In this paper, business environments of Business-to-business (BtoB) and Business-to-business-to-business (BtoBtoB) market for control technology, and their influences to technology development and industry-academia collaboration are discussed by examples of temperature controller distribution. The business environments were shown by using Market-Controlling Structure Chart and Value-Providing Structure Chart that were originally developed for visualizing complicated structures like BtoB or BtoBtoB market.
The paper introduces recent activities on PID gain tuning based on closed-loop operation data. The approach tries to provide PID gain tuning tools only from process input and output data. Such tools are strongly required in the industrial fields because a huge number of PID control loops have to be tuned by a few control engineers. Extended Fictitious Reference Iterative Tuning (E-FRIT) and further researches on E-FRIT have been shown, and future directions on the topic are examined. Finally, the paper discusses how we should proceed with industry-academia collaboration through the research activities on E-FRIT.
Business constraint of commercialized industrial controllers is important factor of control technology that is widely used in lots of manufacturing machines. A competitive axis has been changing in the recent years. And this trend is important issue to control technologies especially advanced PID. In this paper, classification of advance PID and a viewpoint of controller business are discussed based on technology marketing analysis. Designing of advanced PID algorithm must meet business conditions.
The heating equipment which are widely used in the industrial world consume a tremendous amount of electric power. For this reason a limit that is lower than the equipment's actual capacity is often set on power consumption in order to suit the company's power supply and equipment situation. This inevitably affects the equipment's heating performance. We have therefore developed a total power control technology which prevents the overall power consumption of a system of several PID temperature controllers from exceeding a preset limit, while at the same time maintaining a high level of tracking performance so that the temperature stays close to the set point.
The introduction of the method to calculate PID control parameters suitable for the controlling response that operators expect. Specifically, I calculate the most suitable parameter from the AHP (Analytic Hierarchy Process) method using a sensory evaluation and the experimental design using the orthogonal design.
In this study, we propose an application methodology of the Nyquist criterion for biochemical reaction systems with the biological negative feedback loops. The molecular robot or a real biochemical reaction system such as an intracellular signal transduction system utilizes negative feedback regulation to enhance the stability and robustness. However, since such a system tends to be a high dimensional, nonlinear, and complex system with multiple feedback loops, it is troublesome to quantitatively evaluate the stability property of the whole system in terms of a particular feedback loop. We show that by modifying the system gain the Nyquist criterion can be appreciable for this problem, and as a result the stability margin can be calculated.
This paper proposes a controller design method for Hammerstein model which is a dynamical linear system with static input nonlinearity. The method requires only one-shot closed-loop reference response data for controller design. The estimation of the plant model and the validation of the linear part of the plant using the data are performed in time and frequency domains, respectively. Since the method estimates the plant model and the plant is linearized by a nonlinear compensator, the controller for the linearized plant can be designed in consideration of the trade-off between the closed-loop bandwidth and the stability margin by a model-based approach. The effectiveness of the method is numerically evaluated.
Temperature controlled systems are widely used in the industrial field. Energy consumption of the temperature controlled system is especially larger than the other industrial machines. This paper presents a peak power suppression control for the temperature controlled system. Smaller power can be shared effectively among the temperature controlled systems by the proposed method. For industrial implementation, adjusting a priority of channel is simple because variable for the priority is only one parameter. In addition, the settling time control method based on the peak power suppression control is presented. Simulation result of temperature controlled system shows that the proposed method can solve the energy consumption problem.
Recently, welfare vehicles are widely used by patients and elders. Many welfare vehicles are restricted to drive indoor or on a flat road. The vehicle with free casters can not be driven well when the road surface is rolling outdoors. To design compact welfare vehicles for outdoor use at low cost, the skid steer mechanism is focused on. The skid steer vehicle (SSV) is well known for its high traveling ability on an off-road. However, the SSV has disadvantage that the maneuvering assistance is required because its steering is highly affected by road condition. This study aims a steering assistance of SSV for patients and elders by using Model Error Compensator (MEC) that suppresses the modeling error by traditional PID control. The proposed controller consists of MEC and Extended Kalman Filter to reduce the sensor and the system noise assuming the undulating and skiddy roads. The effectiveness of the proposed control system is verified by the driving simulation considering the drivers weight and the road conditions.
