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The dynamic performances of an innovative robotic total station (RTS) are assessed while tracking a moving target; also, the results of these performance evaluations are exploited to design its control system. The proposed methodology is novel and uses ad hoc virtual experiments implemented on the digital twin of the studied RTS. This methodology is general and is also applicable for the design of other mechatronic systems. The proposed ad hoc virtual experiments constitute a benchmark for assessing the tracking performances of pointing systems. The application of this methodology to the studied RTS will help complete the RTS's design by determining the most suitable actuators and the control system constants necessary to obtain the assigned dynamic performances. A 3D-printed prototype of the designed RTS was built and is shown in this paper. [ABSTRACT FROM AUTHOR]

Smart earth station antennas have been used for several decades in many applications, from satellite communications to space object detection and tracking. The accuracy of the azimuth position for such antennas plays a crucial role in most steerable ground station antennas. Satellite tracking and space object detection demand precise tracking capabilities from the Earth. Several methods and techniques have been developed and used in industry to control the directions of ground station antennas, including the azimuth position. The challenge of azimuth tracking is increasing with the demand for full-sky coverage and with the exponential increase in space objects, including man-made satellites and operational and nonoperational objects; thus, providing accurate tracking is a key technology that demands continuous enhancement and development. This article presents the use of a PID-proportional-integral-derivative controller, a slide mode controller and a fractional order PID controller. It also introduces a new methodology based on model predictive control (MPC). The manuscript provides the core design for each of these controllers and provides insight into the performance of each controller even in the presence of disturbance. The camel optimization algorithm (COA) was used to obtain the optimal design parameters of each controller in the considered scenarios. [ABSTRACT FROM AUTHOR]

This research introduces the Sparse Sensor Placement Optimization for Prediction algorithm and explores its use in bioinspired flight-by-feel control system design. Flying animals have velocity-sensing structures on their wings and are capable of highly agile flight in unsteady conditions, a proof-of-concept that artificial flight-by-feel control systems may be effective. Constrained by size, weight, and power, a flight-by-feel sensory system should have the fewest optimally placed sensors which capture enough information to predict the flight state. Flow datasets, such as from computational fluid dynamics, are discrete, often highly discontinuous, and ill-suited for conventional sensor placement optimization techniques. The data-driven Sparse Sensor Placement Optimization for Prediction approach reduces high-dimensional flow data to a low-dimensional sparse approximation containing nearly all of the original information, thereby identifying a near-optimal placement for any number of sensors. For two or more airflow velocity magnitude sensors, this algorithm finds a placement solution (design point) which predicts angle of attack of airfoils to within 0.10° and ranks within the top 1% of all possible design points validated by combinatorial search. The scalability and adaptability of this algorithm is demonstrated on several 2D model variations in clean and noisy data, and model sensitivities are evaluated and compared against conventional optimization techniques. Applications for this sensor placement algorithm are explored for aircraft design, flight control, and beyond. [ABSTRACT FROM AUTHOR]

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Abstract Smart earth station antennas have been used for several decades in many applications, from satellite communications to space object detection and tracking. The accuracy of the azimuth position for such antennas plays a crucial role in most steerable ground station antennas. Satellite tracking and space object detection demand precise tracking capabilities from the Earth. Several methods and techniques have been developed and used in industry to control the directions of ground station antennas, including the azimuth position. The challenge of azimuth tracking is increasing with the demand for full-sky coverage and with the exponential increase in space objects, including man-made satellites and operational and nonoperational objects; thus, providing accurate tracking is a key technology that demands continuous enhancement and development. This article presents the use of a PID-proportional-integral-derivative controller, a slide mode controller and a fractional order PID controller. It also introduces a new methodology based on model predictive control (MPC). The manuscript provides the core design for each of these controllers and provides insight into the performance of each controller even in the presence of disturbance. The camel optimization algorithm (COA) was used to obtain the optimal design parameters of each controller in the considered scenarios.

