Optimal Control Performance Research Articles

Autonomous real-time control for membrane capacitive deionization

Artificial intelligence has been employed to simulate and optimize the performance of membrane capacitive deionization (MCDI), an emerging ion separation process. However, a real-time control for optimal MCDI operation has not been investigated yet. In this study, we aimed to develop a reinforcement learning (RL)-based control model and investigate the model to find an energy-efficient MCDI operation strategy. To fulfill the objectives, we established three long-short term memory models to predict applied voltage, outflow pH, and outflow electrical conductivity. Also, four RL agents were trained to minimize outflow concentration and energy consumption simultaneously. Consequently, actor-critic (A2C) and proximal policy optimization (PPO2) achieved the ion separation goal (<0.8 mS/cm) as they determined the electrical current and pump speed to be low. Particularly, A2C kept the parameters consistent in charging MCDI, which caused lower energy consumption (0.0128 kWh/m3) than PPO2 (0.0363 kWh/m3). To understand the decision-making process of A2C, the Shapley additive explanation based on the decision tree model estimated the influence of input parameters on the control parameters. The results of this study demonstrate the feasibility of RL-based controls in MCDI operations. Thus, we expect that the RL-based control model can improve further and enhance the efficiency of water treatment technologies.

Observer‐based optimal fault‐tolerant tracking control for input‐constrained interconnected nonlinear systems with mismatched disturbances

SummaryThis paper investigates an observer‐based optimal fault‐tolerant tracking control problem for interconnected nonlinear systems with input constraints and mismatched disturbances via adaptive dynamic programming (ADP). Firstly, an augmented system consisting of tracking error and the reference trajectory is constructed, and then the original optimal tracking control problem is transformed into the optimal regulation control problem of the augmented system. An integral sliding‐mode‐based optimal fault‐tolerant control scheme is developed to eliminate the effect of actuator faults and guarantee the optimal control performance. Furthermore, a neural network‐based observer is designed to identify the completely unknown system dynamics based on the input‐output data, thereby relaxing the restriction on system dynamics. Subsequently, the modified Hamilton‐Jacobi‐Bellman equations are solved by using the ADP algorithm under a critic network framework. According to the Lyapunov approach, all signals in the closed‐loop augmented system are uniformly ultimately bounded. Finally, the effectiveness of the developed control scheme is demonstrated via simulation results.

Generalized uniform optimization for robust adaptive Buck converter with uncertain perturbations

This paper presents an event-triggered impulse (ETI) control mechanism with generalized uniform optimization, endowing robust adaptive for various uncertain Buck converter perturbations. An impulse compensates for the state fluctuation error, and the event triggering mechanism (ETM) is introduced into the control process of the stochastic model of the Buck converter. The mean square stability theory of linear matrix inequality (LMI) stochastic systems is provided, wherein the cost function incorporates uncertain perturbation-induced state feedback impulses and adaptive system tuning. Unlike traditional event-triggered control with manually determined triggering times, ETI is activated only when events conform to the optimal design, significantly enhancing error-tracking accuracy and adaptive capability. Moreover, the parameters of the optimal controller remain independent of the statistical characteristics of uncertain noise, ensuring globally optimal operational control and model generality. Namely, the proposed method demonstrates versatility by extending its application to DC motor control, addressing various perturbations Buck converters encounter. Finally, the designed controller can closely track the required angular velocity trajectory across sudden changes in system uncertain perturbation.

Energy control and block performance optimization of bench blasting

In this study, rock-like model blast tests with varying toe burdens are conducted and the evolution of blast-induced fractures is analyzed using the digital image correlation (DIC) method. In addition, post-blast fragmentations are image-processed to quantitatively investigate the blasting performance. A three-dimensional numerical model is developed and validated against the experimental results. The effects of blasthole inclination (70°, 80°, and 90°) and short delays on fragment size distribution (FSD) are numerically investigated. Field trials are conducted to verify the numerical results. The experimental results reveal that the overall fragment size increases significantly in proportion to the toe burden. The explosion energy utilization rates range from 3.56 % to 13.16 %, with a tendency to initially increase and subsequently decrease. The numerical results demonstrate that fragments are more evenly distributed with a blasthole inclination of 70°, resulting in a remarkable improvement in rock fragmentation compared to a vertical blasthole. Compared to the toe burden, the influence of short delays on concrete fragmentation is insignificant. The field test results indicate that a narrower FSD range and stable pit walls are achieved by utilizing inclined blastholes in bench blasting. This study provides theoretical guidance for optimizing bench blasting parameters, which has practical significance for improving explosive energy utilization and production efficiency in open-pit mines.

