Smith Predictor Implementation of a High Dead Time Interacting Tank Process

The control strategy research of the time-delay system is a focused issue in the control field. In order to furthermore improve the performance of the first-order time-delay inertial system, firstly, a new Smith predictor structure is proposed, which solves the constraint that the conventional Smith predictor needs to match the actual object model. Secondly, the performance and parameter function of the new Smith predictor are discussed in theory to provide the basis for parameter tuning. Finally, a new Smith predictor combined with linear active disturbance rejection control (LADRC) is proposed to solve the problem that the two input signals of the linear extended state observer (LESO) are not synchronized on the time scale, and the stability of the new Smith + LADRC time-delay control system is proved theoretically for known and unknown controlled complex objects. Simulation analysis is conducted to verify the robustness of the proposed strategy under the condition of the different parameters. The results indicate that the proposed strategy has better performance than the conventional method in response speed, overshoot, adjustment time, and stability.

The most important part of the heating, ventilation, and air conditioning technology is heating System. This part is used in smart buildings and provides the desired air quality and thermal comfort. The time delay and uncertainty in model parameters due to the several operation mode cause the main challenges in heating system control by the traditional PID approaches. To overcome these problems, this paper presents an intelligent PID algorithm combines the fuzzy logic and neural network method together and used it in Smith predictor structure. Hence, a fuzzy neural network PID controller based on Smith predictor is proposed in this paper for the heating system. By correction of the dynamic learning of neural network and fuzzy inference, PID parameters of the controller get their optimal values. Simulation results of the heating system illustrate that the performance of the fuzzy neural network PID controller based on Smith predictor in comparison to the other control structures has been greatly improved, with fast response, smallest overshoot and lowest rise and settling time.

A simple relay feedback auto‐tuning method is proposed for the Smith predictor, an advanced controller for processes with long dead time. the relay feedback control gives information on one point of the Nyquist curve in terms of ultimate gain and frequency. With an additional measurement of the static gain, a reduced order process model in terms of a first‐ or second‐order dynamics plus dead time could be computed and used to auto‐tune the Smith predictor. When the process dynamics changes more frequently, a self‐tuning controller is required. In this case the Fast Fourier Transform technique can be further employed to track the points on the Nyquist curve by estimating the process frequency response and then used to update the Smith predictor. Excellent performance of the auto‐tuned and self‐tuned Smith predictor has been substantiated by simulations.

PurposeThe purpose of this paper is to propose a novel event-triggered aperiodic intermittent sliding-mode control (ETAI-SMC) algorithm for master–slave bilateral teleoperation robotic systems to further save communication resources while maintaining synchronization precision.Design/methodology/approachBy using the Lyapunov theory, a new event-triggered aperiodic intermittent sliding-mode controller is designed to synchronize master–slave robots in a discontinuous method. Unlike traditional periodic time-triggered continuous control strategy, a new ETAI condition is discussed for less communication pressure. Then, the exponential reaching law is adopted to accelerate sliding-mode variables convergence, which has a significant effect on synchronization performance. In addition, the authors use quantizers to make their algorithm have obvious progress in saving communication resources.FindingsThe proposed control algorithm performance is validated by an experiment developed on a practical bilateral teleoperation system with two PHANToM Omni robotic devices. As a result, the synchronization error is limited within a small range and the control frequency is evidently reduced. Compared with a conventional control algorithm, the experimental results illustrate that the proposed control algorithm is more sensitive to system states changes and it can further save communication resources while guaranteeing the system synchronization accuracy, which is more practical for real bilateral teleoperation robotic systems.Originality/valueA novel ETAI-SMC for bilateral teleoperation robotic systems is proposed to find a balance between reducing the control frequency and synchronization control precision. Combining the traditional sliding-mode control algorithm with the periodic intermittent control strategy and the event-triggered control strategy has produced obvious effect on our control performance. The proposed ETAI-SMC algorithm helps the controller be more sensitive to system states changes, which makes it possible to achieve precise control with lower control frequency. Moreover, we design an environment contact force feedback algorithm for operators to improve the perception of the slave robot working environment. In addition, quantizers and the exponential convergence law are adopted to help the proposed algorithm perform better in saving communication resources and improving synchronization precision.

