields. Multirotor UAV such as quadrotor is usually unstable without a flight controller. Consequently, a proper and accurate model of the UAV dynamics is essential for its system stability. System identification allows the researchers to create an accurate parameter to the mathematical model of a dynamic system based on measured data. This work emphasizes in designing a robust and adaptive filter to develop an accurate mathematical model based on Newton-Euler method which includes aerodynamic drag and moment which are necessary in determining the correct model prediction. The focus of the present work is mainly on Kalman filter development for parameter estimation. While Kalman filter is only efficient in linear problem, an extended version of the filter itself deals with the nonlinear problem in which most real problem is actually nonlinear. This work investigates the performance of the extended version of the Kalman filter relative to parameter estimation. The performance of the filters are evaluated based on their estimation with the actual recorded flight data and presented based on data overlapping using Root Mean Square Error (RMSE). Ardupilot APM is used to acquire the actual flight test data and MATLAB is utilized to carry out the state estimation. To evaluate the performances of the filters, Goodness of Fit (GOF) approach was used. It is found that the GOF index of the present approach is 0.853 which is 25% higher than that of the Robust Extended Kalman Filter approach for the present flight test result.

Purpose – The purpose of this paper is to develop a multiobjective differential evolution (MODE)-based extended H-infinity controller for autonomous helicopter control. Design/methodology/approach – Development of a MATLAB-based MODE suitable for controller synthesis. Formulate the H-infinity control scheme as an extended H-infinity loop shaping design procedure (H ∞ -LSDP) with incorporation of v-gap metric for robustness to parametric variation. Then apply the MODE-based algorithm to optimize the weighting function of the control problem formulation for optimal performance. Findings – The proposed optimized H-infinity control was able to yield set of Pareto-controller candidates with optimal compromise between conflicting stability and time-domain performances required in autonomous helicopter deployment. The result of performance evaluation shows robustness to parameter variation of up to 20 per cent variation in nominal values, and in addition provides satisfactory disturbance rejection to wind distur...

Purpose – The purpose of this paper is to develop a hybrid algorithm using differential evolution (DE) and prediction error modeling (PEM) for identification of small-scale autonomous helicopter state-space model. Design/methodology/approach – In this study, flight data were collected and analyzed; MATLAB-based system identification algorithm was developed using DE and PEM; parameterized state-space model parameters were estimated using the developed algorithm and model dynamic analysis. Findings – The proposed hybrid algorithm improves the performance of the PEM algorithm in the identification of an autonomous helicopter model. It gives better results when compared with conventional PEM algorithm inside MATLAB toolboxes. Research limitations/implications – This study is applicable to only linearized state-space model. Practical implications – The identification algorithm is expected to facilitate the required model development for model-based control design for autonomous helicopter development. Original...

Quadrotor has emerged as a popular testbed for Unmanned Aerial Vehicle (UAV) research due to its simplicity in construction and maintenance, and its vertical take-off, landing and hovering capabilities. It is a flying rotorcraft that has four lift-generating propellers; two of the propellers rotate clockwise and the other two rotate counter-clockwise. This paper presents modeling and system identification for auto-stabilization of a quadrotor system through the implementation of Extended Kalman Filter (EKF). EKF has known to be typical estimation technique used to estimate the state vectors and parameters of nonlinear dynamical systems. In this paper, two main processes are highlighted; dynamic modeling of the quadrotor and the implementation of EKF algorithms. The aim is to obtain a more accurate dynamic modelby identify and estimate the needed parameters for thequadrotor. The obtained results demonstrate the performances of EKF based on the flight test applied to the quadrotor system.

This paper presents parameters estimation techniques for coupled industrial tanks using the mixed Unscented Kalman Filter (UKF) and Differential Evolution (DE) method. UKF have known to be a typical estimation technique used to estimate the state vectors and parameters of nonlinear dynamical systems and DE is one of the most powerful stochastic real-parameter optimization algorithms. Meanwhile, liquid tank systems play important role in industrial application such as in food processing, beverage, dairy, filtration, effluent treatment, pharmaceutical industry, water purification system, industrial chemical processing and spray coating. The aim is to model the coupled tank system using mixed UKF and DE method to estimate the parameters of the tank. First, a non-linear mathematical model is developed. Next, its parameters are identified using mixed Unscented Kalman Filter (UKF) and Differential Evolution (DE) based on the experimental data. DE algorithm is integrated into the UKF algorithm to optimize the Kalman gain obtained. The obtained results demonstrate good performances.