Compensator-Based Self-Learning: Optimal Operational Control for Two-Time-Scale Systems With Input Constraints

Compensator-Based Self-Learning: Optimal Operational Control for Two-Time-Scale Systems With Input Constraints

First- and Second-Order Sliding Mode Control Design for Networked 2-D Systems Under Round-Robin Protocol.

This article investigates the sliding mode control (SMC) problem for a class of uncertain 2-D systems described by the Roesser models with a bounded disturbance. In order to reduce the communication usage between the controller and the actuators, it is supposed that only one actuator node can gain the access to the network at each sampling time along horizontal or vertical direction, where a proper 2-D round-robin protocol is designed to periodically regulate the access token and a set of zero-order holders (ZOHs) is employed to keep the other actuator nodes unchanged until the next renewed signal arrives. Based on a novel 2-D common sliding function, a token-dependent 2-D SMC scheme with first-order sliding mode is appropriately constructed to cope with the impacts from the periodic scheduling signal and the ZOHs. Furthermore, a novel super-twisting-like 2-D SMC scheme with second-order sliding mode is designed to improve the robustness against the bounded disturbance. By resorting to token-dependent Lyapunov-like function, sufficient conditions are obtained to guarantee the ultimate boundedness of the horizontal and vertical states as well as the 2-D common sliding function. For acquiring the optimized gain matrices, two searching algorithms are formulated to solve two optimization problems arising from finding optimized control performance. Finally, two comparative examples are exploited to demonstrate the effectiveness and the advantageous of the proposed first- and second-order 2-D SMC design schemes under round-robin scheduling mechanism.

Adaptive Optimal Tracking Control of an Underactuated Surface Vessel Using Actor-Critic Reinforcement Learning.

In this article, we present an adaptive reinforcement learning optimal tracking control (RLOTC) algorithm for an underactuated surface vessel subject to modeling uncertainties and time-varying external disturbances. By integrating backstepping technique with the optimized control design, we show that the desired optimal tracking performance of vessel control is guaranteed due to the fact that the virtual and actual control inputs are designed as optimized solutions of every subsystem. To enhance the robustness of vessel control systems, we employ neural network (NN) approximators to approximate uncertain vessel dynamics and present adaptive control technique to estimate the upper boundedness of external disturbances. Under the reinforcement learning framework, we construct actor-critic networks to solve the Hamilton-Jacobi-Bellman equations corresponding to subsystems of surface vessel to achieve the optimized control. The optimized control algorithm can synchronously train the adaptive parameters not only for actor-critic networks but also for NN approximators and adaptive control. By Lyapunov stability theorem, we show that the RLOTC algorithm can ensure the semiglobal uniform ultimate boundedness of the closed-loop systems. Compared with the existing reinforcement learning control results, the presented RLOTC algorithm can compensate for uncertain vessel dynamics and unknown disturbances, and obtain the optimized control performance by considering optimization in every backstepping design. Simulation studies on an underactuated surface vessel are given to illustrate the effectiveness of the RLOTC algorithm.

Optimization Design of Semi-active Suspension for Vehicles Based on LQR Optimal Control

Abstract The suspension system is crucial for providing a comfortable ride and ensuring smooth driving. This article presents a random road surface model and a 1/4 vehicle two-degree-of-freedom semi-active suspension model. The optimal control performance index function was obtained by using the minimum principle, and the optimal controller for semi-active suspension was designed. Simulations were conducted in MATLAB/Simulink, using vehicle acceleration, suspension dynamic deflection, and tire dynamic deformation as evaluation indicators. The study found that the vehicle’s acceleration, suspension dynamic deflection, and tire dynamic deformation were reduced by 21.79%, 21.86%, and 9.88%, respectively, with the use of the designed semi-active suspension optimization controller. This resulted in improved driving smoothness and riding comfort.

Equivalent damping ratio oriented investigation on tuned negative stiffness inerter damper for seismic application

