This paper presents the utilisation of continuous ant colony optimisation algorithm intended for active vibration control of flexible beam structures. Ant colony optimisation algorithm is used to realise a direct controller design scheme with single objective function to minimise vibration at the free end of the beam. The performance of the system is presented in both time and frequency domains and simulation results reveal that good performance is achieved using this approach

Abstract-

This article presents an online non-linear dynamic modelling and control approach based on adaptive neuro-fuzzy inference system (ANFIS) for a twin-rotor multi-input multi-output system (TRMS), in the vertical plane motion. The TRMS can be considered as a flexible aerodynamic test rig that resembles the behaviour of a helicopter. The TRMS and similar manoeuvring systems are often subjected to random disturbances arising from various sources such as driving motors and external environmental sources. For such highly non-linear systems with varying operating conditions, adaptive control approaches are suitable tools to cope with plant uncertainties. An inverse-model control of the TRMS is developed using online ANFIS learning algorithm. The consequent and antecedent parameters of a Takagi–Sugeno fuzzy inference system are optimized online using recursive least squares and gradient descent algorithms, respectively. In order to reduce the computation complexity, the training process is minimized based on global system error tolerance. The optimal initialization of the ANFIS parameters is achieved through an off-line training process. The developed strategy is compared to other control laws in terms of tracking performance, disturbance rejection, and response to external noise. The obtained simulation results demonstrate the efficiency of the online inverse control scheme.

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.

This paper presents the development of an improved spiral dynamic optimisation algorithm with application to fuzzy logic dynamic modelling of a twin rotor system. Spiral motion and dynamic step size generated by a spiral model produce unique exploration and exploitation strategies of a spiral dynamic algorithm. However, the algorithm is subj ect to settling into local optima at the end of the search process due to insufficient exploration throughout the search area. An elimination and dispersal strategy of a bacterial foraging algorithm is adopted to solve the problem. Moreover, the application of acquiring nonlinear dynamic model of a helicopter model prototype in hovering mode is presented and the results show the effectiveness of the proposed algorithm to solve real world problems. The result of the modelling work is presented in both time-domain and frequency-domain. It shows that the fuzzy model optimized by the proposed algorithm is better and adequate to represent the characteristic behaviour of the helicopter model prototype system.