Particle swarm optimization based proportional-derivative parameters for unmanned tilt-rotor flight control and trajectory tracking

This paper presents the dynamic modelling and control technique for a tilt-rotor aerial vehicle operating in bi-rotor mode. This kind of aircraft combines two flight envelopes, making it ideal for scenarios that require hovering, vertical take-off/landing and fixed-wing capabilities. In this work, a detailed mathematical model is derived using Newton–Euler formalism. Based on the obtained model, a new control scheme that incorporates six Proportional-Derivative (PD) controllers is proposed for the attitudes (roll (φ), pitch (θ), yaw (ψ)) and the positions (x, y, z) of the aircraft. Then, intelligent Particle Swarm Optimization (PSO) and conventional Reference Model (RM) techniques are applied for optimal tuning of the controllers\u27 parameters. The stability analysis is developed using the Lyapunov approach and its application to the tilt-rotor system in the case of intelligent and conventional PD controllers. Numerical results of two scenarios prove the efficiency of the controllers tuned using the PSO method. Indeed, its ability to track the desired trajectories is demonstrated through 3D path tracking simulations, even in the presence of wind disturbances. Finally, experimental tests of stabilization and trajectory tracking are carried out on our prototype. These testing showed that our tilt-rotor was stable and suitably follows the imposed trajectories

Helicopter gearbox vibration fault classification using order tracking method and genetic algorithm

In this paper, we implemented a diagnostic system for vibration faults that occur on the PUMA helicopter gearbox. We used an approach based on the joint use of the Order Tracking signal analysis and the Genetic Algorithm. To achieve this goal, we first collected a database of vibration signals measured during periodic inspections. The available vibration signals are acquired under a time-varying operating conditions. Therefore, we used the Order Tracking method, which is more accurate in extracting faults features. This technique was performed by resampling the vibration data and then applying the Short Time Fourier Transform. To enable efficient and continuous monitoring of gearbox vibration faults from features, we used Genetic Algorithm to build a rules-based diagnostic model. Genetic operators have been adapted to the specificity of the problem to optimize the parameters of this model. This approach is successfully applied to the diagnosis of vibration defects of helicopter gearboxes. The results have been validated effectively with test data. The diagnostic model can therefore be implemented on helicopter computers to detect faults in flight or on the ground. This approach has been used for the first time in the field of helicopter gearbox vibration fault diagnosis

A novel improved elephant herding optimization for path planning of a mobile robot

Swarm intelligence algorithms have been in recent years one of the most used tools for planning the trajectory of a mobile robot. Researchers are applying those algorithms to find the optimal path, which reduces the time required to perform a task by the mobile robot. In this paper, we propose a new method based on the grey wolf optimizer algorithm (GWO) and the improved elephant herding optimization algorithm (IEHO) for planning the optimal trajectory of a mobile robot. The proposed solution consists of developing an IEHO algorithm by improving the basic EHO algorithm and then hybridizing it with the GWO algorithm to take advantage of the exploration and exploitation capabilities of both algorithms. The comparison of the IEHO-GWO hybrid proposed in this work with the GWO, EHO, and cuckoo-search (CS) algorithms via simulation shows its effectiveness in finding an optimal trajectory by avoiding obstacles around the mobile robot

Adaptive Control Using Neural Networks and Approximate Models for Nonlinear Dynamic Systems

In this research, a comparative study of two recurrent neural networks, nonlinear autoregressive with exogenous input (NARX) neural network and nonlinear autoregressive moving average (NARMA-L2), and a feedforward neural network (FFNN) is performed for their ability to provide adaptive control of nonlinear systems. Three dynamical nonlinear systems of different complexity are considered. The aim of this work is to make the output of the plant follow the desired reference trajectory. The problem becomes more challenging when the dynamics of the plants are assumed to be unknown, and to tackle this problem, a multilayer neural network-based approximate model is set up which will work in parallel to the plant and the control scheme. The network parameters are updated using the dynamic backpropagation (BP) algorithm

Particle Swarm Optimization and Cuckoo Search-Based Approaches for Quadrotor Control and Trajectory Tracking

This paper explores the full control of a quadrotor Unmanned Aerial Vehicles (UAVs) by exploiting the nature-inspired algorithms of Particle Swarm Optimization (PSO), Cuckoo Search (CS), and the cooperative Particle Swarm Optimization-Cuckoo Search (PSO-CS). The proposed PSO-CS algorithm combines the ability of social thinking in PSO with the local search capability in CS, which helps to overcome the problem of low convergence speed of CS. First, the quadrotor dynamic modeling is defined using Newton-Euler formalism. Second, PID (Proportional, Integral, and Derivative) controllers are optimized by using the intelligent proposed approaches and the classical method of Reference Model (RM) for quadrotor full control. Finally, simulation results prove that PSO and PSO-CS are more efficient in tuning of optimal parameters for the quadrotor control. Indeed, the ability of PSO and PSO-CS to track the imposed trajectories is well seen from 3D path tracking simulations and even in presence of wind disturbances

Robust Full Tracking Control Design of Disturbed Quadrotor UAVs with Unknown Dynamics