Additional equivalent damping ratio (EDR) plays a crucial role in the performance evaluation and design methodology of conventional passive dampers. The inclusion of inerter elements has been extensively demonstrated to significantly enhance the control efficiency of these passive dampers. Recently, negative stiffness (NS) elements have been found potential to enhance the control performance of inerter-based dampers (IBDs). However, little research has investigated the enhancement effect of NS elements on the additional EDR of IBDs. Meanwhile, the existing design methodology for IBDs is response-based, posing challenges in ensuring optimal control efficiency and fully utilizing the essential features of inerter and NS elements. In this study, the tuned negative stiffness inerter damper (TNSID), which integrates the NS element into one of the classical IBDs, namely the tuned inerter damper (TID), is investigated in terms of EDR for seismic application. This study makes two primary contributions. Firstly, it proposes an optimization design methodology aimed at achieving optimal control performance and efficiency of the TNSID simultaneously. Secondly, it unveils the enhancement law for the control performance and efficiency of TNSID. To do this, the analytical formulas for additional EDR and EDR enhancement (EDRE) factors are established as indicators of control performance and efficiency, respectively. Closed-form expressions are derived to optimize the TNSID design parameters. The comparative studies are conducted on both single and multi-degree-of-freedom primary structures, utilizing closed-form expressions, frequency transfer functions, and time history analysis. Results demonstrate that the incorporation of the NS element and the proposed optimization design methodology can further enhance both the control performance and efficiency of TNSID, surpassing those achieved by TID and classical fixed-point optimization theory.

Optimal orbit transfer of single-tether E-sail with inertially fixed spin axis

This study analyzes the optimal transfer trajectory of a spacecraft propelled by a spin-stabilized electric solar wind sail (E-sail) with a single conducting tether and a spin axis with a fixed direction in an inertial (heliocentric) reference frame. The approach proposed in this study is useful for rapidly analyzing the optimal transfer trajectories of the current generation of small spacecraft designed to obtain in-situ evidence of the E-sail propulsion concept. In this context, starting with the recently proposed thrust model for a single-tether E-sail, this study discusses the optimal control law and performance in a typical two-dimensional interplanetary transfer by considering the (binary) state of the onboard electron emitter as the single control parameter. The resulting spacecraft heliocentric trajectory is a succession of Keplerian arcs alternated with propelled arcs, that is, the phases in which the electron emitter is switched on. In particular, numerical simulations demonstrated that a single-tether E-sail with an inertially fixed spin axis can perform a classical mission scenario as a circle-to-circle two-dimensional transfer by suitably varying a single control parameter.

Knowledge-shareable adaptive deep dynamic programming for hierarchical generation control of distributed high-percentage renewable energy systems

The increased proportion of distributed renewable energy sources connected to systems leads to frequency regulation difficulty, such as uncoordinated algorithms, inconsistent optimization and control objectives, and long training time. This work proposes a knowledge-shareable adaptive deep dynamic programming (KSADDP) to improve the active control accuracy and generation economy of the proposed hierarchical generation control framework, which contains two-layer control structures. In the first layer, droop control is employed to optimize the outputs of controllable generators, follow the frequency changes, reduce frequency deviation, and improve the operation economy of battery energy storage systems. In the second layer, a KSADDP is proposed to mitigate local optimum and accelerate the hybrid cooperative structure optimization by sharing the parameters of neural networks. These two layers synergistically optimize control performance and reduce generation costs. The simulation results of a three-high-percentage renewable energy system show that: with two layers of cooperation and sharing training information, the KSADDP outperforms the comparison algorithms in seven evaluation indices and reduces the generation cost by 4.85%–7.41 % compared to the comparison algorithms. The proposed KSADDP cooperates the time scales between multiple layers, cooperates the objectives between multiple layers, increases the parameter-sharing process, and reduces training time.

Analytical models of the electric field and carrier transit time in the base of bipolar junction transistors

The conventional theory of the bipolar junction transistor (BJT) does not clarify the electric field and its effect on the carrier transit in the uniformly doped base region, although the electric field is considered to be a second-order effect at high-level injection. In this study, analytical models of the carrier distribution, electric field, and carrier transit time in the base region are developed without making additional assumptions. It is found that the electric field may change direction, from forward to reverse, as long as the injection level reaches a criterion, affecting the carrier transport behavior. The models are based on the renovated p–n junction theory, provide insight into the carrier transit and are able to cover all levels of injection without the need to handle the low- and high-level injection separately. These models hold significance in retrieving the theoretical integrity of bipolar junction devices, and can be useful for further investigation in structure design, capacitance control, and AC performance optimization of the p–n junction-based device.

Design and Development of Complex-Order PI-PD Controllers: Case Studies on Pressure and Flow Process Control

This article examines the performance of the proposed complex-order, conventional and fractional-order controllers for process automation and control in process plants. The controllers are compared regarding disturbance rejection and set-point tracking, considering variables such as response time, robustness to uncertainty, and steady-state error. The study shows that a complex PI-PD controller has better accuracy, faster response time, and better noise rejection. Still, implementation is challenging due to increased complexity and processing requirements. In contrast, a standard PI-PD controller is a known solution but may have problems with accuracy and robustness. Fractional-order controllers based on fractional computations have the potential to improve control accuracy and robustness of non-linear and time-varying systems. Experimental insights and real-world case studies are used to highlight the strengths and weaknesses of each controller. The findings provide valuable insights into the strengths and weaknesses of complex-order and fractional-order controllers and help to select the appropriate controller for specific process plant requirements. Future perspectives on controller design and performance optimization are detailed, identifying the potential benefits of using complex and fractional-order controllers in process plants.

