Control Method For Systems Research Articles

Adaptive Robust Tracking Control of Robotic Manipulator based on SMC and Fuzzy Control Strategy

In recent years, robotic systems have been widely used in different applications, and this has motivated researchers to develop different control methods. A model-free, intelligent, robust control method for a nonlinear robotic manipulator system is proposed in this work. This paper presents a novel solution for the major drawbacks of the sliding mode control scheme, which are chattering. Prior knowledge is needed about the dynamic model of the controlled system and the upper bound of uncertainty. In this paper, a fuzzy-like PD controller with SMC (FLPDSM) is proposed. The fuzzy-like PD controller was designed according to fuzzy rules and membership functions based on the nominal model of the robot manipulator. A robust control term was added to the control signal to compensate for the system uncertainty, and external disturbances are compensated by adding an auxiliary robust term to the SMC control law. Two methods for designing robust control terms are proposed. The first proposed method assumes that the upper bound of system uncertainty is known although it cannot be exactly determined due to external disturbances and uncertainty. Hence, a second method was proposed that assumes this bound to be unknown, and an adaptive gain based on Lyapunov theory was used to derive the adaptation law. The Lyapunov second method was used to ensure the stability of the closed loop system. Performance tests on the proposed methods were implemented through simulation studies for the two-link robotic manipulator, and the test results were compared with the standard SMC to verify the effectiveness of the proposed method. A good trajectory tracking with a high robustness against parameter variations and external disturbances was observed under the presented control scheme.

An SMC-Based Accurate and Robust Load Speed Control Method for Elastic Servo System

In this paper, a modified sliding mode speed controller is designed in order to improve the load speed control performance of an elastic motor drive system with internal and external disturbances. Firstly, based on the load speed deviation and its first-order and second-order differentials, a two-dimensional sliding surface function is presented. The parameters of the sliding surface are determined by a novel particle swarm optimization (PSO) method. The method has improved inertia weights and evaluation functions, which can improve the accuracy and efficiency of the parameter search and enhance the immunity and robustness of the system. Secondly, on the basis of the integral of time-weighted absolute error (ITAE) criterion, the parameters of the exponential approach law are determined, which improves the tracking accuracy. Finally, the designed sliding mode control (SMC) with high tracking accuracy, fast dynamics, and strong robustness is verified by both simulations and experiments.

Load Frequency Control Method for Cyber-Physical Power Systems With 100% Renewable Energy

Load Frequency Control Method for Cyber-Physical Power Systems With 100% Renewable Energy

A High-Interoperability Optimal Frequency Control Method for the AGV Dynamic Wireless Charging Systems Without Communication

A High-Interoperability Optimal Frequency Control Method for the AGV Dynamic Wireless Charging Systems Without Communication

Human-like acceleration and deceleration control of a robot astronaut floating in a space station

The acceleration and deceleration (AD) motions are the basic motion modes of robot astronauts moving in a space station. Controlling the locomotion of the robot astronaut is very challenging due to the strong nonlinearity of its complex multi-body dynamics in a gravity-free environment. However, after training, humans can move well in space stations by pushing the bulkhead, and the motion mechanism of humans is a good reference for robot astronauts. The contribution of this study is modeling the human AD motion in a microgravity environment and proposing a human-like control method for robot astronauts moving in space stations. Specifically, the movement and contact force data of the human body during AD motion were collected on an air-floating platform. Through human AD modeling analysis, the mechanism of human motion is discovered, and semi-sinusoidal primitives of contact forces are proposed for AD motion. Then, a dynamic guidance model of human AD motion is built to complete motion planning under contact constraints, which is used as the expected model for the AD control of robot astronauts. Benefiting from the force primitives, accurate and safe planning of human-like AD motion can be completed. The characteristics and mechanism of human AD motion have been analyzed from the perspective of optimization. Lastly, based on the proposed dynamic guidance model, the AD motion policy is mapped to the robot astronaut system via a system control method based on the equivalent mapping of dynamic responses (force, velocity and pose). Through comparative analysis with real human motion data and simulation results under different conditions, the proposed AD control method can achieve human-like motion efficiently and stably. Even when confronted with errors in the robot’s contact velocities and inertia parameters, the method can significantly reduce the motion errors while ensuring stability.

