A hierarchical system is developed that consists of an intelligent sequence controller and a PID controller, where the intelligent sequence controller supervises the PID controller. In other words, the intelligent sequence controller plays the role of a well-experienced operator and mimics the operator's procedures. In an ethylene plant, the decoking operation of the cracking furnace makes the ethylene plant highly unstable. We apply this hierarchical system to the decoking process in an ethylene plant in order to demonstrate its effectiveness. As a result, the number of operational interventions in the decoking process is reduced by 95%, and the levels of the towers and the overhead temperatures of the towers can be satisfactorily stabilized during the decoking process.
This paper proposes a predictive control method for automotive engines with variable valves. The control purpose is to track not only the torque reference but also the pressure reference of the surge tank in consideration for the constraint of internal exhaust gas recirculation ratio. The control inputs are the throttle angle and the intake valve lift, however, the proposed control method is based on a flow model where the mass flows through the throttle and the intake valves are regarded as the virtual control inputs. The controller designed for the SICE benchmark engine is validated by numerical simulations.
Although the Global Positioning System (GPS) is used widely in car navigation systems, cell phones, surveying, and other areas, several issues still exist. We focus on the continuous data received in public use of GPS, and propose a new positioning algorithm that uses time series analysis. By fitting an autoregressive model to the time series model of the pseudorange, we propose an appropriate state-space model. We apply the Kalman filter to the state-space model and use the pseudorange estimated by the filter in our positioning calculations. The results of the authors' positioning experiment show that the accuracy of the proposed method is much better than that of the standard method. In addition, as we can obtain valid values estimated by time series analysis using the state-space model, the proposed state-space model can be applied to several other fields.
An optimal regulator problem for endpoint position control of a robot arm with (or without) redundancy in its total degrees-of-freedom (DOF) is solved by combining Riemannian geometry with nonlinear control theory. Given a target point, within the task-space, that the arm endpoint should reach, a task-space position feedback with joint damping is shown to asymptotically stabilize reaching movements even if the number of DOF of the arm is greater than the dimension of the task space and thereby the inverse kinematics is ill-posed. Usually the speed of convergence of the endpoint trajectory is unsatisfactory, depending on the choice of feedback gains for joint damping. Hence, to speed up the convergence without incurring further energy consumption, an optimal control design for minimizing a performance index composed of an integral of joint dissipation energy plus a linear quadratic form of the task-space control input and output is introduced. It is then shown that the Hamilton-Jacobi-Bellman equation derived from the principle of optimality is solvable in control variables and the Hamilton-Jacobi equation itself has an explicit solution. Although the state of the original dynamics (the Euler-Lagrange equation) with DOF-redundancy contains uncontrollable and unobservable manifolds, the dynamics satisfies a nonlinear version of the Kalman-Yakubovich-Popov lemma and the task-space input-output passivity. An inverse problem of optimal regulator design for robotic arms under the effect of gravity is also tackled by combining Riemannian geometry with passivity-based control theory.
Many measures have been developed to determine the interestingness of rules in data mining. Numerous studies have shown that the effects of different measures depend on the concrete problems, and different measures usually provide different and conflicting results. Therefore, selecting the appropriate measure becomes an important issue in data mining. In this paper, a novel approach to select the appropriate measure for class association rule mining is proposed. The proposed approach is applied to several problems, including benchmark and real-world datasets. The experimental results show that the proposed approach is a powerful tool to analyze various measures to select the right ones for the concrete problems, leading to the increase of the classification accuracy. Based on the study, this paper further proposes four properties of interestingness measures that should be considered in class association rule mining.
Mobile manipulators are expected to play an important role in production processes of factories and in medical care systems of welfare business. To come up to this expectation, a mobile manipulator is required to simultaneously track both the desired position trajectory and force trajectory. Therefore the authors have proposed two adaptive hybrid position/force control schemes for mobile manipulators. In this paper we implement these control schemes in an actual mobile manipulator and demonstrate the effectiveness of these proposed control schemes experimentally.