Design and Control Performance Optimization of Variable Structure Hydrostatic Drive Systems for Wheel Loaders

The traditional loader drive system is based on the hydraulic torque converter as the key component, and its gear is shifted through the coordination of the clutch and gearbox, which greatly increases power loss and operator fatigue. To address the above problem, a variable-structure hydrostatic drive system is proposed. This system adopts a closed-loop design and adjusts the displacement of the pump and dual motors to follow the throttle opening of the vehicle, achieving automatic gear shifting and smooth speed regulation. It can also automatically change the system structure according to the vehicle’s speed, meeting the vehicle’s demand for rapid switching output of high torque and high speed. At the same time, in the displacement matching control process, an adaptive sliding mode control scheme based on radial basis function neural network compensation is proposed. This scheme designs an adaptive hyperparameter update strategy according to the characteristics of the system to effectively compensate for changes in uncertain factors. Experimental results show that, compared to traditional drive systems, this system has the characteristics of simple operation, smooth speed regulation, and high fuel economy.

CBF‐guided iterative learning control performance optimization for switched systems

AbstractThis article studies the control barrier function (CBF) guided tracking performance optimization for switched systems under iterative learning control (ILC). CBF is an effective method for handling control system state safety. If the safe set is designed as expected performance, and the unsafe set is not expected, then this may be a good choice for optimizing control system performance. We propose a tracking performance optimization method based on CBF to speed up the convergence of the tracking error. A safe set is used to describe the expected tracking performance, by utilizing CBF, safety is further transformed into a set of inequality conditions that constrain ILC controller gains, which ensures that the tracking error is always within the safe set. In addition, an event‐triggering mechanism (ETM) is considered to reduce unnecessary data transmission, and sufficient conditions for exponential stability of the tracking error of switched systems and ILC controllers design are obtained. Finally, a simulation example is given to verify the feasibility of the proposed method.

Effectiveness of portable air cleaners in mitigating respiratory virus transmission risk

Portable air cleaners (PACs) have shown promising potential in reducing the risk of SARS-CoV-2 infection by effectively removing pollutant particles and optimizing airflow patterns. This study focused on a simulated scenario where an infected source and a susceptible person engage in conversation within a naturally ventilated room. By combining the Eulerian fluid method with the Lagrangian particle tracking model, a comprehensive insight into indoor airflow patterns and the dispersion of virus-laden droplets was gained. As deposited droplets may be resuspended or in contact thereby increasing the potential risk of infection, the deposition of droplets of different sizes in different susceptible areas was also specifically analyzed. The impacts of three variables, namely the configuration of the PAC’s opening, air flow rate, and positioning, on the transmission of virus-laden droplets were investigated. The results highlighted the significant role of PAC utilization in effectively capturing droplets emitted by the infected source and reducing virus concentration in the vicinity of the susceptible person, thereby mitigating the risk of transmission. Notably, the design and orientation of the suction opening emerged as crucial factors. Among the various cases studied, the optimal control and prevention performance against the virus was achieved with a virus concentration reduction rate of 97.4% when the PAC had an opening configuration with a larger single-sided suction opening facing the infected source, an airflow rate of 200 m3 h−1, and was positioned at the center of the tabletop between the infected source and the susceptible person. This research underscored the importance of employing PACs with appropriate settings to enhance indoor air quality and minimize the potential for SARS-CoV-2 transmission in similar scenarios.

Fuzzy Super-Twisting Sliding Mode Controller for Switched Reluctance Wind Power Generator in Low-Voltage DC Microgrid Applications