A humidifying system with varying transportation lag was studied experimentally. Various models were tried and the system fitted a first order plus dead time model with an error of ±5%. The humidity was measured using an on‐line Yokogawa hygrometer. From the model parameters, various controllers, such as PI, Smith predictor, IMC, and IMC PID were analyzed using Matlab. The closed loop performance was studied for both regulator and servo problems. Based on rise time, settling time, and overshoot, the present study concludes that the IMC controller is best suited for this process.

Although active disturbance rejection control (ADRC) has demonstrated promising results in many applications, in some safety critical applications e.g. power plant, unmanned aerial vehicle, etc., it is often reluctant to switch from an existing control algorithm, either the traditional PID controller or other advanced control algorithms, to a new one. If the switching between the control algorithms can be made seamless, many more opportunities may open up. In this paper, the problem of making the switching to the ADRC smooth is studied. Previous attempts to solve the problem are not thorough enough, since only a second order system was addressed. A very simple and effective method is proposed based on analysis to make the transition seamless. The solution is very general, and can be applied to system of any order or even system with time-delay. At the core of the solution is the initialization of the “total disturbance”.

This article studies the application of nonparametric modeling approach to model magnetorheological (MR) dampers. For comparison purposes, another typical parametric modeling method for electrorheological (ER) and MR dampers is reviewed. The existing parametric MR damper model includes a stiff Bouc-Wen model that is not friendly for simulation study and real time implementation of model-based advanced control algorithms. In order to avoid the difficulties by using the existing parametric model, the test data from a commercialized MR damper is employed to develop nonparametric models, which can consist of a series of numerically efficient mathematic functions. In addition, the selected functions are required to be continuous and differentiable for potential model-based control algorithms. The results of the nonparametric models show that such different models are comparable. Furthermore, one nonparametric model is selected to be compared with a parametric model and the test data to illustrate the accuracy of the model. The comparison shows that the proposed nonparametric models are able to accurately predict the damper force characteristics, damper bilinear behavior, hysteresis, and electromagnetic saturation. It is further shown that the nonparametric models can be numerically solved with an integration step size of the order of 10 2 s, much faster than the parametric models of the order of 10 5 s, which clearly shows that the proposed nonparametric models are feasible even for real time model-based control algorithms.

Traditional control algorithms may experience processing delays when dealing with complex calculations or large amounts of data, resulting in slower system response speeds. This delay is particularly evident in application scenarios that require real-time response, such as the control of high-speed moving electromechanical equipment. This paper mainly explores the control algorithm of IoT electromechanical systems based on network integration theory, to achieve efficient, stable, and intelligent control of the system. The opening introduction elaborates on the core position of IoT electromechanical systems in contemporary industrial fields and emphasizes the key role played by control algorithms in optimizing system performance. Given the limitations of current control algorithms in response speed, robustness, and other aspects, this paper innovatively proposes a new architecture for control algorithms and verifies its effectiveness through experimental methods. A complete experimental platform was built in this paper, and the performance of the proposed control algorithm was comprehensively tested through the design of control experiments under different operating conditions, as well as system stability experiments under network fluctuations and equipment failures. Experiments have shown that the proposed algorithm can accurately and in real-time manipulate electromechanical equipment, successfully execute predetermined tasks, and demonstrate excellent control effects and stability under various operating conditions. Even in the event of network fluctuations or equipment failures, algorithms can quickly adapt, adjust control strategies, ensure the continuous and stable operation of the system, and demonstrate strong robustness.