A model-based controller design method is widely used in the industrial field. However, in this method, a modeling error deteriorates the control performance. This paper proposes a multivariable controller design method using only frequency response data sets. This method omits the time-consuming model identification and designs a controller directly from raw data sets of the controlled plant. The proposed method provides the parameters of a PID controller using convex optimization subject to stability constrains. The optimization problem is formulated in terms of the diagonal dominance of the system. The designed controller suppresses an interaction and achieves high gain of the system in the case of highly diagonal dominance. The stability of MIMO systems can be evaluated by eigenvalue loci without overestimation of eigenvalue loci. The formulated optimization problem involving nonconvex functions and/or constraints is solved using concave-convex procedure that solves a problem written as a difference between two convex functions. The experimental results show the effectiveness of the proposed method in comparison with the conventional method.
An engine bench is utilized to perform durability test, performance test and other tests of a vehicle engine. Mechanical system of the engine bench consists of tested engine with clutch, dynamometer for absorbing engine power and coupling shaft to connect the engine and the dynamometer. As the stiffness of the clutch varies widely, it is necessary that the shaft torque control of the engine bench becomes stable for the variation of the stiffness of the clutch. In addition, an inertia compensation control of the coupling shaft is necessary for the engine load torque control. The conventional inertia compensation control method of the coupling shaft needs the pseudo-differential of the angular velocity of the dynamometer. In this paper, a stability analysis of the shaft torque control method that we proposed earlier is implemented. and we propose the inertia compensation control method of the coupling shaft that does not need the pseudo-differential of the angular velocity of the dynamometer.
This paper deals with a design problem of adaptive PID control with adaptive Neural Network (NN) feedforward control input for multi-input multi-output (MIMO) discrete-time systems with a parallel feedforward compensator (PFC). Under the ASPR condition, which is satisfied by PFC, the stability of the designed adaptive PID control system is analyzed. The effectiveness of the adaptive PID control method will be confirmed through numerical simulations for a discrete-time system.
PID control schemes have been widely used in most process control systems for a long time. The Chien, Hrones, and Reswick (CHR) tuning method is well known as one of typical schemes for adjustment of PID parameters. However, undesirable overshoot can occur when using the CHR method, and then the settling time may be prolonged. In this paper, the relationship of PID parameters and a first-order system with a dead time is discussed. A new scheme for adjustment of reference tracking PID parameters for a first-order system with a dead time is proposed, whose settling time is shorter than that of the CHR method. Based on the proposed scheme, PID parameters can be easily obtained from system parameters. The effectiveness of the proposed method is experimentally evaluated by not only computer simulation but also experimental results of a temperature control system.
This paper proposes a new offline learning algorithm for a data-driven proportional-integral-derivative (DD-PID) controller based on an extended fictitious reference iterative tuning (E-FRIT). PID controllers have been used in many process systems. However, it is difficult to maintain a good control performance using PID controllers with fixed control parameters because of the nonlinearity of the systems. The DD-PID controller has been proposed as an effective control system for nonlinear systems. This controller can tune its PID parameters adaptively at each equilibrium point of the system output. In the conventional DD-PID controller, the PID parameters are learned so as to minimize a criterion of the FRIT method. However, the FRIT method is based on minimization of the error in system output, and therefore, the criterion is insufficient for systems, such as chemical process systems, for which the stability of a closed loop system is essential. In order to solve this problem, the E-FRIT method has been proposed; a penalty for input variation is incorporated in the criterion for this method. In the present study, an offline learning rule of PID parameters was derived based on the E-FRIT criterion. According to the rule, the PID parameters that are taken into stability can be calculated. The effectiveness of the proposed method was evaluated by simulations of a polystyrene reactor system. The simulation confirmed that the proposed DD-PID controller yields better control result than the conventional learning method.
This paper presents a PID based control with low-order Disturbance Feedback Control (DFC). DFC is a technique to improve the existing control systems with an extra feedback which attenuates disturbances and model errors. This work analyzes Robust Performance (RP), which is the measurement of the robustness and performance of the DFC. A simple grid-based search algorithm is proposed to design disturbance feedback such that DFC can achieve RP for the control system with model uncertainties. Numerical examples show that better performance can be achieved with DFC in comparison with an existing PI controller.
This paper proposes the switching control method based on the controller gain which is relevant to the control performance. The objective of the proposed method is to choose the best control performance of all candidate controllers. The switching mechanism is based on the controller gain which is calculated by all candidate controllers. The accepted controller has the best estimated performance of all controllers. Finally, numerical evaluations demonstrate the practicality and utility of this idea.