PD-Based Iterative Learning Control for the Nonlinear Low-Speed-Jitter Vibration of a Wind Turbine in Yaw Motion

Aiming at the nonlinear low-speed-jitter (LSJ) vibration suppression for a yaw system of a megawatt wind turbine, a kinematics mechanism of the yaw system is investigated from the perspective of tribology, and a kinematics model of the yaw system based on an equilibrium position is established. On the basis of the dynamic modeling of the yaw system, a nonlinear mathematical model of the LSJ system is deduced. Based on the two lead motors’ driving of the conventional yaw motion, an innovative design with a special installation of two auxiliary motors for yaw transmission is carried out, which is integrated with a matching centralized lubrication system (CLS). Based on open-loop proportional-derivative (PD) control and the iterative learning control methods of the time-varying continuous system, the stability control and jitter amplitude suppression of the yaw system are realized by using a combined driving torque provided by the lead and auxiliary gears. From the stability and convergence of the time-domain response and the convergence of the iterative error, the effectiveness of the iterative learning control method with the PD-based regulation is verified, and its advantages for engineering applications are shown based on the algorithm solver improvement. The feasibility of the physical realization and engineering application of the control methodology is verified by using controller-hardware-in-the-loop (C-HITL) simulation technology.

A single-dynamic-gain scaling control method for nonlinear systems with measurement uncertainty

A single-dynamic-gain scaling output feedback control method is proposed with the novelty lying in (i) constructing a couple of observer-controller with the modification of scaling-gain exponent and (ii) introducing time-varying coupled matrix inequalities to deal with the large measurement uncertainty. By combining with the non-identification adaptive mechanism, the proposed dynamic output feedback controller is applicable to a class of nonlinear systems in the absence of the knowledge of upper bounds of unknown parameters in the nonlinearities and output equation, and global state asymptotical regulation is achieved with a bounded adaptive gain. Especially, the uncertain parameters in the nonlinearities and (non-differentiable) output function are both handled by a single-scaling gain. Moreover, the control method of this paper is non-backstepping and all design parameters are easy to obtain.

Research on two-stage optimization control method for energy storage systems based on multi service attribute utility evaluation

ABSTRACT With the high proportion of new energy access and the increasing demand for load electricity, efficient and reasonable control of battery energy storage systems (BESS) in the power grid is the key to promoting new energy consumption, improving the quality and economy of power supply in the power grid. To solve the optimization control problem of BESS under multiple power service objectives, a two-stage optimization control method for BESS based on multi-service attribute utility evaluation has been proposed in this paper. Firstly, based on various power service demands and grid operation constraints, a multi-objective optimized scheduling model for BESS has been established; Then, to solve the complex coupling problem between multiple optimization objectives of BESS, a two-stage optimization structure has been designed that the changes of various service indicators under different optimization objectives have been recognized in the first stage, and the entropy weight method was used to weight each optimization objective in the second stage objectively; Finally, in the IEEE 33-bus simulation example, compared with the suboptimal result under each single-objective method, the relative error of the power line losses, the maximum voltage deviation, the curtailment of new energy and the economics of power grid of this algorithm has been reduced at least 2.31, 1.11, 2.90, and 1.05 times, respectively. Meanwhile, the performances of active power reserve and reactive power reserve are better than most single-objective optimizations.

Modeling and optimization of a passive building HVAC system based on improved PSO and H-J Algorithm

Passive buildings employ the principles of active optimization and passive priority to demonstrate their energy-saving, comfortable, and cost-effective qualities, thereby addressing energy consumption in an efficient manner. However, it cannot be ignored that the Heating Ventilation Air Conditioning (HVAC) system is still the largest energy consumer, especially when it is necessary to maintain the comfort of the indoor environment. Consequently, the control and optimization of the HVAC system is a research hotspot and a challenge in the field of building energy conservation, as it is the key to achieving ultra-low energy consumption operation in passive buildings while ensuring the thermal comfort of the indoor environment. This paper presents a comprehensive modeling, control, and optimization framework for the efficient operation of HVAC systems in a passive building located in Jinan, China. Firstly, the HVAC system is modeled using the transient system simulation tool (TRNSYS), GenOpt, Java, and other software. Secondly, the HVAC system’s year-round operation is simulated using specific configurations. In order to enhance the energy-saving effect, a strategy of increasing heat storage and return air ducts was proposed, and a significant energy-saving effect (approximately 17.31%) was realized. Lastly, the data communication interface with GenOpt is established utilizing the TRNOPT module, a method for actively optimizing the refrigerant temperature is proposed, and recalculation and optimization are carried out utilizing the swarm intelligence algorithm and the pattern search algorithm (Hooker-Jeeves algorithm, H-J algorithm). The final optimization results demonstrate a 19% reduction in energy consumption relative to the baseline heat recovery model, validating the applicability of the optimization method under this strategy. The control and optimization method of the HVAC system proposed in this paper can further exploit the energy-saving potential of the passive building HVAC system and will contribute to the advancement of building energy conservation.

