Twin Rotor MIMO System Research Articles

An LMI approach to robust fault estimation for a class of nonlinear systems

The paper presents a robust fault estimation approach for a class of nonlinear discrete-time systems. In particular, two sources of uncertainty are present in the considered class of systems, that is, an unknown input and an exogenous external disturbance. Thus, apart from simultaneous state and fault estimation, the objective is to decouple the effect of an unknown input while minimizing the influence of the exogenous external disturbance within the framework. The resulting design procedure guarantees that a prescribed disturbance attenuation level is achieved with respect to the state and fault estimation error while assuring the convergence of the observer. The core advantage of the proposed approach is its simplicity by reducing the fault estimation problem to matrix inequalities formulation. In addition, the design conditions ensure the convergence of the observer with guaranteed performance. The effectiveness of the proposed approach is demonstrated by its application to a twin rotor multiple-input multiple-output system. Copyright © 2015 John Wiley & Sons, Ltd.

Modelling of Twin Rotor MIMO System

The paper deals with modelling of a Twin Rotor MIMO System – a laboratory model constructed by Feedback Instruments Ltd. The system consists of a two rotors which resembles a simple helicopter. The non-stationary part of the model can rotate around two perpendicular axes to produce azimuth and elevation output of the system. These outputs are affected by speed of the rotors. Significant cross-coupling can be observed and the system behaviour is nonlinear. A model based on first–principle modelling was derived and is parameters were refined by identification based on real-time experiments. Resulting model was designed in MATLAB/Simulink environment and can serve for control design.

Multiple Sliding Surface Control Approach to Twin Rotor MIMO Systems

In this paper, a multiple sliding surface (MSS) controller for a twin rotor multi-input-multioutput system (TRMS) with mismatched model uncertainties is proposed. The nonlinear terms in the model are regarded as model uncertainties, which do not satisfy the standard matching condition, and an MSS control technique is adopted to overcome them. In order to control the position of the TRMS, the system dynamics are pseudo-decomposed into horizontal and vertical subsystems, and two MSSs are separately designed for each subsystem. The stability of the TRMS with the proposed controller is guaranteed by the Lyapunov stability theory. Some simulation results are given to verify the proposed scheme, and the real time performances of the TRMS with the MSS controller show the effectiveness of the proposed controller.

Output Feedback Linearization Based Controller for a Helicopter-like Twin Rotor MIMO System

In this paper, an output feedback linearization based controller is designed to stabilize the Twin Rotor Multi-input Multi-output System (TRMS), and make its beam track accurately a reference signal, or reach desired positions in 2 DOF. Only yaw and pitch angles are considered available for measurement. An observer, to estimate the remaining states, is coupled with feedback linearization technique in a two-stage procedure. In the first stage, propellers thrusts are considered as virtual control inputs that lead to a TRMS canonical model for which feedback linearization is applied straightforwardly. In the second stage, the motors torques and actual control input voltages are computed, respectively, by solving algebraic equations and inverting motors models. In the proposed approach, the coupling effects are maintained in controller derivation and so there is no need to decouple the TRMS into horizontal and vertical subsystems, as usually done in the literature. Exponential stability of the closed loop is guaranteed by using the second method of Lyapunov. To show the performance and the effectiveness of the proposed controller, simulation results are presented.

Model reference adaptive control for twin rotor multiple-input and multiple-output system via minimal controller synthesis

A model reference adaptive controller for a twin rotor multiple-input multiple-output system is considered in this article. The objective is to make the twin rotor multiple-input multiple-output system move quickly and accurately to the desired attitudes specified by a reference model. Because of the coupling influence between the two axes of the twin rotor multiple-input multiple-output system and its nonlinear complexity, the controller design is performed on the vertical plane and horizontal plane separately. Thus, the nonlinear multiple-input and multiple-output model of the twin rotor multiple-input multiple-output system is decoupled into two subsystems, and the cross-couplings are considered as disturbances to each other. The obtained two models are transformed via Lie derivatives to a canonical form required for adaptive controller design. Then, a hyperstability-based adaptive control technique is applied for each subsystem. The proposed controller design is evaluated in simulations of cross-coupled condition. The obtained results show that the controlled system is robust against disturbances with high tracking performance.

Robust Control of Twin Rotor MIMO System

This paper deals with robust control for a nonlinear multi-input multi-output system called the twin rotor MIMO system (TRMS). The mathematical modeling of helicopter model is simulated using MATLAB/Simulink for controlling the system in elevation and azimut angles using an H∞ robust control with a new method of optimization based on a genetic algorithm (GA) technique. The basic idea is to make handle by the genetic algorithm to optimize the weight functions. Obtained simulation results for different input signals are encouraging and the method is more effective than the standard techniques.

Type-2 fuzzy logic control of a 2-DOF helicopter (TRMS system)

AbstractThe helicopter dynamic includes nonlinearities, parametric uncertainties and is subject to unknown external disturbances. Such complicated dynamics involve designing sophisticated control algorithms that can deal with these difficulties. In this paper, a type 2 fuzzy logic PID controller is proposed for TRMS (twin rotor mimo system) control problem. Using triangular membership functions and based on a human operator experience, two controllers are designed to control the position of the yaw and the pitch angles of the TRMS. Simulation results are given to illustrate the effectiveness of the proposed control scheme.

