The daily lifestyle of an average human has changed drastically. Robotics and AI systems are applied to many fields, including the medical field. An autonomous wheelchair that improves the degree of independence that a wheelchair user has can be a very useful contribution to society. This paper presents the design and implementation of an autonomous wheelchair that uses LIDAR to navigate and perform SLAM. It uses the ROS framework and allows the user to choose a goal position through a touchscreen or using deep learning-based voice recognition. It also presents a practical implementation of system identification and optimization of PID control gains, which are applied to the autonomous wheelchair robot. Input/output data were collected using Arduino, consisting of linear and angular speeds and wheel PWM signal commands, and several black-box models were developed to simulate the actual wheelchair setup. The best-identified model was the NLARX model, which had the highest square error (0.1259) among the other candidate models. In addition, using MATLAB, Optimal PID gains were obtained from the genetic algorithm. Performance on real hardware was evaluated and compared to the identified model response. The two responses were identical, except for some of the noise due to the encoder measurement errors and wheelchair vibration.

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Parallel Robot (PR) has shown its ability to be precise in its movement. Actuators move simultaneously to achieve the required target, on top of that its payload is much greater than what a serial robot can withstand. To determine workspace of the robot with known angles Forward kinematics has to be introduced which, bring a lot of difficulty as it requires the solution of multiple coupled nonlinear algebraic equations. Those equations bring multiple valid solutions. Those solutions could lead to different locations. As it is not going to make the pick and place for PR will be easier. This paper will discuss a numerical method that calculates the Forward Kinematics for PR. This method uses Artificial Neural Network which relay on training with a certain number of iterations. The set of data to be used in the training can be obtained from PR simulation. This method will serve to know workspace around PR as it will help it to pick the target object.

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The tariff is the best incentive factor for the industrial customer today to reduce the peak load. This paper describes different methods of pricing electricity. It is important to study the influence of the tariff on the industrial loads and the costs of electricity before ILM can be implemented. This paper is interested in studying the economic aspects of fixed and operating cost, other factors affecting the generation economy, distribution of power, etc. Costs of supplying electricity to consumers are also considered. A utility can influence its customers' load by providing financial incentives through the prices charged for electricity at different times of use. By designing discriminatory time of use tariffs in which the prices of electricity corresponds to the marginal cost of supply, a utility provides appropriate signals to the customer to increase consumption of electricity at off peak periods. The proposed algorithm is implemented to a real system in operation.