Field-Oriented Control of a Nine-Phase Cage Induction Generator with Large Speed Changes and Variable Load

This paper presents a voltage control system for multiphase squirrel-cage induction generators operating at a high variability of speed and variable load. Field-oriented vector control was used with a change in the sequence of the stator phase currents what changes the number of poles of the magnetic field produced by nine-phase stator winding. At low speeds, the current sequence is changed so that the number of poles increases allowing for the desired voltage to be obtained with greater efficiency. The task of the automatic control system was to control the DC voltage to a desired value at the output of the multiphase PWM converter. This is an alternative control method to the scalar control of voltage and frequency presented in a previous work. The control method and parameters of the automatic control system result from the mathematical model of the multiphase induction machine. The results of the laboratory tests were compared with the effects of the operation of the same nine-phase scalar controlled generator.

Force Tracking Control Method for Robotic Ultrasound Scanning System under Soft Uncertain Environment

Robotic ultrasound scanning has excellent potential to reduce physician workload, obtain higher-quality imaging, and reduce costs. However, the traditional admittance control strategy for robotics cannot meet the high-precision force control requirements for robots, which are critical for improving image quality and ensuring patient safety. In this study, an integral adaptive admittance control strategy is proposed for contact force control between an ultrasound probe and human skin to enhance the accuracy of force tracking. First, a robotic ultrasound scanning system is proposed, and the system’s overall workflow is introduced. Second, an adaptive admittance control strategy is designed to estimate the uncertain environmental information online, and the estimated parameters are used to modify the reference trajectory. On the basis of ensuring the stability of the system, an integral controller is then introduced to improve the steady-state response. Subsequently, the stability of the proposed strategy is analysed. In addition, a gravity compensation process is proposed to obtain the actual contact force. Finally, through a simulation analysis, the effectiveness of the strategy is discussed. Simultaneously, a series of experiments are carried out on the robotic ultrasound scanning system, and the results show that the strategy can successfully maintain a constant contact force under soft uncertain environments, which effectively improves the efficiency of scanning.

Energy and comfort aware operation of multi-zone HVAC system through preference-inspired deep reinforcement learning

This paper proposes a novel optimal control method for multi-zone HVAC systems to enhance energy efficiency and improve occupant comfort. To address the disturbances in outdoor weather and indoor loads, the proposed method formulates the HVAC control problem as a dynamic Markov decision process, and employs deep reinforcement learning (DRL) techniques to obtain the optimal control policy. To manage multiple goals among thermal comfort, indoor air quality (IAQ) and system energy efficiency, a preference-inspired (P-ins) mechanism is designed to achieve the optimal balance among different objectives. The P-ins mechanism effectively guides the agent towards the optimal control policy with high convergence rate. The proposed method has been validated through EnergyPlus-Python co-simulation testbed with real-world data traces, and assessed by an overall evaluation indicator. Results demonstrate that the proposed method can reduce energy consumption without compromising thermal comfort and IAQ. Specifically, the occurrence of temperature violations is reduced below 0.8 %, and a maximum energy saving of 9.41 % can be achieved, compared with traditional methods.

