In this study, a Coanda-drone with length, width, and height of 121.6, 121.6, and 191[mm] was designed, and its total mass was 1166.7[g]. Using four propulsion devices, it could produce a maximum of 5428[g] thrust. Its structure is very different from conventional drones because in this study it combines the design of the jet engine of a jet fixed-wing drone with the fuselage structure layout of a rotary-wing drone. The advantage of jet drone's high propulsion is kept so that it can output greater thrust under the same variation of PWM waveform output. In this study, the propulsion device performs high-speed jetting, and the airflow around the propulsion device will also be jetted downward along the direction of the airflow.

A data-driven controller has been proposed for nonlinear systems, and its effectiveness has been also shown. However, according to this control scheme, considerable large computation burden is required in on-line learning to update the database. The on-line limit its implementation in industrial processes. In this paper, a controller design scheme is proposed, which enables us to update the database in an off-line manner.

The purpose of this paper is to present the static and dynamic characteristics and a smart design approach for the digital PID control forward type multiple-output dc-dc converter. The central problem of a smart design approach is how to decide the integral coefficient. Since the integral coefficient decision depends on the static characteristics, whatever integral coefficient is selected will not be yield superior dynamic characteristics. Accordingly, it is important to identify the integral coefficient that optimizes static as well as dynamic characteristics. In proposed design approach, it set the upper and lower of input voltage and output current of regulation range. The optimal integral coefficient is decided by the regulation range of the static characteristics and the dynamic characteristics and then the smart design approach is summarized. As a result, the convergence time is improved 50% compared with the conventional designed circuit.

This primary objective of this study is to demonstrate simulation and ground-based experiment for the attitude control of flexible spacecraft. A typical spacecraft structure consists of the rigid body and flexible appendages which are large flexible solar panels, parabolic antennas built from light materials in order to reduce their weight. Therefore the attitude control has a big problem because these appendages induce structural vibration under the excitation of external forces. A single-axis rotational simulator with a flexible arm is constructed with on-off air thrusters and reaction wheel as actuation. The simulator is also equipped with payload pointing capability by simultaneous thruster and DC servo motor actuation. The experiment of flexible spacecraft attitude control is performed using only the reaction wheel. Using the reaction wheel the performance of the fuzzy-PID controller is illustrated by simulation and experimental results for a single-axis rotational simulator.

This paper presents the regulation and dynamic characteristics of the dc-dc converter with digital PID control, the minimum phase FIR filter or the IIR filter, and then the design criterion to improve the dynamic characteristics is discussed. As a result, it is clarified that the DC-DC converter using the IIR filter method has superior performance characteristics. The regulation range is within 1.3%, the undershoot against the step change of the load is less than 2% and the transient time is less than 0.4 ms with the IIR filter method. In this case, the switching frequency is 100 kHz and the step change of the load R is from 50 Ω to 10 Ω . Further, the superior characteristics are obtained when the first gain, the second gain and the second cut-off frequency are relatively large, and the first cut-off frequency and the passing frequency are relatively low. Moreover, it is important that the gain strongly decreases at the second cut-off frequency because the upper band pass frequency range must be always less than half of the sampling frequency based on the sampling theory.

We proposed a fuzzy control scheme to implement the cycle-to-cycle control for restoring swing phase of gait using functional electrical stimulation (FES). We designed two fuzzy controllers for the biceps femoris short head (BFS) and the vastus muscles to control flexion and extension of the knee joint during the swing phase. Control capabilities of the designed fuzzy controllers were tested and compared to proportional-integral-derivative (PID) and adaptive PID controllers in automatic generation of stimulation burst duration and compensation of muscle fatigue through computer simulations using a musculo-skeletal model. Parameter adaptations in the adaptive PID controllers did not significantly improve the control performance of the PID controllers. The fuzzy controllers were superior to the PID and adaptive PID controllers under several subject conditions and different fatigue levels. These results showed the fuzzy controller would be suitable to implement the cycle-to-cycle control of FES-induced gait.

Resilient Packet Ring (RPR) is a new technology currently being standardized in the IEEE 802.17 working group. The existed bandwidth allocation algorithms for RPR networks are not able to provide satisfactory solutions to meet the performance requirements. In this paper we propose one fair bandwidth allocation algorithm, termed PID-RPR, which satisfies the performance goals of RPR networks, such as fairness, high utilization and maximal spatial reuse. The algorithm is operated at each RPR node in a distributive way; the proportional, integral and differential (PID) controller is used to allocate bandwidth on the outgoing link of the node for the flows over the link in a weighted manner. To achieve the global coordination, one control packet containing every node's message runs around the ring in order to update the relevant message for all nodes on the ring. When the packet reaches one node, this node adjusts its own rate according to its own message in the control packet; in the meantime it updates other nodes' control message in the control packet. As the control packet propagates around the ring, each node can eventually adjust its sending rate to reach its fair share according to the fairness criterion, and the buffer occupancy at each node is kept within the target value. Our algorithm is of distributed nature in the sense that upstream ring nodes inject traffic at a rate according to congestion and fairness criteria downstream. The simulation results demonstrate that satisfactory performance of RPR networks can be achieved under the proposed bandwidth allocation scheme.