Electric ship propulsion is considered one of the most promising alternatives to conventional combustion systems. Its goal is to reduce the carbon footprint and increase a ship's maneuverability, operational safety, and reliability. The high requirements for ship propulsion make permanent magnet synchronous motors (PMSMs) an attractive solution due to their characteristics. This paper discusses the control problem of a PMSM based on the input–output feedback linearization method combined with the optimal and adaptive control techniques. The method presented here integrates the parameter tuning process with the optimal design of the baseline controller. Since the load disturbances are treated as an additional unknown parameter, there is no need to introduce an integral action to deal with the resulting steady-state error. An important feature of the designed controller is the so-called energetic optimization of the system; i.e., in addition to the aforementioned adaptive and optimal controller, it has a feature of ensuring zero reactive power consumed by the system. The performed simulations of the machine speed stabilization process confirmed the high efficiency of the proposed controller despite the assumed uncertainty of the system parameters and environmental (load) disturbances. Besides achieving high-quality control, an essential feature of this approach is the elimination of the tuning problem. [ABSTRACT FROM AUTHOR]

Fast pyrolysis in auger reactor gains attention for efficient bio-oil production. Due to the quick nature of the process, precise temperature control using the proportional-integral-derivative (PID) algorithm is paramount. This study harnesses various PID tuning approaches through modelling and experimental validation to optimize continuous and precise pyrolysis temperature. System identification was done to investigate the process dynamic with fit accuracy above 93% and design a suitable PID control. Comparison with the experiment data shows a favorable result with rise time and settling time match above 75%. Ziegler-Nichols (ZN) and Cohen-Coon (CC) tuning methods were implemented in the system with undistinguished results, yielding steady-state error (SSE) below 1% and settling time around 4,300 to 4,800 seconds. The heuristic fine-tuning method improved the rise time and settling time by stabilizing before 3,600 seconds. Furthermore, the robustness of PID controllers was verified with a disturbance rejection test, keeping the SSE deviation inside the boundary of 2%. Finally, the setup could support maximum pyrolytic oil production by 69.6% at 500 °C. The result implies that the PID controller could provide a stable and rugged response to support a productive and sustainable pyrolysis plant operation. [ABSTRACT FROM AUTHOR]

This paper presents an iterative proportional-integral-derivative (PID) controller design approach. To achieve the desired control performance, the control designer iteratively increases the proportional (P) and derivative (D) gains. And then, the integral controller is added to the obtained PD controller to reduce the steady-state error. Thus, the proposed approach is easy to implement and effective since it aims to maximize P-gain. Moreover, the stability conditions are presented to explain why stability is ensured while increasing PID gains. Simulations for the motor control system are peformed to validate the effectiveness of the proposed approach.

The Pigeon-Inspired optimization (PIO) algorithm is a novel intelligent optimization algorithm inspired by birds’ behavior as their travel. This; behavior modeled to be used for solving many optimization problems in different fields. However; it always suffers from unstable behavior when used with nonlinear; time-varying systems. In; this paper, this algorithm is adapted to calculate the optimum controller gains for roll and pitch channels in a guided tactical missile. The; vehicle model is presented in a nonlinear; form and then shown in a linearized form for the sake of an autopilot design. The PIO; algorithm is supported and accompanied by an adaptive algorithm to determine the initial states and constraints for the PIO algorithm to enhance the behavior of the optimization algorithm to speed up the convergence rate to reach an optimum and feasible solution. Also; an estimation function is incorporated to estimate model parameters variation such as dynamic pressure, stability derivatives, and mass properties. Meanwhile; a comparative analysis is carried out with original PIO and particle swarm optimization algorithms, utilizing a non-linear; model with the presence of noise source and disturbance to ensure the ability of the algorithm to make the autopilot robust and stable against several sources of uncertainties.

The Pigeon-Inspired optimization (PIO) algorithm is a novel intelligent optimization algorithm inspired by birds' behavior as their travel. This; behavior modeled to be used for solving many optimization problems in different fields. However; it always suffers from unstable behavior when used with nonlinear; time-varying systems. In; this paper, this algorithm is adapted to calculate the optimum controller gains for roll and pitch channels in a guided tactical missile. The; vehicle model is presented in a nonlinear; form and then shown in a linearized form for the sake of an autopilot design. The PIO; algorithm is supported and accompanied by an adaptive algorithm to determine the initial states and constraints for the PIO algorithm to enhance the behavior of the optimization algorithm to speed up the convergence rate to reach an optimum and feasible solution. Also; an estimation function is incorporated to estimate model parameters variation such as dynamic pressure, stability derivatives, and mass properties. Meanwhile; a comparative analysis is carried out with original PIO and particle swarm optimization algorithms, utilizing a non-linear; model with the presence of noise source and disturbance to ensure the ability of the algorithm to make the autopilot robust and stable against several sources of uncertainties. [ABSTRACT FROM AUTHOR]

