Control Performance Index Research Articles

Flexible heat and power load control of subcritical heating units based on energy demand-supply balance

To fulfill the requirement for enhancing the flexibility of heating units within the framework of renewable energy grid integration. In this study, a proposed optimal control strategy for heating units borrows the heat extraction output to perform rapid load changes; the boiler heat supply within the unit is clearly differentiated from the turbine energy demand, and formulate corresponding control signals. The coordinated control of the boiler and turbine based on the energy demand-supply balance of the unit as secondary control for the final load accuracy, ensuring its safe and stable operation. To further enhance the control performance, an improved sparrow optimization algorithm with butterfly search is designed to adjust the controller parameters. Finally, field tests on a 300MW subcritical heating unit reveal that the automatic generation control performance index is improved by 32.16% compared to the traditional coordinated control strategy, while taking less impact on the heat user. These results validate the exceptional performance and significant practical value of the optimized control strategy.

Research on parameter optimization method of thrust vector/aerodynamic rudder composite control law based on singular value method

Advanced aircraft with thrust vectoring/aerodynamic rudder layout have significant control coupling problems. The traditional single-loop control parameter design method cannot take into account the performance of multiple control loops in this scenario. Therefore, a control law parameter optimization method based on the singular value of the hysteresis matrix is proposed. First, the control parameter optimization interval is determined using the time domain control performance index. Then, the singular value method is used to measure the stability margin of the multiple-input multiple-output (MIMO) system on this basis, and the corresponding optimal objective function is established to optimize the controller parameters. The feasibility of this method is verified by numerical simulation. The results show that the designed control parameter optimization algorithm has better time domain control performance and larger system stability margin than the traditional single-loop control parameter design method.

Research on the Energy Management Strategy of a Hybrid Tractor OS-ECVT Based on a Dynamic Programming Algorithm

The multi-degree-of-freedom characteristics of the planetary gear electronic continuously variable transmission (ECVT) configuration in series-parallel hybrid tractors impose more complex requirements for energy management strategies under variable load conditions. For a high-power hybrid tractor, this paper takes the hybrid tractor output-split (OS)-ECVT configuration as the research object and describes the principles of stepless transmission and power-splitting within the configuration. In order to improve the fuel economy of high-power hybrid tractors and the running status of power components, an energy management strategy focused on ploughing conditions based on the Bellman minimum dynamic programming (DP) algorithm is proposed in this paper. Second, equivalent fuel consumption is selected as the performance index for energy-saving control, and the solving principle of the energy management strategy based on the dynamic programming algorithm is established to facilitate the resolution process of the energy management strategy. Finally, the energy-saving control simulation is completed under ploughing conditions. The results show that compared with the energy management strategy based on the optimal operating line (OOL), the energy management strategy based on DP fully utilizes the benefits of low-cost electric energy and enables the hybrid power system to have a wider range of stepless transmission performance. In addition, the hybrid power system has the advantages of enhanced decoupling of speed and torque, higher efficiency, and more economical secondary energy conversion. As a result, the whole machine has enhanced power-split performance, greatly improving the running conditions of the power components. The equivalent fuel consumption values of the energy management strategies based on DP and OOL are about 3.1238 L and 4.2713 L, respectively. The equivalent fuel consumption based on DP is reduced by about 26.87%, which effectively improves the fuel efficiency of hybrid tractors.

