Changes In Reference Signal Research Articles

Model-Free VRFT-Based Tuning Method for PID Controllers

The main objective of this work was to develop a tuning method for PID controllers suitable for use in an industrial environment. Therefore, a computationally simple tuning method is presented based on a simple experiment on the process without requiring any input from the user. Essentially, the method matches the closed-loop response to the response obtained in the steady-state change experiment. The proposed method requires no prior knowledge of the process and, in its basic form, only the measurement of the change in the steady state of the process in the manually or automatically performed experiment is needed, which is not limited to step-like process input signals. The user does not need to provide any prior information about the process or any information about the closed-loop behavior. Although the control loop dynamics is not defined by the user, it is still known in advance because it is implicitly defined by the process open-loop response. Therefore, no exaggerated control signal swings are expected when the reference signal changes, which is an advantage in many industrial plants. The presented method was designed to be computationally undemanding and can be easily implemented on less powerful hardware, such as lower-end PLC controllers. The work has shown that the proposed model-free method is relatively insensitive to process output noise. Another advantage of the proposed tuning method is that it automatically handles the tuning of highly delayed processes, since the method discards the initial process response. The simplicity and efficiency of the tuning method is demonstrated on several process models and on a laboratory thermal system. The method was also compared to a tuning method based on a similar closed-loop criterion. In addition, all necessary Matlab/Octave files for the calculation of the controller parameters are provided online.

A Co-ordinated Control Methodology for Rapid Load-Following Operation of a Pressurized Water Reactor Based Small Modular Reactor

The Pressurized Water Reactor (PWR) based Small Modular Reactors (SMRs) are best suited to perform load-following operation because of their inherent self-stabilizing and self-regulating characteristics. Therefore, these PWR based SMRs can not only acts as standalone electricity generating units but also work alongside intermittent renewable energy sources in Hybrid Energy Systems (HES) configuration. In both the cases, the PWR based SMRs are expected to perform rapid load-following operation along with satisfaction of constraints imposed by overall power coefficients of reactivity. The conventional mode of PWR operation-namely, the Normal Mode and Alternate Mode could not guarantee very rapid power maneuvering alongside satisfaction of the reactivity constraints. This paper puts forward a novel control methodology, which combines the advantages of both the Normal Mode and Alternate Mode, to accomplish very rapid power maneuver without defying the reactivity constraints. The load-following controllers were designed using Incremental Nonlinear Dynamic Inversion (INDI) which guarantees efficient tracking and does not require additional state observer and knowledge of inherent system nonlinearities. However, the nonlinear tracking controllers whose reference signal changes very rapidly are victims of unwanted glitches/spikes in control output which may ultimately lead to actuator wear-out. This problem has been handled by the use of a Reference Shaper whose detailed design methodology has also been presented. The performance of the control methodology and the controllers are verified in real-time using real-time Hardware-in-Loop (HiL) simulations.

Model-Based Evaluation of Closed-Loop Deep Brain Stimulation Controller to Adapt to Dynamic Changes in Reference Signal.

High-frequency deep brain stimulation (DBS) of the subthalamic nucleus (STN) is effective in suppressing the motor symptoms of Parkinson's disease (PD). Current clinically-deployed DBS technology operates in an open-loop fashion, i.e., fixed parameter high-frequency stimulation is delivered continuously, invariant to the needs or status of the patient. This poses two major challenges: (1) depletion of the stimulator battery due to the energy demands of continuous high-frequency stimulation, (2) high-frequency stimulation-induced side-effects. Closed-loop deep brain stimulation (CL DBS) may be effective in suppressing parkinsonian symptoms with stimulation parameters that require less energy and evoke fewer side effects than open loop DBS. However, the design of CL DBS comes with several challenges including the selection of an appropriate biomarker reflecting the symptoms of PD, setting a suitable reference signal, and implementing a controller to adapt to dynamic changes in the reference signal. Dynamic changes in beta oscillatory activity occur during the course of voluntary movement, and thus there may be a performance advantage to tracking such dynamic activity. We addressed these challenges by studying the performance of a closed-loop controller using a biophysically-based network model of the basal ganglia. The model-based evaluation consisted of two parts: (1) we implemented a Proportional-Integral (PI) controller to compute optimal DBS frequencies based on the magnitude of a dynamic reference signal, the oscillatory power in the beta band (13–35 Hz) recorded from model globus pallidus internus (GPi) neurons. (2) We coupled a linear auto-regressive model based mapping function with the Routh-Hurwitz stability analysis method to compute the parameters of the PI controller to track dynamic changes in the reference signal. The simulation results demonstrated successful tracking of both constant and dynamic beta oscillatory activity by the PI controller, and the PI controller followed dynamic changes in the reference signal, something that cannot be accomplished by constant open-loop DBS.

Improving the dynamics of a synchronous in-phase electric drive in transient modes of synchronization, phasing and control in response of reference signal changes

Synchronous in-phase electric drive is widely used in thermal-imaging and laser scanning systems due to their high accuracy and dynamic characteristics in a wide range of angular velocity control. The purpose of the article is to develop ways to control a synchronous-in-phase electric drive in the modes of synchronization, phasing and control in response of reference signal changes, allowing to improve the dynamic performance of the electric drive and reduce information loss in the scanning system. The method of electric drive phasing that is quasi-optimal in speed has been improved. Recommendations on the organization of transient processes with an abrupt change in the reference signal are formulated. For use in the electric drive control system, a circuit implementation of an impulse frequency-phase discriminator with an extended linear zone in the phase comparison mode has been proposed, it allows to improve the dynamic performance of the electric drive in the modes of synchronization, phasing and control in response of reference signal changes.