PurposeThe purpose of this paper is to present the synthesis of a robust controller for autonomous small‐scale helicopter hovering control using extended H∞ loop shaping design techniques.Design/methodology/approachThis work presents the development of a robust controller for smooth hovering operation required for many autonomous helicopter operations using H∞ loop shaping technique incorporating the Vinnicombe‐gap (v‐gap) metric for validation of robustness to uncertainties due to parameter variation in the system model. Simulation study was conducted to evaluate the performance of the designed controller for robust stability to uncertainty, disturbance rejection, and time‐domain response in line with ADS‐33E level 1 requirements.FindingsThe proposed techniques for a robust controller exhibit an effective performance for both nominal plant and 20 percent variation in the nominal parameters in terms of robustness to uncertainty, disturbance wind gust attenuation up to 95 percent, and transient performance in compliance with ADS‐33E level 1 specifications.Research limitations/implicationsThe controller is limited to hovering and low‐speed flight envelope.Practical implicationsThis is expected to provide efficient hovering/low‐speed autonomous helicopter flight control required in many civilian unmanned aerial vehicles applications. Also, the technique can be used to simplify the number of robust gain‐scheduled linear controllers required for wide‐envelope flight.Social implicationsThe research will facilitate the deployment of low cost, small‐scale autonomous helicopters in various civilian applications.Originality/valueThe research addresses the challenges of parametric variation inherent in helicopter hovering/low‐speed control using an extended H∞ loop shaping technique with v‐gap metric.

Anti-swing control is a well-known term in gantry crane control. It is designed to move the payload of gantry crane as fast as possible while the payload swing angle should be kept as small as possible at the final position. A number of studies have proposed anti-swing control using the well-known proportional, integral, derivative (PID) control method. However, PID controllers cannot always effectively control systems with changing parameters. Some studies have also proposed intelligent-based control including fuzzy control. However, the designers often have to face the problem of tuning many parameters during the design to obtain optimum performance. Thus, a lot of effort has to be taken in the design stage. In this paper Fuzzy-tuned PID controller design for anti-swing gantry crane control is presented. The objective is to design a practical anti-swing control which is simple in the design and also robust. The proposed Fuzzy-tuned PID utilizes fuzzy system as PID gain tuners to achieve robust performance to parameters’ variations in the gantry crane. A complex dynamic analysis of the system is not needed. PID controller is firstly optimized in MATLAB using a rough model dynamic of the system which is identified by conducting a simple open-loop experiment. Then, the PID gains are used to guide the range of the fuzzy outputs of the Fuzzy-tuned PID controllers. The experimental results show that the proposed anti-swing controller has satisfactory performance. In addition, the proposed method is straightforward in the design.

This paper presents a method of robust control system design for aircraft flight control systems with the existence of uncertainty parameters such as stability and control derivatives resulted by parameter identification process using real flight test data. A nonlinear model of aircraft turn coordination system is considered where the problem is to find the optimal controller by using the H∞ optimal control method combined with a quasi-Newton optimization method. The present method designs the gain of the cross pass channel for the aileron and rudder interconnect, which causes nonlinearity of the system. The robust controller design is qualified as designing the H∞ optimal control under a given value of the 'nonlinear' gain, and sequentially determining the optimal 'nonlinear' gain subject to minimize the error performance of the turn coordination system by using the quasi-Newton method. The perturbations of the uncertainties model have structured uncertainties constructed by the differences between the aerodynamic coefficients derived by parameter identification process and wind tunnel result. The proposed method is applied to the real flight test data of N250 PA-1 aircraft.