The control of slurry pH in the absorber tower of the wet flue gas desulfurization (WFGD) system is characterized by significant time delays and strong nonlinearity. To maintain stable operation under these conditions, traditional controllers must adopt conservative parameter settings, which lead to slower response speeds and diminished control accuracy. To address this limitation, this paper proposes an improved Smith predictor based on a deep neural network (referred to as DNN-Smith) to enhance the performance of traditional controllers in <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\text{p H}$</tex> control. First, based on the existing neural network-based Smith predictor structure, the two internal neural network models are consolidated into a single one. Second, the dynamic characteristics of the <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\text{p H}$</tex> in the WFGD system are modeled using a deep neural network. Finally, the improved DNN-Smith is implemented for <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\text{p H}$</tex> control in the WFGD system. The results demonstrate that the deep neural network-based pH prediction model reduces key prediction errors (RMSE and MaxAE) by 84.54 % and 87.04 %, respectively, compared to the traditional ARX model. The proposed DNN-Smith-PI controller outperforms the conventional PI controller, reducing the RMSE and IAE in <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\text{p H}$</tex> control by <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\text{8 7. 0 \%}$</tex> and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\text{8 6. 4 \%}$</tex>, respectively. Furthermore, the improved <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\text{p H}$</tex> control performance contributes to more stable control of the clean flue gas <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\text{SO}_{2}$</tex> concentration, achieving an approximately 22 % reduction in RMSE compared to traditional PI control. Therefore, the DNN-Smith-PI controller presented in this paper provides an effective solution.

This paper provides an identification method for three-parameter models i.e. first order with dead time models (FODT) and second order with dead time (SODT) models. The proposed identification method is based on step response and can be easily implemented using digital microprocessors. The proposed method first identifies the order of the plant i.e. first order or second order from the behavior of the plant with constant input. After the order of the plant is determined, a test step input is applied to the system and the three parameters of the plant are obtained from the corresponding response of the plant. The output of the plant need not to be zero when the test signal is applied. The efficacy of proposed algorithms is verified through simulation.

As robotics technologies evolve rapidly, path planning has emerged as a fundamental pillar underpinning autonomous navigation systems. Traditional algorithms such as A*, Dijkstra, and Bellman-Ford demonstrate competence in structured environments, yet exhibit notable limitations in dynamic and uncertain scenarios. In contrast, intelligent algorithmsincluding Genetic Algorithms (GA), Reinforcement Learning (RL), and hybrid strategiesprovide measurable advantages in adaptability, learning capacity, and robustness. This study centers on comparing traditional and intelligent control algorithms for path planning from a theoretical perspective, outlining their respective principles, performance traits, and application contexts. The findings suggest that intelligent algorithms, owing to their capacity for real-time learning, environmental adaptation, and heuristic generalization, consistently outperform traditional approaches in dynamic and unstructured operational contexts. The paper offers critical theoretical insights to guide future algorithm selection and optimization for robot navigation. Furthermore, the paper discusses real-world implementation challenges, such as computational complexity, sensor uncertainty, and algorithm convergence stability, to highlight the applicability of these techniques in complex systems.

Intrinsic characteristics of distillation such as dead time and high nonlinearities do not allow the complete elimination of transient times when any external disturbance or set‐point change occurs. Thus, aiming at the use of easy‐tuning systems, a distributed‐action control in trays of a diabatic distillation unit with Smith's predictor was implemented in the Simulink environment to further reduce transient times and out‐of‐specification product. The distributed‐action strategy with Smith's predictor led to a reduction of 33.3 min (33 %) in the transient time of the top temperature control loop and 66 % in out‐of‐specification product, when compared with the conventional strategy, and thus is shown to be an efficient approach to increasing the productivity of distillation plants.

