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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.

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.

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 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.

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.