Deep reinforcement learning, the fastest growing technique, to solve real-world complex problems by creatinga simple mathematical framework. It includes an agent, action, environment, and a reward. An agent will interactwith the environment, takes an optimal action aiming to maximize the total reward. This paper proposesthe compelling technique of deep deterministic policy gradient for solving the complex continuous actionspace of 3-wheeled omnidirectional mobile robots. Three-wheeled Omnidirectional mobile robots tracking isa difficult task because of the orientation of the wheels which makes it rotate around its own axis rather tofollow the trajectory. A deep deterministic policy gradient (DDPG) algorithm has been designed to train in environmentswith continuous action space to follow the trajectory by training the neural networks defined forthe policy and value function to maximize the reward function defined for the tracking of the trajectory. DDPGagent environment is created in the Reinforcement learning toolbox in MATLAB 2019 while for Actor and criticnetwork design deep neural network designer is used. Results are shown to illustrate the effectiveness of thetechnique with a convergence of error approximately to zero.

This paper investigates the trajectory tracking problem for a Multi-Input Multi-Output (MIMO) Twin Rotor Aerodynamic System (TRAS) using a hybrid architecture based on an H∞∞∞∞ controller and Iterative Learning Control (ILC). TRAS is a fast, nonlinear coupled system and therefore it is a challenging task to design a control system that ensures the tracking for fast changing trajectories. The controllers proposed in the literature for the TRAS through linear approaches tend to have a large control effort, while the ones designed using the nonlinear approaches track only for smooth input trajectories. Both issues are important from control point of view. In this paper, these issues are addressed by designing a feedback H∞∞∞∞ control that stabilizes the system and a feedforward ILC which reduces the control effort. The H∞∞∞∞ controller achieves the tracking for input trajectories with sharp edges, but the control effort required for tracking is large. With the proposed hybrid approach, tracking is achieved by the H∞∞∞∞ controller whereas the required control effort is reduced in each subsequent iteration by ILC. After a few iterations, accurate tracking at a minimized control effort is achieved. The simulations have been performed using MATLAB software and the controller designed through the proposed approach has been validated on nonlinear model of the system. The results of the proposed technique, compared with the flatness-based and back-stepping control strategies, show that the proposed controller ensures accurate tracking at the reduced control effort.

Twin rotor aerodynamic system (TRAS) approximates the dynamics of helicopters and other vertical take off rotor crafts. The nonlinear nature with significant cross-coupling between the inputs and outputs of the main and tail rotors make the control of such system for either stabilization or reference tracking a challenging task. In this paper, the problem of disturbance rejection for TRAS is addressed by designing disturbance observers through H∞ based approach. The system is decoupled into main and tail rotors subsystems. For each subsystem, an inner loop disturbance observer is synthesized that provides disturbance rejection, whereas to ensure stability and performance an outer loop baseline feedback controller is designed. Two different cases are considered. In first case 2 proportional-integral-derivative controllers are designed to use as outer loop baseline feedback controllers with disturbance observers whereas in the second case linear quadratic Gaussian (LQG) controllers are designed. For both cases simulations are performed with nonlinear Matlab Simulink model of TRAS and results are compared to determine which approach delivers better performance. Simulation results show that the 2 conflicting requirements of reference tracking and disturbance rejection can be met simultaneously with the proposed approach increasing the disturbance rejection capability of the closed loop system.

An airship is lighter than an air vehicle with enormous potential in applications such as communication, aerial inspection, border surveillance, and precision agriculture. An airship model is made up of dynamic, aerodynamic, aerostatic, and propulsive forces. However, the computation of aerodynamic forces remained a challenge. In addition to aerodynamic model deficiencies, airship mass matrix suffers from parameter variations. Moreover, due to the lighter-than-air nature, it is also susceptible to wind disturbances. These modeling issues are the key challenges in developing an efficient autonomous flight controller for an airship. This article proposes a unified estimation method for airship states, model uncertainties, and wind disturbance estimation using Unscented Kalman Filter (UKF). The proposed method is based on a lumped model uncertainty vector that unifies model uncertainties and wind disturbances in a single vector. The airship model is extended by incorporating six auxiliary state variables into the lumped model uncertainty vector. The performance of the proposed methodology is evaluated using a nonlinear simulation model of a custom-developed UETT airship and is validated by conducting a kind of error analysis. For comparative studies, EKF estimator is also developed. The results show the performance superiority of the proposed estimator over EKF; however, the proposed estimator is a bit expensive on computational grounds. However, as per the requirements of the current application, the proposed estimator can be a preferred choice.

Agriculture activities are completely dependent upon energy production worldwide. This research presents sensorless speed control of a three-phase induction motor aided with an extended Kalman filter (EKF). Although a proportional integral (PI) controller can ensure tracking of the rotor speed, a considerable magnitude of ripples is present in the torque generated by a motor. Adding a simple derivative to have a proportional integral derivative (PID) action can cause a further increase in ripple magnitude, as it allows the addition of high-frequency noise in the system. Therefore, a fractional-order-based PID control is presented. The proposed control scheme is applied in a closed loop with the system, and simulation results are compared with the PID controller. It is evident from the results that the fractional order control not only ensures 20 times faster tracking, but ripple magnitude in torque was also reduced by a factor of 50% compared to that while using PID and ensures the effectiveness of the proposed strategy.