Ari Santoso

Engine Torque Control of Spark Ignition Engine Using Robust Fuzzy Logic Control

Due to government regulations and policies aimed at improving fuel economy and reducing emissions, it is increasingly important to achieve control over transient behavior and meet performance objectives over the life of the vehicle. This requires the development of high performance and robust power train controllers. The performance objectives are often conflicting, or at best interrelated. Controlling engine torque is one way to potentially meet these performance requirements. Spark ignition engine is a combustion engine that operates with a very wide operating point. Each point of engine operation provides different properties to each other, so that the control treatment to be given varies a great deal. This problem led to a single controller design becomes very difficult to be realized. In this research, a new design method of fuzzy robust control proposed to provide an alternative solution to build an integrated-control that can anticipate a system that works on a wide operating point and have different characters for each operating point. From the simulation results, it can be concluded that the design of robust fuzzy controller can be used simultaneously for several operating point without having to redesign the controller.

Performance enhancement of inverted pendulum system by using type 2 fuzzy sliding mode control (T2FSMC)

The drawbacks of using FSMC when controlling the position motion on a non linear inverted pendulum system are the chattering problem and the robustness against parameter uncertainty and disturbances. This paper consider about the implementation of Fuzzy Type 2 in combining with SMC (T2FSMC) to enhance the ability of FSMC to overcome both of those problems. To be more optimally damp the oscillation, the range of fuzzy membership is determined based on the minimum ITAE (integral of time absolute error) value while the rule is constructed based on the product between the sliding surfaces and its derivatives. To enhance robustness performance against disturbances and parameter uncertainties, the range of non crisp membership value (footprint uncertainty) of Fuzzy Type 2 is determined by considering the minimum sum of ITAE values at several conditions with different plant parameter. It is proved from the simulation that the proposed method can solve chattering problem more effectively and also more robust against disturbances and parameter uncertainties than T1FSMC.

An Improved Face Recognition System for Service Robot Using Stereo Vision

Service robot is an emerging technology in robot vision, and demand from household and industry will be increased significantly in the future. General vision-based service robot should recognizes people and obstacles in dynamic environment and accomplishes a specific task given by a user. The ability to face recognition and natural interaction with a user are the important factors for developing service robots. Since tracking of a human face and face recognition are an essential function for a service robot, many researcher have developed face-tracking mechanism for the robot (Yang M., 2002) and face recognition system for service robot( Budiharto, W., 2010). The objective of this chapter is to propose an improved face recognition system using PCA(Principal Component Analysis) and implemented to a service robot in dynamic environment using stereo vision. The variation in illumination is one of the main challenging problem for face recognition. It has been proven that in face recognition, differences caused by illumination variations are more significant than differences between individuals (Adini et al., 1997). Recognizing face reliably across changes in pose and illumination using PCA has proved to be a much harder problem because eigenfaces method comparing the intensity of the pixel. To solve this problem, we have improved the training images by generate random value for varying the intensity of the face images. We proposed an architecture of service robot and database for face recognition system. A navigation system for this service robot and depth estimation using stereo vision for measuring distance of moving obstacles are introduced. The obstacle avoidance problem is formulated using decision theory, prior and posterior distribution and loss function to determine an optimal response based on inaccurate sensor data. Based on experiments, by using 3 images per person with 3 poses (frontal, left and right) and giving training images with varying illumination, it improves the success rate for recognition. Our proposed method very fast and successfully implemented to service robot called Srikandi III in our laboratory. This chapter is organized as follows. Improved method and a framework for face recognition system is introduced in section 2. In section 3, the system for face detection and depth estimation for distance measurement of moving obstacles are introduced. Section 4, a detailed implementation of improved face recognition for service robot using stereo vision is presented. Finally, discussions and future work are drawn in section 5.

A method for path planning strategy and navigation of service robot

This paper presents our work on the development of Path Planning Strategy and Navigation by using ANFIS(Adaptive Neuro-Fuzzy Inference System)controller for a vision-based service robot. The robot will deliver a cup to a recognized customer and a black line as the guiding track for navigating a robot with a single camera. The contribution of this research includes a proposed architecture of ANFIS controller for vision-based service robot integrated with improved face recognition system using PCA, and the algorithm for moving obstacle avoidance. We also propose a path planning algorithm based on Dijkstra’s algorithm to obtain the shortest path for robot to move from the starting point to the destination. In order to avoid moving obstacles, we have proposed an algorithm using binaural ultrasonic sensors. The service robot called Srikandi is also equipped with 4 DOF arm and a framework of face recognition system. The proposed path planning strategy and navigation was tested empirically and proved effective.