Solar energy is sustained using the principle of photovoltaic effect through a solar photovoltaic (PV) system as the main receiver of sunlight for the island. The use of photovoltaic (PV) systems to generate power from solar energy has increased in recent years due to its availability and sustainability. The proposed bat algorithm provides a very quick confluence and high accuracy since it interactively merges with the exploration advancements with the substantial distinctive signal search during the maximum power point tracking process from the PV array. The improved bat algorithm proposes a signal search method to increase the speed and tracks more power at a longer distance by using the signal between the bat and its prey. An energy storage system (ESS) stores solar energy and releases it into the system for use when energy generation from the source is low. DC to DC converter controls and regulates the output generated voltage from the PV array. DC to AC converter converts DC power produced to AC power to be supplied to the island for use. The design and simulation were performed in MATLAB Simulink and the result shows the effectiveness of the proposed design. [ABSTRACT FROM AUTHOR]

Electric ship propulsion is considered one of the most promising alternatives to conventional combustion systems. Its goal is to reduce the carbon footprint and increase a ship’s maneuverability, operational safety, and reliability. The high requirements for ship propulsion make permanent magnet synchronous motors (PMSMs) an attractive solution due to their characteristics. This paper discusses the control problem of a PMSM based on the input–output feedback linearization method combined with the optimal and adaptive control techniques. The method presented here integrates the parameter tuning process with the optimal design of the baseline controller. Since the load disturbances are treated as an additional unknown parameter, there is no need to introduce an integral action to deal with the resulting steady-state error. An important feature of the designed controller is the so-called energetic optimization of the system; i.e., in addition to the aforementioned adaptive and optimal controller, it has a feature of ensuring zero reactive power consumed by the system. The performed simulations of the machine speed stabilization process confirmed the high efficiency of the proposed controller despite the assumed uncertainty of the system parameters and environmental (load) disturbances. Besides achieving high-quality control, an essential feature of this approach is the elimination of the tuning problem.

The popularization of the Internet of Things has made our daily life more and more convenient. By using the Internet of Things, we can create an intelligent home control system that makes our rooms safer, more comfortable, and can meet our growing demand for items. This article discusses how to use the Internet of Things to improve the efficiency of smart homes, and focuses on how to use wireless sensor networks, embedded systems, and various technologies such as network communication, audio and video to achieve these goals. This can enable these intelligent home control systems to provide efficient services. This article also introduces how to use Internet of Things technology in the design process of home control systems to better handle wiring and other related issues. We will also explore the differences between traditional and smart homes. The results showed that four buyers rated the smart home system, with an average score of 4 before use indicating a lower score, and an average score of 9 after use indicating a higher score. [ABSTRACT FROM AUTHOR]

In the paper, an innovative active control of a horizontal seat suspension system is discussed in context of realistic input vibration that occurs in the cabins of agricultural tractors. The vibration reduction system is represented by the state-space model and its numerical parameters are determined experimentally. For such a reliable mathematical representation of the system dynamics, the finite-time sliding mode controller is successfully designed for different spectral classes of the random excitation signal. A multi-objective control strategy is implemented in order to satisfy opposite requirements, that is, at the limited value of active force, a horizontal vibration of the seated human body is considerably reduced, but the suspension travel does not exceed unacceptable stroke of the system. Laboratory measurements of the seated human body have shown improved comfort of drivers under fore-and-aft vibrations. [ABSTRACT FROM AUTHOR]

A molten-salt reactor (MSR), in which the nuclear waste can be separated and taken away from the liquid fuel through online chemical treatment, has remarkable advantages in safety and economics. To ensure the safe operation of an MSR plant, it is necessary to carry out a controller design and stability analysis of the whole system. In this work, a nonlinear distributed-parameter model of an MSR plant with a graphite-moderated liquid core was established and verified by the reference model. By applying a joint feedforward and feedback control strategy, the power controller and steam temperature controller were designed to adjust the plant load. The Nyquist criterion was used to assess the stability of the control system. The system responses to four different perturbations at the nominal power level were simulated, and the results show that the designed controllers have good reliability. In addition, the control performance was tested in two load change cases: a step load reduction and a linear load change. The dynamic response characteristics of the system parameters show that the developed control system based on the nonlinear distributed-parameter model is capable of fast and accurate regulation. [ABSTRACT FROM AUTHOR]