DBFact applied to minimum variance performance assessment for nonminimum phase multivariate systems from closed‐loop data

AbstractThis paper introduces an approach for determining a minimum variance control (MVC) benchmark for nonminimum phase (NMP) multi‐input multi‐output (MIMO) systems using closed‐loop operational data. The MVC benchmark is derived from the MVC law of DBFact factorization introduced by Lima, Trierweiler, and Farenzena. Unlike other factorization methods, DBFact offers advantages such as non‐iterative computation and ensuring internal stability of the MVC law. This approach considers the inherent directionality of NMP MIMO systems, enhancing the reliability of the control performance index. However, the original method relies on prior knowledge of the process model. To overcome this limitation, this paper proposes a method for calculating the MVC benchmark when prior knowledge is absent. It introduces a MIMO system identification strategy employing minimally invasive signal tests. The methodology is evaluated across various control conditions using a quadruple‐tank plant with additional time delays. The study emphasizes the importance of directionality in assessing MIMO system performance, particularly in evaluating individual loop performances. Results demonstrate the identification procedure's effectiveness in accurately calculating the proposed MVC benchmark, even with a mere 1% increase in output variance considered.

Intelligent switching gain based sliding mode control for optimization of power consumption in cooperative manipulators

AbstractCooperative manipulators, vital for intricate tasks, are gaining widespread attention across industries. Recognizing their impact on power consumption, costs, and task outcomes, this paper emphasizes the critical study of control methods, actuator power consumption, and manipulator accuracy. Addressing these challenges, we propose a neural network‐sliding mode controller (NN‐SMC) to optimize actuator power consumption and minimize errors in cooperative manipulators. Operating in trajectory and point‐to‐point modes, the NN‐SMC dynamically generates real‐time switching mode controller (SMC) gains (L and K) for precise control. Stability is ensured by maintaining gains within permissible ranges. In point‐to‐point mode, the NN orchestrates an optimal path generation, along with tailored gains. To evaluate performance accurately, a novel control performance index is introduced. Experimental results on 3‐DOF cooperative manipulators demonstrate a remarkable 28% increase in the control performance index for the trajectory mode and a substantial reduction in computational complexity for both modes. This work not only addresses inherent challenges in cooperative manipulators but also signifies a methodological advancement through the integration of neural network‐based control, promising enhanced efficiency and stability.

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.

Data-driven output consensus for a class of discrete-time multiagent systems by reinforcement learning techniques

The primary objective of this research paper is to examine the challenges associated with optimization and tracking control in discrete-time multiagent systems. This study begins by defining the optimal tracking control issue within the framework of a leader–follower multiagent system. A novel approach based on policy iteration is introduced for calculating the control law and performance index. The paper also includes an analysis of the convergence properties of this technique, ensuring its effectiveness in reaching optimal solutions. To facilitate this, an actor–critic neural network framework is utilized to approximate the control law and iterative performance index function. The proposed approach enables the real-time implementation of the policy iteration method, eliminating the requirement for prior knowledge of the system’s dynamics. The paper concludes with a series of simulation experiments that demonstrate the practicality and efficiency of the developed optimal tracking control strategies.

AOPC-based control for efficient uncertainty mitigation in UASB wastewater treatment with multiple manipulated variables and distributed biomass integration

This study proposes an adaptive optimal predictive control (AOPC) for the upflow anaerobic sludge blanket (UASB) reactor with recirculation, employing a PDE-ODE system. Effectively addressing complexity and uncertainties associated with Danckwerts-boundary conditions and biomass distribution, an analytical model predictive control scheme incorporates adaptive set points and compensators. By dynamically adjusting multiple manipulated inputs, including the feed flow rate and recirculation-to-feed ratio, the controller achieves precise effluent concentration control. The robustness of the control system is investigated through fluctuations in inlet concentrations (15–30 %) and variations in bacterial growth rates (10–30 %). The control performance index, ISE, indicates that the AOPC-based control system outperforms the PI controller by 28–150 times for inlet concentration variations and 3–84 times for growth rate changes. With a settling time of 1–3 days, the proposed system excels over the conventional controller, which consistently struggles to maintain the target, emphasizing its inherent robustness.