PID control schemes have been widely used for many industrial processes, which can be represented by nonlinear systems. In this paper a new scheme for neural-net based PID controllers is presented. The connection weights and some parameters of the sigmoidal functions of the neural network are adjusted using a real-coded genetic algorithm. The effectiveness of the newly proposed control scheme for nonlinear systems is numerically evaluated using a simulation example.

Since most process systems have nonlinearities, it is necessary to consider the design of schemes to deal with such systems. In this paper, a new design scheme of PID controllers is proposed. This scheme is designed based on the internal model control (IMC) which is a kind of the model driven controllers. The internal model consists of the design-oriented model and the full model. The full model is designed by using the neural network. The primary PID control system is firstly constructed for the augmented system which is composed of the controlled object and the internal model, and this control system is designed by the pole-assignment method. Furthermore, the secondary PID controller is designed in order to remove the steady state error. Finally, the effectiveness of the newly proposed control scheme is numerically evaluated on a simulation example.

As an enhancement mechanism for the end-to-end congestion control, AQM (Active Queue Management) can keep smaller queuing delay and higher throughput by purposefully dropping the packets at the intermediate nodes. Comparing with RED algorithm, although the PI (Proportional-Integral) controller for AQM designed by C. Hollot improves the stability, it seems unscientific to tune the controller parameters through trial-error, moreover the transient performance of the PI controller is not perfect, such as the regulating time is too long. In order to overcome this drawback, in this paper, the PID (Proportional-Integral-Differential) controller is proposed to speed up the responsiveness of AQM system. The controller parameters are tuned based on the determined gain and phase margins. The simulation results show that the integrated performance of the PID controller is obviously superior to that of the PI controller.

We have designed a torque magnetometer using a 60-kG split-type superconducting magnet. A balance torque compensates the torque acting on a sample in the magnetic field. The feedback circuit for a sample direction consists of an optical position sensor, a moving coil, and a PID controller. We measured the coil current to know a sample torque. The whole torque machinery is directly rotated by a stepping motor of angular resolution 0.0036. An advantage of the torque apparatus is a wide dynamic range up to 1000 dyncm. The sample temperature can be controlled between 4 K and 300 K.

In this paper, a design method of neural-net based PID controllers is proposed for multivariable nonlinear systems with mutual interactions. The proposed method adopt both a static pre-compensator and some multi-layered neural networks. The former is used for roughly decoupling the controlled object, and the latter is used in order to improve decoupling and to linearize the approximately decoupled controlled object. Also the design scheme based on the relationship between PID law and the generalized minimum variance control (GMVC) law is adopted. The effectivenes of the proposed control scheme is evaluated on a simulation example.

PID control schemes based on the classical control theory, have been widely used for various real control systems. However, in practice, since it is considerably difficult to determine the PID parameters suitably, lots of researches have been reported with respect to tuning schemes of PID parameters. Furthermore, several self-tuning and auto-tuning techniques in the PID control have been reported for systems with unknown or slowly time-varying parameters. On the other hand, so-called a generalized predictive control (GPC) scheme has been reported as a useful self-tuning control technique for unknown and/or time variant delay systems. In this paper, a new self-tuning predictive PID control algorithm based on a GPC criterion is proposed.

PID control schemes have been widely used in most of process control systems. Most of these processes are often treated as first-order systems with dead times. And also, in many cases, PID parameters are usually tuned based on the process parameters, i. e. , the time constant, the dead time and the process gain. However, since these process parameters can not be obtained exactly, it is well known that it is difficult to find the suitable PID parameters in practice. In this paper, we propose a genetic tuning scheme of PID parameters for first-order systems with large dead times. The authors have already proposed a tuning method of PID parameters using a genetic algorithm (GA), which was based on the relationship between PID control and generalized minimum variance control(GMVC) laws. In practice, for large dead time systems, first-order low pass pre-filters are often used. The proposed method is an extended version of the previously proposed method mentioned above to the system with a pre-filter due to the large dead time, i. e. , a tuning method of both PID parameters and the pre-filter using a GA. The proposed control scheme is numerically evaluated on some simulation examples.

A simulation-based ITS (Intelligent tutoring system), SRIM, has been developed for the purpose of providing individualized learning to students of PID control. We first indicate that the following two steps will be a burden to the student during personal use of simulators: 1) Selection of operational goals and 2) Interpretation of the simulation results. In order to reduce the burden of students in learning with a simulator, SRIM guides the learning process by providing local goals for PID controller tuning and by giving messages. Two tutoring strategies: i.e. the exercise style strategy and the illustrating style strategy, are employed in SRIM. In the exercise style strategy, a local goal for tuning a PID controller is first given to the student. A local goal is defined as one which can be satisfied by a single operation step such as Decrease the off-set." The student selects his operation and executes the simulation. By observing the simulation, the student understands whether his operation was a success or a failure. The illustrating style strategy is invoked to repair the student's erroneous knowledge when a contradiction is detected in the student model or a wrong operation is selected repeatedly. The architecture of ITS is employed to perform the local goal selection and the tutoring strategy switching, in a natural, well timed manner. The performance of SRIM was evaluated for the purpose of demonstrating the effectiveness of the teaching strategy. The evaluation experiment was carried out in the following steps: 1) Pre-test, 2) Learning and 3) Post-test. The teaching effect of SRIM was compared with other learning methods such as simple use of simulators or a textbook from the results of the pre-test and the post-test. The results showed that SRIM is effective in providing individualized learning with simulators.