A new data‐driven approach based on initial controller parameters and initial experimental data was proposed to tune a controller such that the response of the closed‐loop and open‐loop systems are close to the desired control response. This method is called virtual internal model tuning (VIMT). In VIMT, one‐shot closed‐loop output data are required. Practically, there are cases where it is important to focus on the input rather than on the output. This study extends VIMT to the case where only one‐shot input data and initial controller parameters are obtained through closed‐loop experiments. Furthermore, we discuss the advantage of the proposed method compared to the conventional VIMT via frequency response analysis of weight function. In this study, I investigated the control performance of the proposed methods through experimental verification. © 2022 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

The vehicle active suspension has been widely used as it could effectively improve the vehicle ride comfort. To solve the issues of actuator uncertainty and time delay existing in the active suspension system, a multi-objective non-fragile H ∞ control algorithm is proposed in this work. First, to effectively describe the vehicle suspension dynamics, a quarter-car suspension model is constructed first in this study. Second, aiming at improving the ride quality and guarantee the hard constraints of the suspension system, a multi-objective H 2/ H∞ performance index is proposed. Subsequently, the issues of external disturbances, actuator uncertainty and delay are considered in the design process of the suspension controller. Then, a multi-objective non-fragile control algorithm is proposed based on an effective Lyapunov functional. The control gain can be obtained by solving a convex optimization problem in terms of linear matrix inequalities. Finally, numerical simulations are implemented on MATLAB/Simulink platform to show the robustness and superiority of the proposed controller. Experiments are implemented on a quarter-car test rig to validate the practical performance of the designed controller. [ABSTRACT FROM AUTHOR]

With an increasing focus on climate action and energy security, an appropriate mix of renewable energy technologies is imperative. Despite having considerable global potential, wave energy has still not reached a state of maturity or economic competitiveness to have made an impact. Challenges include the high capital and operational costs associated with deployment in the harsh ocean environment, so it is imperative that the full energy harnessing capacity of wave energy devices, and arrays of devices in farms, is realised. To this end, control technology has an important role to play in maximising power capture, while ensuring that physical system constraints are respected, and control actions do not adversely affect device lifetime. Within the gamut of control technology, a variety of tools can be brought to bear on the wave energy control problem, including various control strategies (optimal, robust, nonlinear, etc.), data-based model identification, estimation, and forecasting. However, the wave energy problem displays a number of unique features which challenge the traditional application of these techniques, while also presenting a number of control 'paradoxes'. This review articulates the important control-related characteristics of the wave energy control problem, provides a survey of currently applied control and control-related techniques, and gives some perspectives on the outstanding challenges and future possibilities. The emerging area of control co-design, which is especially relevant to the relatively immature area of wave energy system design, is also covered. [ABSTRACT FROM AUTHOR]

The work aims to construct a complete electronic slogging system in order to enrich the characteristics of pattern and get large slogging movement distance. Herein, first, the knitting principle of double tricot machine is studied, and the conclusion is drawn that the looping process is composed of the swing of guide bar and the slogging of guide bar. Second, taking the RD4N tricot machine as the research object, the minimum overlap angle of 25° and the minimum underlap angle of 80° are obtained, which occur on the GB1 and GB4 bar, respectively. Third, discussions are carried out over the scheme and principles of the slogging system, the controller, servo motor, etc., are analyzed and designed, an electronic slogging execution program is drafted, and the electronic cam curve adopts the non-dwell corrected trapezoidal acceleration motion curve, which completely meets the requirements of production. The actual test proves the smooth operation, precise slogging, and fast response of the control systems. [ABSTRACT FROM AUTHOR]

This paper analyses the application of a genetic algorithm (GA) for the purpose of designing the control system with separately excited DC motor controlled according to the rotor angle. The presented research is based on the utilization of a mathematical model designed with separate electrical and mechanical sub-systems. Such an approach allows fine-tuning of PID controllers by using an evolutionary procedure, mainly GA. For purpose of PID tuning, the new fitness function which combines several step response parameters with the aim of forming a unique surface which is then minimized with a genetic algorithm. From the results, it can be seen that the elitism-based algorithm achieved better results compared to the eligibility-based selection. Such an algorithm achieved a fitness value of 0.999982 resulting in a steady-state error of 0.000584 rad. The obtained results indicate the possibility of applying a GA in the parameterization of the PID controller for DC motor control.