Direct synthesis approach to design PDμ${\text{PD}}^\mu$ based smith predictor for integrating plants: Studied on a quadrotor UAV

AbstractThis article presents a simple yet novel method of designing a fractional‐order proportional derivative (namely, ) controller for all types of integrating plants. Considering the importance of the direct synthesis approach, the method obtains robust closed‐loop performance. The setpoint parameter is obtained using the multi‐optimization Pareto solution, considering the optimal values in control efforts and performance index together. The numerical investigations have shown improved servo and regulatory responses compared to recently published strategies with fractional orders. It is also known that classical PIDs cannot accurately follow a ramp setpoint. A real‐world situation also demands that the reference inputs become an acceleration ramp type. The proposed technique can handle rising ramp setpoints with plant uncertainty and measurement noise. Hardware verification is also performed on a quadrotor minidrone to check for real‐time issues.

Data-driven model reduction approach for active vibration control of cable-strut structures

The cable-strut structures have the features of low stiffness and weak damping, resulting in large vibration response under dynamic excitation. The vibration response of the cable-strut structures can be reduced by active control methods. However, many traditional active control methods need to build a state-space model (SSM) based on finite element model (FEM), and solving the active control force for the high-dimensional SSM is time-consuming. In this paper, the dynamic mode decomposition with control (DMDc) method is proposed to achieve a data-driven reduced-order SSM for active vibration control of cable-strut structures. This proposed method only uses the data of structural response and control force input to build reduced-order SSM and does not rely on the FEM. Linear quadratic regulator (LQR) design using the DMDc-based reduced-order SSM (DMDc-LQR) is more computationally efficient than that using the FEM-based SSM (FEM-LQR). The relationship between the design parameters of the DMDc-LQR controller and the FEM-LQR controller is established, which ensures that the control performance indexes of the DMDc-LQR controller and the FEM-LQR controller are equal. The effectiveness of the proposed DMDc-LQR method is validated using numerical Kiewitt cable dome and tensegrity grid under wind and seismic loads. The results show that the DMDc-LQR controller maintains similar control effect to that of the FEM-LQR controller and has advantage of high computing efficiency as well. In addition, the influence of critical parameters (i.e., data noise and model order) on vibration control effect is comprehensively explored. It is found that increasing the model order is effective to mitigate the noise influence on the DMDc-based reduced-order SSM and ensures the effectiveness of the DMDc-LQR controller.

Stability-Based Optimum Design of Tuned Mass Damper using Chaos Game Optimization in Pole Placement Scheme

The present work investigates the optimization of Tuned Mass Dampers (TMDs) for improving the seismic behavior of structures by considering system stability using a well-known robust Chaos Game Optimization (CGO) metaheuristic algorithm. In this regard, a new approach was introduced to locate the system’s poles as an objective function in the optimum design of TMDs. In this scheme, the mechanical parameters of TMD are designed to find the pole placement of the structural system with more stability. This research presents a dynamic analysis and comparative evaluation of various cost functions—including relative controlled drift response, the transfer function (TF) of acceleration response, and pole placement scheme—to determine the most effective design for TMD parameter optimization. Structural control performance indexes are used to compare the seismic behavior of each design. The results demonstrate the cost function based on the pole placement scheme’s capability to enhance TMD design, suggesting valuable theoretical and practical understandings for more resilient and efficient structural designs.

An interval type-3 fuzzy PID control system design and its application in solid oxide fuel cells power plant

Compared with type-2 fuzzy sets, the secondary membership degree of interval type-3 fuzzy sets is an interval rather than crisp value, which makes interval type-3 fuzzy sets can obtain more degree of freedoms. This article studies an interval type-3 fuzzy PID controller based on interval type-3 fuzz sets. The framework of interval type-3 fuzzy PID controller is identical with type-2 fuzzy PID controller, but it contains more adjustment controller parameters and its type reduction procedure is more complex. In this paper, type reduction of interval type-3 fuzzy sets is derived from general type-2 fuzzy sets represented by α-plane and a direct NT type reduction algorithm is applied. The control effects of interval type-3 fuzzy PID controller are firstly tested by 2 nonlinear plants, the simulation results show that interval type-3 fuzzy PID controller has better control performance indexes than PID controller, type-1 fuzzy PID controller, interval type-2 fuzzy PID controller and general type-2 fuzzy PID controller. Furthermore, the interval type-3 fuzzy PID controller will be applied in rated voltage control of solid oxide fuel cells (SOFC) power plant. The output voltage control of SOFC is quite challenging because of the strong nonlinearity, limited fuel flow, and rapid variation of the load disturbance. The simulation results demonstrate the advantages and robustness of proposed interval type-3 fuzzy PID controller.