Soil solarization as a non-chemical weed control method in tree nursery production systems of the Pacific Northwest, USA

IntroductionHerbicide application in tree nurseries is limited because of the potential for chemical injury to the large diversity of trees species grown, the lack of registered products, and increasing restrictions on herbicide use, necessitating the costly practice of hand weeding. Soil solarization can reduce the weed seedbank by trapping solar energy under clear plastic film, resulting in high soil temperatures lethal to imbibed weed seeds and seedlings. The objective of this study was to determine if soil solarization would be an effective weed management strategy in Pacific Northwest, USA, tree production systems.MethodsField studies were conducted at three commercial tree nurseries in Oregon and Washington over two years to test soil solarization in reducing the naturally occurring weed seedbank and the time required to hand weed fields. Further field and laboratory tests were conducted with five weed species: Poa annua, Polygonum pensylvanicum, Amaranthus retroflexus, Portulaca oleracea, and Cyperus esculentus. Weed seeds and tubers were buried in packets at 5 and 10 cm to determine their viability after 6 weeks of solarization. A laboratory study was conducted with all but C. esculentus to quantify the exposure time at 45, 50, and 55°C required for 90% death (T90).ResultsSoil solarization was particularly effective in reducing the emergence of naturally occurring weeds in the fall and winter, when weed emergence was reduced by 94-96%. Emergence was reduced 67-81% during the subsequent spring and early summer. Nine to ten months after solarization, solarized areas had a 52 – 69% reduction in hand weeding time compared to non-solarized areas. In field trials with buried seed and tuber packets, mortality differed by location and depth, with P. annua and P. pensylvanicum having the greatest percent seed mortality followed by A. retroflexus and variable results for P. oleracea and C. esculentus. In lab studies, seed mortality differed depending on species and temperature; however, at 55°C, there was a relatively rapid drop in seed viability for all species, and T90 values ranged from 1.2 to 41 h whereas at 45°C the range was 47 to > 3000 h. Similar to the field studies, P. annua and P. pensylvancium were more sensitive to heat, followed by A. retroflexus and P. oleracea.ConclusionSoil solarization can be an effective weed management tool in reducing the weed seedbank in Pacific Northwest tree nurseries and other fall-sown crops but may not work for certain, thermotolerant weed species such as C. esculentus.

Development and performance evaluation of intelligent algorithm for optimal control of a hybrid heat pump system during the cooling season

The purpose of this study is to develop a control method for a hybrid heat pump system based on an artificial neural network (ANN) to reduce energy use and create a more comfortable thermal environment. The proposed optimal control method uses an ANN-based predictive model to predict the heat storage tank and indoor temperature during the cooling period and controls the flow rate of the circulation pump on the heat source and load side of the system. The performance of the predictive model for the heat storage tank temperature (R2 = 0.9988; coefficient of variation of the root mean square error [CV(RMSE)] = 1.06 %; normalized mean bias error [NMBE] = 0.16 %; and mean absolute error [MAE] = 0.09℃) and the indoor temperature (R2 = 0.9893; CV(RMSE) = 1.66 %; NMBE = 0.16 %; and MAE = 0.15℃) was excellent. The temperature control of the heat storage tank using the optimal algorithm exhibited an improvement of 18.39 % for the CV(RMSE), 3.10 % for the NMBE, and 1.31 °C for MAE compared with rule-based. For the indoor temperature, the optimal algorithm improved the CV(RMSE) by 1.30 %, NMBE by 0.42 %, and MAE by 0.29℃ compared to rule-based. The energy use was reduced by 52.85 % for the entire system using the optimal control method compared with the existing control strategy under similar outdoor conditions. Using the proposed control method, it is thus possible to improve thermal comfort and reduce carbon emissions in the building sector by improving the control and energy performance of hybrid heat pump systems.

Research on system integration and control methods of an apple-picking robot in unstructured environment

PurposeThe purpose of this article is to solve the issue that apple-picking robots are easily interfered by branches or other apples near the target apple in an unstructured environment, leading to grasping failure and apple damage.Design/methodology/approachThis study introduces the system units of the apple-picking robot prototype, proposes a method to determine the apple-picking direction via 3D point cloud data and optimizes the path planning method according to the calculated picking direction.FindingsAfter the field experiments, the average deviation of the calculated picking direction from the desired angle was 11.81°, the apple picking success rate was 82% and the picking cycle was 11.1 s.Originality/valueThis paper describes a picking control method for an apple-picking robot that can improve the success and reliability of picking in an unstructured environment and provides a basis for automated and mechanized picking in the future.