H∞BASEDOBSERVER FOR DISTURBANCE COMPENSATION IN DECOUPLED TRMS USING LMI OPTIMIZATION

Twin Rotor MIMO System is a laboratory model of helicopter. In this paper, the problem of disturbance rejection in TRMS is dealt with. Using disturbance observers, without any additional sensors is an attractive method to attenuate the effects of disturbances as they are highly cost effective. This method uses a simple form of DOBs, which does not need to solve the plant model inverse, and uses H∞control method using LMIs to design the Q-filter in the DOB. The estimation capability of DOB is verified using simulation results in frequency domain as well as in time domain.

Controlling the Pitch and Yaw Angles of Twin Rotor MIMO System in Simulation-Based Platform using Fuzzy Logic Controller and PID Controller

In this paper, the PID and fuzzy logic controllers are designed to control the pitch and yaw angles of a twin rotor MIMO system (TRMS) in simulation-based platform. Two controllers were used in each case, one for pitch angle and one for yaw angle. The twin rotor MIMO system designed as decoupling system in simplification. The tuned controllers gave a very good response in the simulation. These results will provide a solid base for designing the final optimized real-time controller in the next stages of the research.

Adaptive Backstepping Control for A Twin Rotor MIMO System

This paper addresses the attitude control problem for a twin rotor MIMO system (TRMS). The complexity of the control problem lies in significant nonlinear cross-coupling between the main and tail rotors. The uncertainty in system parameters further adds to the complexity of the control problem. An adaptive backstepping controller is designed based on the cascaded structure of the TRMS mathematical model. It is shown using Lyapunov stability analysis that the proposed adaptive update laws make the closed loop system stable. A hybrid EKF-Luenberger type observer has also been developed to estimate the states of the TRMS. Simulation and experimental results confirm efficacy of the developed backstepping controller.

Real-Time Implementation of Neuro Adaptive Observer-Based Robust Backstepping Controller for Twin Rotor Control System

In this paper, a robust backstepping controller based on the neuro adaptive observer for the twin rotor multiple-input-multiple-output (MIMO) system is designed and implemented in real time. The twin rotor MIMO system (TRMS) belongs to a class of nonlinear uncertain system having unstable, coupled dynamics. Nonlinearities of the TRMS are estimated using Chebyshev neural network. A tuning scheme based on Lyapunov theory of stability is developed which can guarantee the boundedness of tracking error and weights of the neural network. The proposed observer-based control guarantees the stability of the closed-loop adaptive system and the tracking errors converge to small residual sets in the presence of constraints on the control input. The effectiveness of the proposed observer-based robust controller is illustrated through simulation and experimental results. The real time implementation has been carried out on the real-time TRMS using MATLAB real-time tool box and Advantech PCI1711 card.

Adaptive Active Force Control Application to Twin Rotor Mimo System

This paper reports a current study on modeling and simulation of adaptive Active Force Control (AFC) based scheme embedded with an artificial neural network (ANN) and/or fuzzy logic (FL) in response manipulations of the twin rotor multi-input multi-output (MIMO) system (TRMS). TRMS is well known for its non-linear behaviour and common classical control scheme such as Proportional-Integral-Derivative (PID) would not be adequate to compensate disturbances. The disturbances in this case were as the results of non-linear external and internal parametric changes, namely angular momentum and couple reactions between the two axes of TRMS. The adaptive control algorithm was proposed in both pitch and yaw to generate an optimum control gain for both responses, simulated viz. MATLAB/SIMULINK software Package. The ANN and FL were integrated into the scheme and act as optimum control algorithm in catalyzing the performance of the TRMS. The results from hybrid conditions of PID-AFC, PID-AFC-ANN and PID-AFC-FL respectively were observed and analyzed. From performance evaluation, PID-AFC-FL scheme has demonstrated a potentially robust and effective manipulating capability in trajectory tracking.

DESIGN AND REALIZATION OF A HYBRID INTELLIGENT CONTROLLER FOR A TWIN ROTOR MIMO SYSTEM

An intelligent control scheme using a fuzzy switching mechanism, grey prediction and genetic algorithm (GA) is applied to a coupled nonlinear system, called a twin rotor multi-input multi-output system (TRMS). In real-time control, a Xilinx Spartan II SP200 FPGA (Field Programmable Gate Array) is employed to construct a hardware-in-the-loop system through writing VHDL on this FPGA. The objective is to stabilize the TRMS in significant cross-coupled conditions, and to experiment with setpoint control and trajectory tracking. The proposed scheme improves the performance of the PID controller. Control gains and parameters of the fuzzy switching mechanism are obtained by GA. Simulation results show that the proposed approach results in better performance than is achieved by conventional PID control, GA-PID control, CMAC, or fuzzy control.