Fast response of mechatronics module for robotic

The synthesis technique, the mathematical model and results of experimental investigation of the control system of the robotic complex mechatronic module are presented in the article. It is shown that in most cases the dynamic system can be approximated by the serial connection of two first-order aperiodic links, while the speed in the torque control loop can reach 200-300 rad/s. The specified speed of the system was achieved due to improved specific weight and dimensions parameters of the electric drive (element of the mechatronic system) made on the basis of a contactless motor. The obtained results indicate the possibility of successful application of the proposed mechatronic module for objects of robotized systems in which the reference signal changes at a frequency not exceeding 50 Hz.

Stability analysis of fuzzy filtering type III

In order to get the best corresponding answer in accordance with a reference model signal, digital filters should have the minimum error at its output using the mean square criterion. Inserting a fuzzy mechanism into its internal structure to construct an intelligent filter, the reference signal was adaptively interpreted to select and emit a decision answer in accordance with external reference signal changes, thereby updating the best correct new conditions into a process dynamically. The fuzzy filter gets the interpretation of the input signal level selecting the best weight parameter values from a set of membership functions stored into the knowledge base (KB), in order to give a signal approximation of the reference signal in natural form. The fuzzy stage improves the filter answers minimizing its convergence error using a classification of its operation into levels considering the minimum error distance. This work describes the stability properties of the fuzzy filter in accordance with the Kharitonov’s polynomials theory that establishes the maximum and minimum limits intervals of the fuzzy filter and the Routh-Hurwitz criterion to get the stability analysis of the filtering process. The states of the fuzzy filtering require that all of its answers bound into the error criteria probabilistically, in accordance with the Nyquist and Shannon assumptions and finally the paper shows the simulations of the fuzzy filter into the Kalman structure using the Matlab© tools. Key words: Digital filtering, fuzzy systems, estimation, stability.

Fuzzy digital filter type III

A digital filter interacts with a reference model signal into a real process in order to obtain the best corresponding answer, having the minimum filter output error using the mean square criterion. A fuzzy mechanism into the filter structure was added obtaining an intelligent filter, selecting and emitting an answer decision according to the external reference signal changes. This is in order to actualize the best correct new conditions updating a dynamic process. The fuzzy filter makes an interpretation of the input signal level to select the best parameter values from a set of membership values into the knowledge base (KB), updating the filter weights giving the approximation answers according to the reference signal. This fuzzy stage improves the filter answers, minimizing the filter error criterion with a classification of its operation levels. The filtering process requires that all answers of the error criteria are probabilistically bounded, considering the Nyquist and Shannon assumptions, having the fuzzy filter simulations using Matlab© tools into the Kalman structure.

Fuzzy Digital Filtering: Signal Interpretation

The paper makes a description of the fuzzy filter properties considering its operational principles. A digital filter interacts with a reference model signal into real process in order to get the best corresponding answer, having the minimum error at the filter output using the mean square criterion. Adding into this filter structure a fuzzy mechanism, to obtain an intelligent filtering because adaptively select and emit a decision answer according with the external reference signal changes, in order to actualize the best correct new conditions updating a process dynamically. The interpretation of the input signal level describes the operation of the reference model, to update the filter weights giving the answers approximation in accordance with the reference signal in natural form. Finally the paper shows the simulations results of the fuzzy filter into the Kalman structure using the Matlab© tool.

Cogging effect minimization in PMSM position servo system using dual high-order periodic adaptive learning compensation

Cogging effect which can be treated as a type of position-dependent periodic disturbance, is a serious disadvantage of the permanent magnetic synchronous motor (PMSM). In this paper, based on a simulation system model of PMSM position servo control, the cogging force, viscous friction, and applied load in the real PMSM control system are considered and presented. A dual high-order periodic adaptive learning compensation (DHO-PALC) method is proposed to minimize the cogging effect on the PMSM position and velocity servo system. In this DHO-PALC scheme, more than one previous periods stored information of both the composite tracking error and the estimate of the cogging force is used for the control law updating. Asymptotical stability proof with the proposed DHO-PALC scheme is presented. Simulation is implemented on the PMSM servo system model to illustrate the proposed method. When the constant speed reference is applied, the DHO-PALC can achieve a faster learning convergence speed than the first-order periodic adaptive learning compensation (FO-PALC). Moreover, when the designed reference signal changes periodically, the proposed DHO-PALC can obtain not only faster convergence speed, but also much smaller final error bound than the FO-PALC.

Evolutive Neural Net Fuzzy Filtering: Basic Description

The paper describes the operation principles of the evolutive neuro fuzzy filtering (ENFF) properties, which based on back propagation fuzzy neural net, this filter adaptively choose and emit a decision according with the reference signal changes of an external reference process, in order to actualize the best correct new conditions updating a process. This neural net fuzzy filter mechanism selects the best parameter values into the knowledge base (KB), to update the filter weights giving a good enough answers in accordance with the reference signal in natural sense. The filter architecture includes a decision making stage using an inference into its structure to deduce the filter decisions in accordance with the previous and actual filter answer in order to updates the new decision with respect to the new reference system con-ditions. The filtering process states require that bound into its own time limit as real time system, considering the Ny-quist and Shannon criteria. The characterization of the membership functions builds the knowledge base in probabilis-tic sense with respect to the rules set inference to describe the reference system and deduce the new filter decision, per-forming the ENFF answers. Moreover, the paper describes schematically the neural net architecture and the deci-sion-making stages in order to integrate them into the filter architecture as intelligent system. The results expressed in formal sense use the concepts into the paper references with a simulation of the ENFF into a Kalman filter structure using the Matlab© tool.