Self-tuning PID feedback control method for magnetic suspension active vibration isolation system with parameters uncertainty

A self-tuning PID (Proportion-Integral-Derivative) control method based on the adaptive mechanism was proposed for the magnetic suspension active vibration isolation system with the characteristics of nonlinear, time-varying, and model parameter uncertainty. The controller identified the controlled object parameters online and solved the controller parameters in real time. To test the effectiveness of the proposed method, a six degree-of-freedom (DOF) magnetic suspension active vibration isolation system was designed and built. The six DOF dynamic model of the vibration isolation system with uncertain model parameters was derived for control. Experiments were carried out and compared with the control effects of the tradition PID control and cascade PID control algorithm. The experimental results show that the proposed self-tuning PID controller has more outstanding practicality and effectiveness in solving the model parameter uncertainty problem compared with the existing studies at home and abroad, and it also proves that the control method has a good isolation effect on the wide band disturbances.

Improving the incremental conductance control method of a solar energy conversion system

In the context of renewable energy, this study treats the case of the conversion of solar energy, which is one part of non pollutant energy, to electrical one. Maximizing methods of the generated power is discussed in this paper. Thus, the case of solar panel is studied and a compariso between different strategies of the incremental conductance method in order to maximize the output power is given, analyzed, compared and discussed.

Experimental graybox quantum system identification and control

Understanding and controlling engineered quantum systems is key to developing practical quantum technology. However, given the current technological limitations, such as fabrication imperfections and environmental noise, this is not always possible. To address these issues, a great deal of theoretical and numerical methods for quantum system identification and control have been developed. These methods range from traditional curve fittings, which are limited by the accuracy of the model that describes the system, to machine learning (ML) methods, which provide efficient control solutions but no control beyond the output of the model, nor insights into the underlying physical process. Here we experimentally demonstrate a ‘graybox’ approach to construct a physical model of a quantum system and use it to design optimal control. We report superior performance over model fitting, while generating unitaries and Hamiltonians, which are quantities not available from the structure of standard supervised ML models. Our approach combines physics principles with high-accuracy ML and is effective with any problem where the required controlled quantities cannot be directly measured in experiments. This method naturally extends to time-dependent and open quantum systems, with applications in quantum noise spectroscopy and cancellation.

Superheat matching control method for a roll-bond photovoltaic-thermal heat pump system

In this paper, a superheat matching control method (SMCM) for a roll-bond photovoltaic-thermal heat pump (RB-PVT-HP) system is proposed to achieve adaptive control of the target superheat under different operating conditions. The target superheat was determined based on a comprehensive consideration of the stable running superheat and the optimally performing superheat of the system. The effectiveness of the SMCM was experimentally verified. The trigeneration performance tests of the system under the SMCM were conducted in the summer. Results show that the average deviation degrees of superheat were 0.48 °C and 2.09 °C in the heating and cooling processes, respectively. In the heating process, the average input power of the compressor, heating power and coefficient of performance of the system were 5.40 kW, 17.90 kW, and 3.31, respectively, across four days of continuous operation. The average photoelectric conversion efficiency, comprehensive energy output capacity and comprehensive performance of thermal-and-electrical generation were 14.19 %, 21.29 kW and 34.76 %, respectively, during daytime operation. In the cooling process, the average input power of the compressor, cooling power and energy efficiency ratio of the system were 3.82 kW, 7.72 kW, and 2.02, respectively, during nighttime operation.

Control of the maglev system with a low stiffness elastic track beam by pole assignment and a reduced order state observer

A maglev train system is an open loop unstable and strongly nonlinear system. Self-excited coupling vibration between the magnet and the track beam can occur even when the train is stationary. To avoid this, the track beam is generally designed with high stiffness, which directly increases the construction cost of the maglev line. In this research, a new control method for the maglev system with a low stiffness elastic track is proposed based on full state feedback theory and pole assignment theory. In the newly designed controller, track beam DOF is introduced into the control loop with the use of the reduced order state observer (ROSO) to make the control system have the ability to suppress track vibration actively, thus reducing the over dependence of the system stability on track properties. Effectiveness of the proposed control strategy is verified through experiments. Results show that it exhibits a better response compared to the traditional control method, with smaller fluctuations in levitation air gap and magnet acceleration. Furthermore, it can maintain system stability with relatively lower track stiffness, reducing the minimum requirements for track stiffness by 53.4% in the test.