The rapid-prototyping and deployment of flight control algorithm on a real-time embedded target has been one of the major challenges in an unmanned helicopter system development. In this paper, a MATLAB-Simulink based embedded target programming approach is presented to address this challenge. The motivation for this approach is that, since most of the computational tasks in the unmanned helicopter system, such as modeling, controller design and simulation are readily and effectively achieved within the MATLAB environment, then, extending the platform to deployment of the resulting flight algorithm onto an embedded target would greatly ease the overall developmental activities. The design and deployment of an H ∞ based flight control algorithm onto an embedded target is presented for a small scale unmanned helicopter system. The results of the hardware-in-loop simulation and real-flight tests show effectiveness of the proposed approach. This is expected to facilitate and contribute towards a single-platform integrated development environment for unmanned system development.

This paper presents the application of the moving horizon states estimation (MHSE) method to estimate the states of nonlinear aircraft equation of motion from a dynamic maneuver’s flight test data. To determine the optimum solution of minimizing the performance index, a Quasi-Newton or gradient method is used. The present method also uses the Armijo’s line search gradient to guarantee the solution/estimation to converge faster to the global optimum estimate. The MHSE method is applied to flight test data of N250 PA-1 aircraft for parameter identification and flight path reconstruction. The result of estimation is also used to evaluate the accuracies of the measurement systems.

The conflict between the transient performance of the link position and tip vibration in a flexible link system has made the control of such system a challenging task. The system is required to obtain a fast transient position response together with minimal tip vibration. This can be viewed like many other real-life control problems as a multi-objectives optimization problem in which an optimal compromise between the design objectives is required. PID controller is noted with historical simplicity in terms of design and implementation when compares to other linear time invariant (LTI) control techniques. However, the shortcoming of PID lies in the tuning of the controller gains for a given problem. To overcome this, a Multiobjective Differential Evolution (MODE)-based PID controller is reported in this study for controlling a flexible link system. The gains of the PID controller are tuned using a developed MATLAB-based MODE to obtain pareto-solutions for both link position and tip vibration. The performance of the selected best PID controller from MODE-based design is benchmarked with the LQR controller provided by the manufacturer (QUANSER) of the laboratory scale flexible link plant. Though, the LQR shows better transient performance in the position responses, the developed MODE-PID gave better tip response performances as indicated in both the simulation and experimental responses obtained.

The three degree of freedom (3DOF) helicopter is a simplified model, which is used to study the behaviours of the helicopters. It is an example of a so-called underactuated mechanical system that has a fewer control inputs than degrees of freedom. Application of multiple-surface sliding mode control to such model looks attractive, because of the ability of this method to stabilize the underatuated systems and to deal with uncertainties, which usually exist in such systems. In this paper, a multiple-surface sliding mode is proposed to control the elevation and travel angles of the 3DOF helicopter.

In this paper, this design was a Nonlinear Energy-based Control Method (NEM) controller for a Three degree-of-freedom (3 DOF) Helicopter developed by Quanser. It is a control design technique that exploits the energy state of a system to achieve stabilization and/or tracking. There are four stages to design NEM controller, which are: energy-based modeling of helicopter dynamics using Euler-Lagrange formalism; design of the NEM controller based on passivity principle and Lyapunov stability idea; tuning the controller gain(s) to satisfy the design criteria and robustness analysis.

This paper describes one of possible parameter identification approach for a quadrotor. The unknown parameter of the quadrotor will be identified using state estimation method with the implementation of Unscented Kalman Filter (UKF). In the identification of state and parameter for nonlinear dynamic system, UKF has grown to be superior techniques. Two main processes highlighted in this paper are dynamic modeling of quadrotor and the implementation of UKF algorithm. The aim is to identify and estimate the needed parameters for an autonomous quadrotor. The obtained results demonstrate the performance of UKF based on the flight test applied to the quadrotor system.

This paper presents a robust feedback controller design for parametric uncertainty systems via constrained optimization. The proposed controller design employs modern optimization tools (Particle Swarm Optimization and Differential Evolution) in a single objective constrained optimization. The objective of the optimization is to search for a set of robust controller gains such that the stability radius of the closed-loop system is maximized. The constraint of the optimization is a closed-loop poles region which is directly related to the desired time-domain control performance. The proposed controller design is applied to control of pendulum-like systems (gantry crane, flexible joint and inverted pendulum). The results show the robust performance of the designed controller.