In process industries, PID control has been applied to controlled objects such as chemical plants. A cascade control system is applied in order to improve control performance by using several feedback loops. However, it is complicated to design a cascade control system because this control system includes several controllers. In this paper, a design scheme of data-oriented cascade control system without using system models is proposed. According to the proposed scheme, PID gains of several controllers can be derived by using only closed-loop data. The effectiveness of the proposed method is verified by using a simulation example.
This paper presents an algorithm that directly uses the experimental data to determine the optimal parameters of PI controllers in the DC motor speed control system which is driven by cascade control. By using fictitious reference iterative tuning (FRIT), which is one of the controller parameter tuning that enables us to obtain the ideal parameter with only one-shot experiment, this paper constructs a fictitious reference signal for the cascade control systems such that we can simultaneously tune the PI controller parameters for better performance. The proposed algorithm does not require any parameters of the DC motor but only one-shot experimental data collected from the closed-loop system. To show the validity of the proposed result, we give an experimental example on DC motor speed control.
The paper gives a controller tuning method using a Virtual Feedback Tuning (VRFT) approach in frequency domain. While the original VRFT uses a data-driven performance index based on input/output data directly, the proposed one derives a frequency domain performance index based on the Fourier transformation of one-shot experimental input-output data. The frequency domain performance index enables the optimal pre-filter to be straightforwardly designed without using the spectral density of the collected input signal. Hence, the proposed one outperforms the original one when the input and output data are collected from the closed loop step response. The effectiveness of the proposed method is illustrated through a numerical example.
In this paper, a new system identification method is proposed using a real-coded genetic algorithm(GA), which the operating data, that is, closed-loop data, is utilized. Not only system parameters but also control parameters are estimated using the real-coded GA. Then, since control parameters are known, the reliability of estimates is evaluated in comparison of the estimates with true control parameters. In order to evaluate the effectiveness of the proposed scheme, it is employed for the sway control of crane system.
A weigh feeder control system is designed using a PI control law which is based on generalized minimum variance control with the steady-state predictive output. The control performance is assessed using minimum variance index, and the control parameters are adaptively updated such that the control performance is optimized. The effectiveness of the proposed method is demonstrated through experimental results.
Lots of self-turning control schemes have been proposed for unknown parameter systems. As one of them, a self-turning pole-assignment scheme has been widely used for unknown time-delay systems. However these methods need to estimate system parameters accurately. The error of estimations cause the deterioration of controlled performance. In this paper, a deign scheme of PID controller based on a pole-assignment scheme is proposed. And the proposed scheme is discussed by a simulation example.
This paper presents a stochastic decision making framework of the retailer for the power procurement in the deregulated electricity market taking into account the risk induced by the volatile market prices. Unlike conventional related literature, the paper incorporates the three types of trades, which are a forward contract, a spot market, and a real-time market for adjusting the imbalance, in the transaction model with rational responses of the consumers to the selling prices offered by the retailer. For the risk hedge using the forward contract, it is quite significant to reflect the risk attitude of the retailer on decision making in market transactions. In the literature, the weighting method is often used to formulate the retailer's decision making for the power procurement. In the weighting method, however, it is difficult to determine both the weighting coefficients and the confidential level associated with the risk so as to reflect the risk attitude of the retailer adequately, particularly in the bi-level programming problem. In this paper, in order to naturally model the retailer's decision making, we employ the fractile model in which the target variable is maximized subject to the probability that the profit is not less than the target variable exceeds a given assured level given by the retailer according to the risk attitude. Through the computational experiments, we demonstrate the retailer's behavior with satisfactory level for the profit and risk in the deregulated electricity market.
Recently, the introduction of the battery has been investigated in the electric power system. The purpose of the introduction of the battery is the system stabilization against the voltage fluctuation and the frequency fluctuation caused by the introduction of the renewable energy and the system accident. In this study, the battery is ranked as an instantaneous reserve power that instantaneously provides energy to the system in order to stabilize the system against the system disturbance. This study investigates the optimal battery assignment method against the system disturbance. Specifically, the one line accident in the electric power system is assumed as the cause for the system disturbance. Then, a new optimal battery assignment problem whose objective is the minimization of the variation of phase difference on all nodes caused by the accident at each branch is formulated. Then, an optimization method that consists of the tabu search and the differential evolution is applied to the formulated problem. The effectiveness of the proposed method is confirmed through numerical experiments of the application to the IEEJ EAST10 model.