In this study, we develop a rigorous tracking control approach for quadrotor unmanned aerial vehicles (UAVs) with unknown dynamics, unknown physical parameters, and subject to unknown and unpredictable disturbances. In order to better estimate the unknown functions, seven interval type-2-adaptive fuzzy systems (IT2-AFSs) and five adaptive systems are designed. Then, a new IT2 adaptive fuzzy reaching sliding mode system (IT2-AFRSMS) which generates an optimal smooth adaptive fuzzy reaching sliding mode control law (AFRSMCL) using IT2-AFSs is introduced. The AFRSMCL is designed a way that ensures that its gains are efficiently estimated. Thus, the global proposed control law can effectively achieve the predetermined performances of the tracking control while simultaneously avoiding the chattering phenomenon, despite the approximation errors and all disturbances acting on the quadrotor dynamics. The adaptation laws are designed by utilizing the stability analysis of Lyapunov. A simulation example is used to validate the robustness and effectiveness of the proposed method of control. The obtained results confirm the results of the mathematical analysis in guaranteeing the tracking convergence and stability of the closed loop dynamics despite the unknown dynamics, unknown disturbances, and unknown physical parameters of the controlled system

Robust Interval Type-2 Fuzzy Sliding Mode Control Design for Robot Manipulators

This paper develops a new robust tracking control design for n-link robot manipulators with dynamic uncertainties, and unknown disturbances. The procedure is conducted by designing two adaptive interval type-2 fuzzy logic systems (AIT2-FLSs) to better approximate the parametric uncertainties on the system nominal. Then, in order to achieve the best tracking control performance and to enhance the system robustness against approximation errors and unknown disturbances, a new control algorithm, which uses a new synthesized AIT2 fuzzy sliding mode control (AIT2-FSMC) law, has been proposed. To deal with the chattering phenomenon without deteriorating the system robustness, the AIT2-FSMC has been designed so as to generate three adaptive control laws that provide the optimal gains value of the global control law. The adaptation laws have been designed in the sense of the Lyapunov stability theorem. Mathematical proof shows that the closed loop control system is globally asymptotically stable. Finally, a 2-link robot manipulator is used as case study to illustrate the effectiveness of the proposed control approach

A robust type-2 fuzzy sliding mode controller for disturbed MIMO nonlinear systems with unknown dynamics

In this paper, in order to achieve the best tracking control of a class of multi-input multi-output (MIMO) nonlinear systems with unknown dynamics and unknown disturbances, a new robust adaptive interval type-2 fuzzy sliding mode control law (AIT2-FSMCL) has been proposed. Based on developing interval type-2 fuzzy local models for some operating points of the controlled system, an interval type-2 fuzzy logic system (IT2-FLS) has been designed to better estimate the unknown nonlinear dynamics of the studied system. Then, to enhance the tracking control performance and ensure the system robustness in the presence of approximation errors, parameter variations, un-modelled dynamics and external disturbances, a new AIT2-fuzzy sliding mode system (AIT2-FSMS), has been introduced. In order to avoid the chattering phenomenon while keeping the system performance, the AIT2-FSMS uses three AIT2-fuzzy logic systems (AIT2-FLSs) to estimate the optimal gains of the AIT2-FSMCL. The adaptation laws have been derived using the Lyapunov stability approach. The mathematical proof shows that the closed-loop system with the proposed control approach is globally asymptotically stable. Finally, the proposed design method is applied to a two-link robot arm to validate the effectiveness of the proposed control approach

New Trajectory Tracking Approach for a Quadcopter Using Genetic Algorithm and Reference Model Methods

This paper deals with the trajectory tracking problem for a quadrotor unmanned aerial vehicle (UAV). For this purpose, two control strategies are proposed. First, a flight controller with a hierarchical structure is designed, whereby the complete closed-loop system is divided into two blocks. The system has an inner block for attitude control and an outer block for position stabilization, for a total of six proportional-derivative/proportional-integral-derivative (PD/PID) controllers. The second new trajectory tracking strategy is based on attitude stabilization. In addition to a direct stabilization of yaw and altitude, the x and y positions are stabilized by choosing an appropriate control of roll and pitch angles. The relations between positions (x, y) and rotations (roll, pitch) are derived from the natural flight of the quadcopter. In this second approach, with only four controllers, the quadrotor UAV is able to follow any trajectory. In both approaches, the PD/PID controllers are synthesized using the genetic algorithm method, and compared with those obtained by the reference model method. Furthermore, a comparison between PD and PID controller performance is performed. Thereafter, the robustness of the proposed controllers is tested for trajectory tracking in a disturbed environment. Simulation results demonstrate that for the two approaches, PD controllers show a better behavior with respect to quadcopter stabilization than in trajectory tracking under different conditions

Aircraft Air Compressor Bearing Diagnosis Using Discriminant Analysis and Cooperative Genetic Algorithm and Neural Network Approaches

Monitoring and diagnosis of rotating machines has become an effective and indispensable tool for the efficient and timely detection of defects, avoiding then incidents that can have serious economic and human consequences. Bearings are the most sensitive parts of these machines; this is why special attention must be paid to its monitoring. This paper presents a methodology for diagnosing an aircraft air compressor bearing using neural networks that have been optimized by genetic algorithms. We used in our study a database of vibratory signals that were recorded on a test bench from bearings with different defects. The faults features are extracted from these noisy signals using the estimate of the spectral density. The diagnostic capacity of obtained model has been demonstrated by a comparative study with two other automatic classifiers, which are discriminant analysis and neural networks whose training has been done with the Back-Propagation algorithm. This approach has the advantage of simultaneously ensuring the optimal structure of the neural network and accomplishing its learning. The importance of this study is the construction of a diagnostic tool that is characterized by efficiency, speed of decision making and ease of implementation not only on the computers on the ground, but also on the mounted calculators on aircraft