فهرست مطالب سید جلیل ساداتی رستمی

Implementing control and guidance systems on an autonomous underwater vehicle requires spending much money, which in case of failure, will suffer heavy losses. For this purpose, the complete method of evaluating an autonomous underwater vehicle's guidance, control, and navigation system is the hardware in the loop test before the diving and buoyancy so that the validation of the designed system can be evaluated in conditions close to reality.This research discusses the implementation of the hardware in the loop test to guide the path of a remotely operated vehicle. Considering the dynamics of the underwater robot - which is highly non-linear and dependent; First, the design of a free model sliding mode controller in the presence of underwater currents as a disturbance in the form of software in the loop is discussed. Second, the software in the loop test is performed by applying the control signal obtained from the proposed controller to the driver of the electric motor as the robot's proportion, whose mathematical model has been identified in the laboratory. Finally, by gradually replacing the hardware drives of the robot in the loop, the hardware in loop test of six degrees of freedom of the remotely operated vehicle is done in real-time in the presence of environmental disturbances. The proximity of the hardware in the loop test results with the software test in the loop shows the correctness of the work done. Meanwhile, the performance of the proposed controller is compared with the PID controller in trajectory tracking. In the end, the accuracy and validity of the implemented hardware table are examined by analyzing the frequency spectrum of the measurements of the hardware in the loop rig.

A robust controller for an uncrewed underwater vehicle operating in complex oceanic environments is essential because external disturbances from the environment uncertainty in the model potentially disrupt the system's stability. This research proposes a Supper-Twisting Sliding mode controller for an uncrewed underwater vehicle in the presence of external disturbances, limitations of thrusters, and measurement noise. Supper-Twisting is a second-order sliding-mode controller that combines a continuous signal with a discrete signal to produce a robust controller. The stability of the proposed controller is investigated using the phase plane graphical method. A linear Kalman filter is used to estimate the measurement error in the navigation system and then correct them.The performance of the controller and the proposed estimation algorithm in the motion control loop of the underwater robot are compared with other controllers. In addition, the validation of the proposed methods is evaluated in the form of hardware in the loop with the presence of electric motors as robot propulsions in the MATLAB®/ Simulink® Real-Time environment.

In this paper, an adaptive wavelet neural network tracking controller is studied for solving control and stability problem of a class of uncertain nonlinear systems. The considered systems in this paper are of the discrete-time form in pure-feedback structure and include the backlash and external disturbance. The backlash nonlinearity input appears non-symmetric in the systems. These systems are more general than those in the previous work. There are major difficulties for stabilizing such systems and in order to overcome the difficulties, by using prediction function of future states, the systems are transformed into an n-step-ahead predictor. The wavelet neural networks are used to approximate the unknown functions and unknown backlash in the transformed systems and the adaption laws are to update neural weights and to compensate for the unknown parameter of backlash. Based on the Lyapunov theory, it is shown that the proposed controller guarantees that all the signals in the closed-loop system are bounded and the tracking error converges to a small neighborhood of zero. The simulation of a Single-link robot arm system is provided to verify the effectiveness of the control approach in the paper. Finally, in order to validate, the results of the proposed method are compared with the results of PID and sliding mode controller.

Numerous studies have been devoted to motion control of wheeled mobile robots in recent years. Among them, trajectory tracking has received much attention.. A feed-forward and feedback control structure for trajectory tracking is used to circumvent the limitation of Brockett’s theorem. Feed-forward control is calculated according to the reference trajectory, it can not compensate instrumentation and initial state errors, therefore a feedback controller is utilized as well. In this paper a model predictive controller is used as the feedback controller. Since the initial state is not often matched to the desired trajectory, rapid tracking of the trajectory in early steps is very important. In this paper a model predictive controller with laguerre functions and another one with exponential data weighting is used to reduce tracking error in early steps. According to simulation results, reference trajectory tracking is improved through laguerre functions in model predictive controller.

In this paper, a new type of iterative learning control systems with fractional order known as iterative learning control with fractional order derivative and iterative learning control with fractional proportional–derivative for linearized systems of single-link robot arm is introduced. First order derivative of classic Arimoto is used for tracking error in updating law of derivative iterative learning control. Suggested method in this paper implement tracking error for updating control law of iterative learning of fractional order. For the first time, nonlinear robot system is linearized by input feedback linearization. Then, convergence analysis of iterative learning control law of type PD^alpha is studied.In the next step, we define a criteria for parameters optimization of proposed controller by using Biogeography-based optimization algorithm. Both updating law of fractional order iterative learning control (D^alpha-type ILC and PD^alpha-type ILC) is applied on linearized robot arm and performance of both controller for different value of alpha is presented. For improving the performance of closed loop system, coefficient of fractional order iterative learning control (proportional and derivative coefficients) is optimized by BBO algorithm. Proposed iterative learning control is compared with common type of system.