Implementation of Control Standardization Based on HCM5000G DC Control and Protection Platform

This paper introduces the whole construction of the control standardization simulation system based on the HCM5000G DC control and protection platform, which can meet the requirements of engineering design and various control performance indexes by adopting the standardization design of the self-control and localization platform. In this paper, the construction principle of the standardized control system and the optimization strategy of the control structure are discussed in detail, and the engineering simulation model is realized by using the real-time digital simulation system with the layered and distributed standardized interface design, which provides an important reference for the future large-scale application of the control and protection of DC Engineering.

A chart to evaluate the effect of active control on a bridge

To reinforce and improve the function of a light bridge for disaster relief, a neutral equilibrium mechanism (NEM) was developed and applied for controlling the internal force and reducing the vertical deformation of a bridge to form as virtual piers. The control force of the NEM was generated by a proportional–integral–derivative controller with a combination of displacement GP, speed GD and adjusted GI gain coefficients to neutralise the vertical deformation of a bridge that had been created by a moving live load and the dead weight of the bridge. To effectively evaluate the control efficiency of bridge deformation under this mechanism, a multi-dimensional quality evaluation method is proposed to assess the control benefits of bridge deformation under different combinations of control coefficients. The test and analysis results in the control performance analysis chart show clearly that the control performance index MTIi is around 2–3, 3 and 5, respectively, for the combination of: GP = 0.5, GD = 0.0, GI = 0.02; GP = 1.0, GD = 0.0, GI = 0.02; and GP = 2.0, GD = 0.0, GI = 0.02. The analysis results present a clear demonstration of control performance, which can serve as a reference for future practical control safety.

Application of Machine Learning to Performance Assessment for a Class of PID-Based Control Systems

In this paper, a novel machine learning derived control performance assessment (CPA) classification system is proposed. It is dedicated for a wide class of PID-based control industrial loops with processes exhibiting dynamical properties close to second order plus delay time (SOPDT). The proposed concept is very general and easy to configure to distinguish between acceptable and poor closed loop performance. This approach allows for determining the best (but also robust and practically achievable) closed loop performance based on very popular and intuitive closed loop quality factors. Training set can be automatically derived off-line using a number of different, diverse control performance indices (CPIs) used as discriminative features of the assessed control system. The proposed extended set of CPIs is discussed with comprehensive performance assessment of different machine learning based classification methods and practical application of the suggested solution. As a result, a general-purpose CPA system is derived that can be immediately applied in practice without any preliminary or additional learning stage during normal closed loop operation. It is verified by practical application to assess the control system for a laboratory heat exchange and distribution setup.

Optimal security control for networked switched systems with a state‐prediction‐based anti‐DoS mechanism

AbstractThe openness of networks renders them vulnerable to various forms of attacks. When networked switched systems (NSS) suffer from denial of service (DoS) attacks and delays, the real‐time property of the dataset decreases, greatly affecting the control performance. To address this issue, this article proposes a switched adaptive dynamic programming (ADP) predictive control method. An event‐triggered mechanism is designed to reduce unnecessary waste of network transmission resources. A predictive mechanism is designed to accurately reconstruct the missing system state and switching signal under DoS network attacks. Then, the reconstructed data are applied to train the actor and critic neural networks, which are used to approximate the optimal control policy and performance index function (PIF) of the NSS, respectively. Furthermore, the iterative convergence of the switched ADP algorithm is proved. Finally, a numerical example is provided to verify the effectiveness of the proposed method.