The study aims to improve the utilization rate of water resources and meet the new national requirements for wastewater discharge indexes. The study proposes an intelligent control system for printing and dyeing wastewater treatment based on the internet of things (IoT) and deep learning (DL). Specifically, IoT is used to accurately monitor the real-time data of sewage treatment equipment and quality, and a DL structure with multi-layer nonlinear mapping is constructed. The wastewater in a printing and dyeing factory is taken as the research object, and it is treated by the printing and dyeing wastewater treatment system with "iron–carbon micro electrolysis, biological contact oxidation (BCO), and advanced oxidation process (AOP)" as the main processes. IoT and DL are combined to establish an intelligent wastewater control system for the factory. The sewage treatment system is optimized using the back propagation neural network (BPNN) in DL. And then, the intelligent operation module based on DL is designed and implemented to manage the sewage treatment system. The results show that the removal rates of chemical oxygen demand (COD) and chromaticity by iron–carbon micro electrolysis process are 70.19% and 91.32%; those of COD and ammonia nitrogen by BCO are more than 80%; and those of COD and ammonia nitrogen by AOP are 62.2% and 25.6%. This shows that through DL of the collected data and analysis of the wastewater treatment law, predicting and controlling wastewater discharge can improve the accuracy rate and efficiency of wastewater treatment and provide technical support for wastewater treatment and control. [ABSTRACT FROM AUTHOR]

In this paper, a new hybrid algorithm, based on multi-objective genetic programming and bond graphs in combination with Monte Carlo simulation, is proposed for Pareto optimal robust design of dynamical systems. For this purpose, first, an embryo model is considered for a dynamical system. Then, genetic programming is used to help this embryo model evolve through generations and achieve an optimum model. It is clear that without considering the uncertain parameters of the designing system, unwanted behaviors could occur. To overcome this, Monte Carlo simulation has been used to minimize the variations of objective functions by generating some samples around the nominal values of the uncertain parameters. In other words, the mean and variance of the objective function are minimized simultaneously through sampling, generated by Monte Carlo simulation, to reach a robust behavior. The proposed algorithm has been employed for robust design of two case studies, namely a closed-loop typewriter drive system and an optimum controller for quarter-car suspension system. A comparison between the results obtained using the proposed algorithm and those obtained through other approaches in the literature shows that more robust behavior in the presence of uncertainties can be observed in the proposed method. [ABSTRACT FROM AUTHOR]

Dynamic simulation provides a novel tool in risk assessment that is capable of predicting process behavior in different situations, quantifying unsafe process boundaries and examining the propagation of deviations through the process. This study investigated the most frequent disturbances in the integrated acid gas enrichment and amine regeneration process in the natural gas refinery including disturbances in amine regeneration column temperature, feed flow rate, pressure, and gas leakage from the top product stream. Using Aspen Plus dynamics simulations, the consequences of various deviation scenarios and a detailed analysis of the reasons for the occurrence of the consequences were elaborated. The calculated propagation time from the beginning of each disturbance to the occurrence of an accident makes it possible for the operators to plan the necessary arrangements and effective measures based on the available response time. Furthermore, the consequences of flooding and weeping leading to changes in product quality are numerically ranked to facilitate comparing and prioritizing these consequences. Finally, based on the simulation results, new protective layers were designed to prevent various high-risk consequences. The complexity of the integrated process evaluated in this study illustrates the generalizability of the HAZOP dynamic method to achieve new results in chemical process hazard analysis thus serving as a useful tool for process safety specialists.