There is limited research focused on achieving optimal torque control performance of Switched Reluctance Generators (SRGs). The majority of existing studies tend to favor voltage or power control strategies. However, a significant drawback of SRGs is their susceptibility to high torque ripple. In power generation systems, torque ripple implicates fluctuations in the generated power of the generator. Moreover, high torque ripple can lead to mechanical vibrations and noise in the powertrain, impacting the overall system performance. In this paper, a Torque Sharing Function (TSF) with Indirect Instantaneous Torque Control (IITC) for SRG applied to Wind Energy Conversion Systems (WECS) is proposed to minimize torque ripple. The proposed method adjusts the shared reference torque function between the phases based on instantaneous torque, rather than the existing TSF methods formulated with a mathematical expression. Additionally, this paper introduces an innovative speed control scheme for SRG drive using a Fuzzy Super-Twisting Sliding Mode Command (FSTSMC) method. Notably robust against parameter uncertainties and payload disturbances, the proposed scheme ensures finite-time convergence even in the presence of external disturbances, while effectively reducing chattering. To assess the effectiveness of the proposed methods, comprehensive comparisons are made with traditional control techniques, including Proportional–Integral (PI), Integral Sliding Mode Control (ISMC), and Super-Twisting Sliding Mode Control (STSMC). The simulation results, obtained using MATLAB®/SIMULINK® under various speeds and mechanical torque conditions, demonstrate the superior performance and robustness of the proposed approaches. This study presents a thorough experimental analysis of a 250 W four-phase 8/6 SRG. The generator was connected to a DC resistive load, and the analysis focuses on assessing its performance and operational characteristics across different rotational speeds. The primary objective is to validate and confirm the efficacy of the SRG under varying conditions.

A novel design strategy of track‐based asymmetric nonlinear energy sink for seismic response mitigation of structures

AbstractMass dampers, whether linear or nonlinear, rely on resonance with the structure to achieve optimal control performance. However, linear mass dampers require frequency tuning, while nonlinear mass dampers are sensitive to input energy levels, rendering both inadequate for control problems that involve frequency and energy uncertainties. To address this issue, the track asymmetric nonlinear energy sink (TANES) has been developed as a robust mass damper against frequency and energy changes. In this study, a novel design strategy consisting of physical and parameter design was proposed for TANESs. First, TANESs with inverted tracks as the auxiliary mass were proposed, resulting in a significant reduction in the dimensions and weight of the device. The new physical design was then realized, and the mathematical descriptions of the TANES system were validated through experiments on a two‐story frame structure. Subsequently, a parameter design method was proposed to match the frequency variation pattern of the TANES with the given structural and load conditions. Following the design procedure, three TANESs with markedly different track shapes were obtained and compared with existing mass dampers under different types of excitations. Results showed that the frequency responses of the TANES systems subjected to harmonic ground excitations shared great similarity with those of a well‐tuned TMD system at small‐to‐moderate energy levels. As the energy level increased, nonlinear responses such as strongly modulated responses were observed in the TANES systems with decrease structural frequencies, which significantly reduced the resonant peak in the frequency responses. The frequency responses of the TANES systems were consistent with the design objective and could be used to quantitatively predict the robustness performance of the devices under impulsive and seismic excitations. With the reduced dimensions and weight resulting from the inverted‐track configuration and the robustness performance in line with common structural and load conditions, the proposed design strategy of TANESs demonstrated great potential in the seismic response mitigation of engineering structures.

Comparative Study of Advanced Modulation and Control Schemes for Dual Three-Phase PMSM Drives With Low Switching Frequencies

In this paper, multiple advanced low-switching-frequency modulation and control schemes are investigated for dual three-phase permanent-magnet synchronous motor (PMSM) drives. The modulation schemes under investigation include multisampling space vector modulation (MS-SVM), selective harmonic elimination pulse width modulation (SHEPWM) and synchronous optimal pulse width modulation (SOPWM) while the control schemes include complex vector control, model predictive control (MPC) and flux trajectory control-based model predictive pulse pattern control (MP <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> C). The difference between three-phase and dual three-phase PMSM drives at low switching frequencies is analyzed. Moreover, an optimal pulse width modulation (PWM)-based MP <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> C scheme is proposed for the dual three-phase PMSM, where the optimal PWM modulation and MP <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> C control are combined such that the optimal steady-state and dynamic control performance is achieved among the modulation and control schemes investigated. Experimental results are given to compare and validate the proposed control scheme.

Fuzzy System to Control a Liquid Mixing Tank

This paper aims to develop a fuzzy system to control the level and temperature of a fluid in a mixing tank by dynamically adjusting the flow rates of cold and hot liquids. The system involves a temperature and level loop, with the control variables being the rates of cold water and hot water flow, while also considering the outlet flow from the reservoir as a disruptive factor. The main goal of this control strategy is to maintain the liquid level and temperature within specified reference values. The system is a multivariable process that uses a fuzzy control system to develop a technique that, based on the state variables, can adapt the controller to achieve optimal control performance. In the development of this application, some linear controllers were tested, each implemented around specific regions within the phase plane. The designed controllers are then employed in conjunction with the fuzzy system to evaluate the system’s state variables. Therefore, an interpolation of these linear controllers is conducted, allowing for their application in a nonlinear system, with the aim of enhancing control performance near the boundaries between regions. The results show that the fuzzy approach is promising and provides convergence to the reference values of the liquid level and temperature in the mixing tank.