In this paper we consider finite-time stabilization and finite-time boundedness control problems for time-varying discrete-time systems. We give a set of sufficient conditions, in terms of difference LMIs, for the existence of observer-based output feedback controllers that make the system finite-time stable and finite-time bounded. We then reduce the obtained results to the ones for time-invariant discrete-time systems and derive numerically tractable sufficient conditions given by LMIs. We also show numerical examples to illustrate the design methods of observer-based output feedback controllers.
An injection unit is considered as a speed control system utilizing a reaction-force sensor. Our purpose is to design a fault detector that detects and isolates actuator and sensor faults under the condition that the system is disturbed by a reaction force. First described is the fault detector's general structure. In this system, a disturbance observer that estimates the reaction force is designed for the speed control system in order to obtain the residual signals, and then post-filters that separate the specific frequency elements from the residual signals are applied in order to generate the decision signals. Next, we describe a fault detector designed specifically for a model of the injection unit. It is shown that the disturbance imposed on the decision variables can be made significantly small by appropriate adjustments to the observer bandwidth, and that most of the sensor faults and actuator faults can be detected and some of them can be isolated in the frequency domain by setting the frequency characteristics of the post-filters appropriately. Our result is verified by experiments for an actual injection unit.
In this paper, we consider control of grasp and manipulation of an object in a 3-dimensional space by a 3-fingered hand robot with soft finger tips. We firstly propose a 3-dimensional deformation model of a hemispherical soft finger tip and verify its relevance by experimental data. Second, we consider the contact kinematics and derive the dynamical equations of the fingers and the object where the 3-dimensional deformation is considered. For the system, we thirdly propose a method to regulate the object and the internal force with the information of the hand, the object and the deformation. A simulation result is presented to show the effectiveness of the control method.
Conventional ultrasonic non-destructive inspection systems used for detecting cracks in concrete are based primarily on the visual inspection of the amplitude of the sensor output and therefore, are not highly reliable. Moreover, in an inclined crack, the point of reflection of the transmitted signal is not necessarily just below the sensor, because this point is determined by a trade-off between the directivity of the sensor and the tilt angle of the inclined crack. Therefore, the conventional methods are unreliable for identifying an inclined crack in concrete structures. In this paper, we propose a simple and effective method to detect an inclined crack in concrete structures using an ultrasonic sensor with the signal propagation model and a method of least squares. First, the signal propagation model is used to obtain the propagation times of the reflected wave signals from the crack using a pattern matching technique and then a method of least squares is applied to identify the inclined crack accurately considering both the reflection efficiency and the signal strength at the point of reflection on the crack. Finally, experiments are performed to demonstrate the validity of the method.
An engine control education system is designed. This system can realize the following functions: it serves to familiarize people with gasoline engine properties and can be applied to carry out engine control simulation, to design engine control logic and to realize engine real-time simulation. In the paper, the structure of this education system is explained. The system is composed of a computer, a high-speed arithmetic processing board, an ECU and an engine test bench. Engine control simulations are carried out, and engine properties are obtained. Therefore this system can assist people in mastering gasoline engine properties. Besides, a real-time simulation system is designed, and PID control real-time simulation is realized. In the future, new control systems can be designed based on the current one. When the engine simulator is connected with engine test bench and ECU, engine real-time simulation can be realized.
This paper presents a guideline for deciding physical parameters for the Acrobot, a typical example of two-link underactuated robots. A set of physical parameters for the Acrobot is determined using an evaluation function, which is derived by analyzing the robustness of an existing swing-up controller for the Acrobot. A typical set of physical parameters used in many papers are then evaluated using our evaluation function, and the validity and performance of our proposed guideline are studied with a series of simulations.
A Riemannian-geometry approach for control of two-dimensional object grasping and manipulation by using a pair of multi-joint planar robot fingers is presented, together with a basic discussion on stability of position and force hybrid control of redundant robotic systems under geometric constraints. Even in the case that the shape of the object is arbitrary, it is possible to see that rolling contact constraints induce the Euler equation of motion in an implicit function form, in which constraint forces appear as wrench vectors affecting on the object. The Riemannian metric can be introduced in a natural way on a constraint submanifold induced by rolling contacts. A control signal called “blind grasping” is defined and shown to be effective in stabilization of grasping without using the details of information of object shape and parameters or external sensing. The concept of stability of the closed-loop system under constraints is renewed in order to overcome the degrees-of-freedom redundancy problem. An extension of Dirichlet-Lagrange's stability theorem to a system of DOF-redundancy under constraints is presented by using a Morse-Lyapunov function.
Instead of functioning as a part of a pointing device for computers, the optical mouse sensors are employed for combined tactile sensing in this study. Based on the experimental characteristics of optical mouse sensors, with a step-by-step table-look-up method, a combined tactile sensing system is proposed. When an object surface is moved underneath the optical mouse sensor, the translation direction, the category and the height of the object surface can be obtained with the recorded output of the optical mouse sensor based on a predefined data table.
The authors have proposed a very simple autonomous learning system consisting of one neural network (NN), whose inputs are raw sensor signals and whose outputs are directly passed to actuators as control signals, and which is trained by using reinforcement learning (RL). However, the current opinion seems that such simple learning systems do not actually work on complicated tasks in the real world. In this paper, with a view to developing higher functions in robots, the authors bring up the necessity to introduce autonomous learning of a massively parallel and cohesively flexible system with massive inputs based on the consideration about the brain architecture and the sequential property of our consciousness. The authors also bring up the necessity to place more importance on “optimization” of the total system under a uniform criterion than “understandability” for humans. Thus, the authors attempt to stress the importance of their proposed system when considering the future research on robot intelligence. The experimental result in a real-world-like environment shows that image recognition from as many as 6240 visual signals can be acquired through RL under various backgrounds and light conditions without providing any knowledge about image processing or the target object. It works even for camera image inputs that were not experienced in learning. In the hidden layer, template-like representation, division of roles between hidden neurons, and representation to detect the target uninfluenced by light condition or background were observed after learning. The autonomous acquisition of such useful representations or functions makes us feel the potential towards avoidance of the frame problem and the development of higher functions.