Quasi-LPV modeling, identification and control of a twin rotor MIMO system

This paper describes the quasi-linear parameter varying (quasi-LPV) modeling, identification and control of a Twin Rotor MIMO System (TRMS). The non-linear model of the TRMS is transformed into a quasi-LPV system and approximated in a polytopic way. The unknown model parameters have been calibrated by means of the non-linear least squares identification approach and validated against real data. Finally, an LPV state observer and state-feedback controller have been designed using an LPV pole placement method based on LMI regions. The effectiveness and performance of the proposed control approach have been proved both in simulation and on the real set-up.

Robust adaptive fuzzy control of twin rotor MIMO system

This paper considers designing an adaptive fuzzy controller to position the yaw and pitch angles of a twin rotor MIMO system (TRMS) in two degrees of freedom. The goal of the controller is to stabilize the TRMS in a desired position or track a specified trajectory. The parameters of the fuzzy controller are updated using the gradient descent algorithm in order to increase its robustness against external disturbances and/or changes in system parameters. Moreover, the stability of the overall closed-loop system is guaranteed based on the Lyapunov stability theory. The proposed controller is applied to a TRMS with heavy cross coupling between its axes. Experimental results show good performance of the proposed controller as compared to the non-adaptive fuzzy and PID controllers, especially when there are system uncertainties and external disturbances.

System Identification and H∞ Observer Design for TRMS

A dynamic model for the two-degree-of-freedom Twin Rotor MIMO System (TRMS) is extracted using a blackbox system identification technique. Its behaviour in certain aspects resembles that of a helicopter, with a significant cross coupling between longitudinal and lateral directional motions. Hence, it is an interesting identification and control problem. Using the extracted model an H∞ observer is designed which will estimate the states of the system in presence of worst case noise assumed to be impact on the system.

Real-time implementation of state observers for twin rotor MIMO system: an experimental evaluation

This paper presents an experimental evaluation of three different kinds of real-time state observers for twin rotor multiple-input-multiple-output (MIMO) system. The twin rotor MIMO system (TRMS) exemplifies a high order non-linear system with significant cross couplings. Based on the experimental results a comparative analysis of Chebyshev neural network-based observer and sliding mode observer with local state observer is presented. Finally, the robustness issues are also addressed.

Adaptive second-order sliding mode controller for a twin rotor multi-input–multi-output system

In this study, an adaptive second-order sliding mode (SOSM) controller is proposed to control a laboratory helicopter called the twin-rotor multi-input–multi-output system (TRMS). The design objectives of the controller are to stabilise the TRMS in significant cross couplings, reach a desired position and accurately track a specified trajectory. The TRMS model is divided into a horizontal and a vertical subsystem (VS). The cross coupling existing between the two subsystems is considered as the system uncertainty. A simple adaptive tuning law is developed for the SOSM controller to deal with the bounded system uncertainty. The major advantage offered by this adaptive SOSM controller is that advance knowledge about the upper bound of system uncertainty is not a necessary requirement. The adaptive SOSM controller for the VS uses a proportional plus integral sliding surface to counter the offset present in the pitch angle. System robustness and the stability of the controller are proved by using the Lyapunov criterion. Apart from imparting robustness, the proposed adaptive SOSM controller reduces undesired chattering in the control input and thus is suitable for application in practical motion control applications. The proposed control scheme is validated by simulation results and is compared against the existing proportional-integral-derivative controllers to show that the overall performance of the proposed adaptive SOSM controller is better in the aspects of error and control indices.

Hybrid intelligent control for set-point tracking of a flexible manoeuvring system using sigmoid activation function

This article addresses the input tracking control problem of a highly nonlinear flexible system namely a twin-rotor multi-input multi-output system in the vertical plane motion. The twin-rotor multi-input multi-output system can be considered as an aerodynamic experimental model, representing the control challenges of complex air vehicles. A hybrid control scheme comprising a fuzzy proportional–derivative-like control and a conventional integrator is developed by adding the outputs of both control actions. To satisfy the tracking characteristics of the manoeuvring system, an efficient combination of the control actions is developed using a smooth nonlinear activation function which continuously regulates contribution of the integral term in the controller output. The effect of the activation function greatly reduces overshoots when the system is exposed to abrupt changes during its manoeuvre. The control system performance is tested in simulations and experimentally in real-time situations. Robustness and stability of the controller are also investigated. The obtained results prove the efficiency of the proposed scheme in terms of time-domain performance specifications of the system.

Enhancement of a single-input fuzzy logic control system using a novel method

Despite its excellent performance as a controller for linear and non-linear systems, the fuzzy logic controller (FLC) has certain limitations. For instance, large-scale complex fuzzy systems like multi-input, single-output, or multi-output systems create complex applications with large amounts of rules. This study presents a novel framework that reduces the number of rules for an FLC based on a single-input fuzzy logic controller. Controller parameters are then optimized based on a genetic algorithm, and subsequently eliminating the trial and error approach involved in controller design. Reliability of the proposed method is verified with three complex and strong nonlinear systems, i.e. a twin-rotor multi-input–multi-output system, an inverted pendulum and cart system, and permanent magnet linear synchronous motor systems. Simulation and experiment results demonstrate that the proposed framework performs well in terms of the number of rules in the rules base and robustness. Moreover, the fuzzy rule number of the proposed approach decreases only with the number of linguistic labels for the membership function.