One of the major problems associated with H-infinity controller design is the challenges of weighting function parameters' tuning. For a given control problem, it is requires to select appropriate weighting function parameters to satisfy both the stability requirement and time domain specifications. The conflicting nature of these objectives has made such design exercise a tedious endeavor. In this study, the optimal-tuning of the H-infinity controller parameters using multi-objective differential evolution (MODE) technique is proposed, and apply for control of a two-mass-spring system which represents a typical benchmark robust control problem. The performance of the resulting robust controller is evaluated both on the nominal plant and perturbed plants with parameter variation. The simulation results indicate a good compromise between the robustness and time domain performance.

Computational intelligence has been successfully applied to many engineering applications including control engineering problems. In this paper, a robust state feedback control design using particle swarm optimizer based constrained optimization is proposed. The feedback controller is optimized based on state space model of the plant with structured uncertainty such that the closed-loop system would have maximum stability radius. A wedge region is assigned as a constraint to locate the desired closed-loop poles, which correspond to time-domain control system performance. The proposed controller design is applied to anti-swing control for a gantry crane system. The experimental result is shown, and comparison with that of conventional linear quadratic regulator based controller and H ∞ loop shaping controller are made. The proposed method achieves a satisfactory robust performance based on the structured singular value analysis and the experimental results.

In this paper, the state feedback control design problem for tracking control of a flexible link manipulator is considered. The calculation of state feedback control gains is conventionally handled by pole placement method or LQR method via Riccati equation. Unfortunately, they still possess trial and error approach of choosing some parameters. Particularly, choosing elements of Q and R matrices in the state feedback control using LQR method has to be done by trial. Therefore, an intelligent method to resolve this problem is proposed by adopting PSO algorithm. The experimental work is carried out to evaluate the effectiveness of the proposed method.

PID (proportional+integral+derivative) control is known as simple and easy-to-implement controller. However, the robustness performance is often not satisfactory when dealing with parameter variations of the plant. In addition, its design procedure is not straightforward for the system which is non-SISO (Singe Input Single Output) system like a gantry crane. In this paper, a stable robust PID controller for anti-swing control of automatic gantry crane is proposed. The proposed method employs Genetic Algorithm (GA) in min-max optimization to find the stable robust PID. In the optimization, the robustness of the controller is tested using Kharitonov's polynomials robust stability criterion to deal with parametric uncertainty appears in gantry crane model. For practicality, the model is identified by conducting simple open-loop experiment in the beginning. The experimental results show that a satisfactory robust PID control performance can be achieved. The controller is able to effectively move the trolley of the crane in short time while canceling the swing of the payload for different conditions of payload mass and cable length variations.

Robust control has been studied in recent years as an efficient methodology for the design of highly performing controllers of positioning systems. However, it is necessary to provide accurate nominal model and associated uncertainty bounds to design this type of controllers. The aim of this paper is to robustly identify a single axis high precision positioning system with uncertainties. For a given data set a nominal model is identified then model error modeling techniques are used to handle uncertainties in modeling. To examine the quality of the identified nominal and modeling error models H ∞ is designed based on the identified uncertainties. Computer simulation and experimenting with the high precision positioning system verify that the obtained nominal model and modeling error model are reliable and the implemented approach can be used to develop an integrated identification and robust controller.

In this paper, a novel method for tuning PID controller of automatic gantry crane control using particle swarm optimization (PSO) is proposed. PSO is one of the most recent optimization techniques based on evolutionary algorithm. PSO is also known as computationally efficient method. This work presents in detail how to apply PSO method in finding the optimal PID gains of gantry crane system in the fashion of min-max optimization. The simulation results show that with proper tuning a satisfactory PID control performance can be achieved to drive nonlinear plant. The controller is able to effectively move the trolley of the crane in short time while canceling the swing angle of the payload hanging on the trolley at the end position. The robustness of the controller is also tested.

In this paper, a robust state feedback control design using particle swarm optimisation-based constrained optimisation is proposed. The feedback controller is designed based on state space model of the plant with structured uncertainty such that the closed-loop system would have maximum stability radius. A wedge region is assigned as a constraint to locate desired closed loop poles. The proposed method is applied into design of anti-swing control of an automatic gantry crane experiment. A comparison with that of LQR-based controller is made. The result shows that the proposed method effectively locates the closed loop poles within the prescribed wedge region and its robust performance is guaranteed.