Model predictive control of a miniaturized linear single-piston cryocooler

High-speed Nuclear Quality Pulse Height Analyzer for Synchrotron-based Applications J-F. Beche, J. J. Bucher, L. Fabris, V. J. Riot Abstract--A high throughput Pulse Height Analyzer system for synchrotron-based applications requiring high resolution, high processing speed and low dead time has been developed. The system is comprised of a 120ns 12-bit nuclear quality Analog to Digital converter with a self-adaptive fast peak detector- stretcher and a custom-made fast histogramming memory module that records and processes the digitized data. The histogramming module is packaged in a VME or VXI compatible interface. Data is transferred through a fast optical link from the memory interface to a computer. A dedicated data acquisition program matches the hardware characteristics of the histogramming memory module. The data acquisition system allows for two data collection modes: “standard” data acquisition mode where the data is accumulated and read in synchronization with an external trigger and “live” data acquisition mode where the system operates as a standard Pulse Height Analyzer. The acquisition, standard or live, can be performed on several channels simultaneously. A two-channel prototype has been demonstrated at the Stanford Synchrotron Radiation Laboratory accelerator in conjunction with an X-ray Fluorescence Absorption Spectroscopy experiment. A detailed description of the entire system is given and experimental data is shown. I. S UMMARY A fast high resolution and low dead time Pulse Height Analyzer (PHA) system for Synchrotron-based applications has been developed. This module is an addition to and completes the previously designed custom electronics for XAFS applications [1, 2]. The PHA system receives analog information from a low dead time pulse processing front-end section. The low dead time PHA system is comprised of a fast high resolution Analog to Digital Converter (ADC), a fast histogramming memory and a VME or VXI compatible interface. The VME-based system consists of one complete data acquisition channel. The VXI-based PHA system addresses the problem of simultaneous data acquisition from multiple channels. Both systems were tested at the Stanford Synchrotron Radiation Laboratory in conjunction with an X- ray Fluorescence Absorption Spectroscopy experiment. The block diagram of a single acquisition channel is shown in Fig. This work was supported by the Director, Office of Energy Research, Office of Basic Energy Sciences, Chemical Science Division of the U.S. Department of Energy under Contract No. DE-AC03-76SF00098. J-F Beche, J. J. Bucher, L. Fabris and V. J. Riot are with E. O. Lawrence Berkeley National Laboratory, Berkeley, CA 94720 USA (telephone: 510- 495-2327, e-mail: JFBeche@lbl.gov). The analog input section is built around a fast self-adaptive peak detector-stretcher described in [1]. The peak detector- stretcher includes all the necessary logic to provide pileup rejection and gating functions. The stretched signal is fed to a commercial ADC chosen to maximize throughput without compromising the differential non-linearity (DNL). This ADC is an 8Msps unit, providing 14 bit of resolution with no missing code over the full temperature range, and with an intrinsic DNL of ½ LSB. In order to improve the DNL (not adequate for nuclear spectroscopy applications), only the first 12 most significant bits are used. A 6-bit sliding scale correction [3] lowers the DNL to less than 1%. Including delays, the conversion time of the ADC is 120ns. The electronic pulse-processing amplifier, in its fastest version, shapes the energy events coming from the detector with a 250ns peaking time fourth-order, pseudo-gaussian shaping function. The conversion time of the ADC does not add to the dead time associated with the pulse-shaping amplifier. Data collection is either gated by an external synchronization signal such as an accelerator beam clock or a precise clock signal to time-stamp the data collection and measure count rates. After an analog to digital conversion has taken place, the ADC board control logic sends a request to the histogramming memory module. When the histogramming memory controller acknowledges the request, it uses the ADC data to increment by one the content of the corresponding address memory location. The entire operation lasts 130ns, which is less than the dead time of the pulse- processing amplifier. Therefore, the histogramming operation does not increase the overall event-by-event processing time of the entire system. The histogramming memory module is packaged in a VME or VXI compatible interface and is built around a dual-port static random access memory. One port is dedicated to data accumulation while the second port is mapped to the VME or VXI address and data buses. The transfer to the computer is controlled by either the synchronization signal in the form of an interrupt signal sent to the VME or VXI controller, or by the computer accessing the memory at regular time intervals. The former corresponds to the “standard” acquisition mode and the latter to the “live” mode. In standard mode the content of the histogramming memory must be erased (zeroed) before collecting any new data set.

Stringent output voltage regulation with faster transient response is demanded for dc-dc converters. A boost converter is important in LED lighting, photovoltaic systems, hybrid electric vehicles etc. But the presence of a Right Half Plane (RHP) zero in the small-signal model of a boost converter for its continuous current mode operation restricts its speed of response. With standard fixed frequency control, the effect of a RHP zero cannot be eliminated completely. In this paper, the principle of Smith Predictor control is used to minimize the effect of RHP zero and maximize the achievable control loop bandwidth with sufficient phase margin. The Smith Predictor structure is realized with a Sallen-Key band pass filter and simulated with a voltage mode (VM) boost converter and a type III compensator in closed loop. The analytical results are verified with large-signal CADENCE switched simulations. The limitations of Smith Predictor control are also discussed in this context.