A molten-salt reactor (MSR), in which the nuclear waste can be separated and taken away from the liquid fuel through online chemical treatment, has remarkable advantages in safety and economics. To ensure the safe operation of an MSR plant, it is necessary to carry out a controller design and stability analysis of the whole system. In this work, a nonlinear distributed-parameter model of an MSR plant with a graphite-moderated liquid core was established and verified by the reference model. By applying a joint feedforward and feedback control strategy, the power controller and steam temperature controller were designed to adjust the plant load. The Nyquist criterion was used to assess the stability of the control system. The system responses to four different perturbations at the nominal power level were simulated, and the results show that the designed controllers have good reliability. In addition, the control performance was tested in two load change cases: a step load reduction and a linear load change. The dynamic response characteristics of the system parameters show that the developed control system based on the nonlinear distributed-parameter model is capable of fast and accurate regulation.

The civil aviation sector plays an increasingly significant role in transportation sustainability in the environmental, economic, and social dimensions. Driven by the concerns of sustainability in the aviation sector, more electrified aircraft propulsion technologies have emerged and form a very promising approach to future sustainable and decarbonized aviation. This review paper aims to provide a comprehensive and broad-scope survey of the recent progress and development trends in sustainable aviation electrification. Firstly, the architectures of electrified aircraft propulsion are presented with a detailed analysis of the benefits, challenges, and studies/applications to date. Then, the challenges and technical barriers of electrified aircraft propulsion control system design are discussed, followed by a summary of the control methods frequently used in aircraft propulsion systems. Next, the mainstream energy management strategies are investigated and further utilized to minimize the block fuel burn, emissions, and economic cost. Finally, an overview of the development trends of aviation electrification is provided. [ABSTRACT FROM AUTHOR]

A practical design for a high-precision motion control system was formulated without an accurate dynamic model. The designed control system consisted of a nominal characteristic trajectory following (NCTF) control system—which can be easily designed without an accurate mechanical model—and time-invariant feedforward (FF) elements determined using a learning controller (LC). Specifically, an improved FF element design method, in which the LC was used to procedurally determine all the FF elements from the results of only four experiments, was used to enhance the motion control performance of the NCTF control system. To verify the effectiveness of the proposed framework, the NCTF control system was designed for a ball screw mechanism. This system was combined with the time-invariant FF element that is designed using the improved FF element design method. The performance of this system was compared with those of the conventional NCTF control system and the NCTF control system with the LC used as the FF element. The effectiveness of the improved FF element design method and combination of the determined FF elements and NCTF control system was experimentally demonstrated. Moreover, the enhancement in the position resolution of the FF element, which could compensate for the position-dependent factor in motion control, was verified. The average maximum error of this control system for the sinusoidal input with an amplitude of 0.2 mm and a frequency of 1 Hz is 0.209 μm, which is less than half the error of the other control systems. • Designed high-precision motion control system without accurate dynamic model. • Combined nominal characteristic trajectory following controller and time-invariant FF elements. • Improved the FF element design to be accomplished via only four experiments. • Enhanced trajectory accuracy for low-speed motion by higher positional resolution. • Effectiveness of systems experimentally validated based on ball-screw mechanism. [ABSTRACT FROM AUTHOR]

Automobile manufacturing industry is one of the basic industries in the national emerging economy. An automobile manufacturing plant includes four workshops: stamping, welding, painting and final assembly workshops. Automobile coating workshop is a workshop with a high degree of automation in the process of automobile manufacturing. Nowadays, the production mode of automobile industry has changed from batch production to customer order production, thus traditional automobile production lines can no longer meet the individual needs of automobile production. Therefore we designed a flexible automobile coating production control system based on RFID and fieldbus technology. This paper introduces the related functions of the automobile coating control system in detail, which mainly includes video monitoring system, body identification system, safety lamp system, production control system and production management system. The automobile coating control system has been put into operation in Tianjin for 3 years, and the control effect is good. [ABSTRACT FROM AUTHOR]

This paper analyses the application of a genetic algorithm (GA) for the purpose of designing the control system with separately excited DC motor controlled according to the rotor angle. The presented research is based on the utilization of a mathematical model designed with separate electrical and mechanical sub-systems. Such an approach allows fine-tuning of PID controllers by using an evolutionary procedure, mainly GA. For purpose of PID tuning, the new fitness function which combines several step response parameters with the aim of forming a unique surface which is then minimized with a genetic algorithm. From the results, it can be seen that the elitism-based algorithm achieved better results compared to the eligibility-based selection. Such an algorithm achieved a fitness value of 0.999982 resulting in a steady-state error of 0.000584 rad. The obtained results indicate the possibility of applying a GA in the parameterization of the PID controller for DC motor control. [ABSTRACT FROM AUTHOR]

