This capstone project focuses on optimizing actuator control, specifically for DC motors. The project aims to design and evaluate various controllers, ranging from classic to intelligent. Finding the optimal controller for a DC motor offers many advantages, including stability, robustness, and precise control, which are useful in fields like robotics. This paper proposes several controllers: pole placement, different configurations of PID, LQR, and LQI with different optimization techniques, fuzzy logic, and adaptive neuro-fuzzy controller schemes. The MATLAB environment was used to develop and test these controllers. Their performance was measured in terms of rise time, settling time, and overshoot. Based on the performance, the I-PD controller is overall the optimal controller for the DC motor. It achieves the fastest rise time of 507.7 msec, a settling time of 2.3 sec, and an overshoot of 0.51%

A drone or UAV is an Unmanned Aerial Vehicle with multiple forms of usage. Drones can be programmed to fly with different degrees of autonomous flight. Autonomous-controlled flight makes it possible for the drone to fly without human involvement and it is then controlled solely by software. In the near future, UAVs will help people and industries accomplish tasks that would have been impossible before, such as covering delivery services and transportation. For the purpose of having a stable and proper movement of a quadcopter under any circumstances such as climatic changes and air bumps, the design and development of robust controllers is essential. This paper proposed control methodologies to investigate system parameters by using different control schemes as PID and LQR. The desired accuracy is a parameter of the time-response performance param eters such as overshoot, settling time, and steady-state time. This work started by deriving the forward and inverse kinematics of the quadcopter with respect to the ground frame. The Denavit-Hartenberg method is utilized in the matrices derivation. Moreover, the dynamics model of the system was studied to find the actuators’ torques, angular velocity, and acceleration parameters. The MATLAB environment was utilized in processing the mathematical frame. By selecting the best system parameters, the most robust controller could be developed. The last phase of this work is the system implementation via a prototype that includes four PID controllers for the thrust, Roll, Pitch, and Yaw; sensor system to pro vide a fixed altitude and to provide proximity measurements of the environment, and Arduino module (for programming), I2C for interfacing, Pulse Width Modulation (PWM), and power management Con troller.A drone or UAV is an Unmanned Aerial Vehicle with multiple forms of usage. Drones can be programmed to fly with different degrees of autonomous flight. Autonomous-controlled flight makes it possible for the drone to fly without human involvement and it is then controlled solely by software. In the near future, UAVs will help people and industries accomplish tasks that would have been impossible before, such as covering delivery services and transportation. For the purpose of having a stable and proper movement of a quadcopter under any circumstances such as climatic changes and air bumps, the design and development of robust controllers is essential. This paper proposed control methodologies to investigate system parameters by using different control schemes as PID and LQR. The desired accuracy is a parameter of the time-response performance param eters such as overshoot, settling time, and steady-state time. This work started by deriving the forward and inverse kinematics of the quadcopter with respect to the ground frame. The Denavit-Hartenberg method is utilized in the matrices derivation. Moreover, the dynamics model of the system was studied to find the actuators’ torques, angular velocity, and acceleration parameters. The MATLAB environment was utilized in processing the mathematical frame. By selecting the best system parameters, the most robust controller could be developed. The last phase of this work is the system implementation via a prototype that includes four PID controllers for the thrust, Roll, Pitch, and Yaw; sensor system to pro vide a fixed altitude and to provide proximity measurements of the environment, and Arduino module (for programming), I2C for interfacing, Pulse Width Modulation (PWM), and power management Con troller.

The appropriate evaluation of the charge state of the battery is critical for ensuring safety and avoiding potential malfunctions in electric cars, cell phones, computers, and medical devices. The battery management system, on the other hand, provides important functions such as guaranteeing safe operation and informing the user about the battery’s state. Several approaches for estimating the battery state of charge (SoC) have been presented to produce the most effective management system. Machine learning techniques are used to compare prediction accuracy and the region of the convergence curve on testing and training data in this study. In this project, we proposed the use of machine learning algorithms as a means of predicting the battery state of charge in several locations that use battery management systems. The suggested methodology states using Weka software to implement three different algorithms of machine learning on battery parameters such as constant and current, temperature, and voltage. Results indicate that the best structure obtained using Weka is the Random Forest having the maximum correlation coefficient by finding the root mean square error (RMSE). Contradictory, the three machine learning algorithms which are decision tree, random forest, and linear regression revealed that decision trees have low correlation and relatively high root mean square error. The significance of the present project relies on its ability to predict the state of charge, a necessary prerequisite to executing a sustainable battery management system in electrical grids, assisting operators in efficiently managing general power, and helping achieve energy efficiency and production to its maximum ability, we used the stander USB while implementing the prototype.

During muscle activation, the surface Electromyography, sEMG, electrical signal is produced from small electrical currents generated by the exchange of ions across the muscle membranes and detected by electrodes. During a muscular activity, the brain sends excitation signals through the nervous system to a group of motor units which are the junction points between the neuron and the muscle fibers. As a result, each motor unit produces a ‘Motor Unit Action Potential’ (MUAP). This process is, continuously, repeated as long as the muscle is required to generate a force, producing a train of action potentials. The trains from concurrently active motor units superimpose to produce the resultant EMG signal. A group of muscles are involved in a certain movement of the human body. For a specific activity, there is a direct proportionality between the number of muscles, force, excitation from the nervous system, number of motor units, and firing rate. The bioelectric EMG signal has a wide range of applications such as a diagnostic and evaluation tool for neurological disorders, low back pain, physiotherapy, rehabilitation, sports, biofeedback and ergonomics research. Recently, EMG has found its use in the robotics field. A robotic mechanism can be effectively controlled by an EMG signal. The advances in electronics and microcontroller technology such as filtration, rectification, and amplification, improved the control options for robotic mechanisms. In this sense, we propose a design and implementation of an EMG data acquisition system with the Myoware device and a microcontroller. This thesis discusses, in detail, the effective use of sEMG as a tool for controlling a robotic hand. A detailed elaboration of the electrode types, signal acquisition technique, electronics circuit design considerations and the control procedure to drive electric motors in a robotic hand is provided. The MATLAB is used to analyze the acquired non-invasive signal.