Although magnetic bearings are generally reliable, an important concern for machine applications is fault tolerance. The fault-tolerant operation of a magnetic bearing system can be realized through the identification and isolation of fault actuators and subsequent support reconstruction via residual actuators. In this study, a novel fault-diagnosis algorithm for electromagnetic actuators (EMAs) is proposed. The equivalent slope of the load current is defined as a fault-diagnosis threshold by theoretically analyzing the variation characteristics of the current in the modulation. The synchronous sampling of the current and its processing algorithm are designed. By combining these algorithms with the generalized bias current linearization theory reported previously, a fault-tolerant control (FTC) system for magnetic bearings is developed, and control of multi-position and multi-current loops is realized by using two digital signal processors (DSP). The FTC of the rotor motion and the fault-diagnosis of actuators are executed in parallel. The proposed control system is verified by the experiments, and experimental results demonstrate that the proposed system can cope with EMA fault in an extremely short time of less than 5 ms. Thus, the support can be reconstructed by employing the FTC to maintain rotor stability.

Mode transition (MT) is critical for adaptive cycle engine (ACE) to achieve mission adaptability. There are many constraints for the MT, making the control law difficult to design. At present, most design methods require manual analysis by researchers. When the design parameters of an engine are changed, it takes a lot of time to redesign the control law. What is more, most control laws need to adjust not only the mode selection valve (MSV), but also many other variable geometry components (VGCs) simultaneously, which makes the control system design complex and difficult. In this study, a general design method based on the particle swarm optimization method and the sensitivity calculation method is presented. This method can derive the control law for the MT without manual analysis, improving the efficiency of the design process. At most, one VGC needs to be adjusted during the change of state of the MSV, simplifying the control system. The fluctuation of thrust and airflow is less than 2%, and the surge margin of the compressors is higher than 10%, leading to a safe and stable MT. This method can adapt to the adjustment of the cycle parameters of the engine and the deviation of component performance, making it suitable for the engine performance design stage. It can also be applied to the design for other types of ACEs. [ABSTRACT FROM AUTHOR]

In the construction industry, the cement floor is often polished by manual and mechanical methods. The working environment is harsh and the working efficiency is low. The automatic polishing robot can replace manual operation and improve the working efficiency. In this paper, the basic motion of the cement floor polishing robot is studied, and the motion control scheme of the robot is proposed. Firstly, the kinematics model of the robot and the speed control algorithm to realize the basic motion were designed. Then, the design of the motion control system of the cement ground grinding robot was designed with STM32 single chip microcomputer as the control core, and the basic motion control in the complex trajectory of the robot was realized. Experimental results show that the maximum positioning error of the robot is 3.1 cm when it moves in a straight line and turns. The reliability and stability of the system for the basic motion control of the robot are verified, which provides a reference for the control of similar robots.

Automated path following is one of the major problem in automated vehicle control. Automated vehicle should follow the desired path to minimize the lateral deviation from the path while traveling at the desired speed. In this paper, LQI control method is proposed to solve this problem. LQI based control system is designed based on speed dependent gain scheduling taking into account the effect of vehicle speed on vehicle dynamic behaviour. The proposed method is tested in a simulation environment with a high degree-of-freedom nonlinear vehicle model including steer-by-wire steering actuator model. The performance of the proposed control system is compared with PID and LQR based control systems in two different simulation scenarios. Statistical error values are used for numerical comparison of different control methods. Simulation results and error values show that speed dependent gain scheduled LQI control system equipped automated vehicle follows the desired path with less error at constant and variable vehicle speed.

New parameter‐tuning methods, called data‐driven tuning, have been proposed, which can derive the controller parameters without using plant models. These include iterative feedback tuning (IFT), virtual reference feedback tuning (VRFT), and fictious reference iterative tuning (FRIT). In this study, a simple data‐driven tuning method is proposed. Through closed‐loop experiments, one‐shot input data and the initial controller parameters are obtained, based on which the control system is tuned. The tuned control system can realize a high tracking performance, and the desired response can be obtained. Furthermore, the validity of the proposed method is investigated through numerical simulation and experiments, which establish that the control performance is improved by the proposed parameter‐tuning method. The proposed method can obtain appropriate controller parameters. © 2021 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]