A novel robust extended dissipativity state feedback control system design for interval type-2 fuzzy Takagi-Sugeno large-scale systems

Recently, systems have become large in their model and dynamic. To apply control algorithms, serious problems appear that need to be solved. Two significant problems are modelling the dynamics of the large-scale system and reducing effects of perturbations. In this paper, we use the advantage of large-scale systems modelling based on the type-2 fuzzy Takagi–Sugeno model to cover the uncertainties caused by large-scale systems modelling. The advantage of using membership function information is the reduction of conservatism resulting from stability analysis. Also, this paper uses the extended dissipativity robust control performance index to reduce the effect of external perturbations on the large-scale system, which is a generalization of , , passive and dissipativity performance indexes and control gains can be achieved through solving linear matrix inequalities (LMIs). Hence, the whole closed-loop system is asymptotically stable. Finally, the effectiveness of the proposed method is demonstrated by two practical examples.

Thermodynamic modeling and control of hybrid solar-fossil fuel power generation and storage system

Hybrid solar-fossil fuel power generation and storage (HSFF-PGS) is an innovative technology characterized by renewable and conventional hybrid energy utilization and thermochemical energy storage. The HSFF-PGS system has prominent advantages in improving the solar energy grade and storing thermal energy with a high energy density. However, investigations on its dynamic characteristics and real-time control are inadequate, which hinders the development of the system’s automation level to some degree. To deal with this issue, first, in this paper a dynamic model that includes the thermal inertia and reaction kinetics is constructed. Simulations under the nominal conditions show that the net solar utilization efficiency and energy savings ratio can reach 48.28% and 34.5%. Second, the design/off-design working condition properties are presented and the dynamic characteristics, such as nonlinearity and coupling, are explored to identify the control difficulties. The calculation results show that the relative gain array value ranges from 1 to 5 and the Vinnicombe gap is greater than 0.4 under most working conditions. Based on the dynamic analysis results, distributed active disturbance rejection control (ADRC) strategies for power generation and exhaust temperature regulation are developed. Then, the control performances of the proposed distributed ADRC and the conventional proportional integral derivative (PID) control are compared under nominal working conditions and typical-day scenarios. The control performance indexes are improved by 66.7% on average. Thus, the distributed ADRC is confirmed to be a better control method and has a good potential for dynamic operation of the HFSS-PGS system.

Enhancing Microcomputer Edge Computing for Autonomous IoT Motion Control

Devices microprocessors, microcontrollers, and Field Programmable Gate Arrays (FPGA) play the core rule at the IoT edge level and it should be right provisioned. For proper controller performance, control algorithms should be implemented near the actuator eliminating the delay effects. In the IoT domain, this means to implement the mentioned algorithm at the edge level and prior data transmitting. The efficient IoT-enabled motion control can be obtained by considering two main factors; the first factor is from the actuator design point of view and the second factor is from the controller performance point of view. Therefore, in this article, the two mentioned factors are treated concerning the microprocessor rule and importance as a core for proper prototype design and as the main platform to implement the control algorithms. A comparison of controller performance indices for both prototypes is done using previously distributed motion control schemes and newly developed schemes after tuning the respective schemes gains in an optimal manner. The scheme with better behavior of both prototypes are selected for the IoT integration process, this scheme ensures optimal edge computing for the distributed motion control, making the implementation of all control computation take place at the IoT-edge level. As a result, the dynamic pipeline stages (DPS) based prototype gives better controller performance indices for most strategies, less power consumption, and optimally utilized resources encouraging the use of the microprocessors with reconfigurable components at the IoT-edge level.

A Control and Simulation Method for Space-based Opto-mechanical Structures Using Uniform Scanning

To achieve high-precision imaging in the calibration area of a sub-satellite point, this paper proposes a method for assessing the scanning accuracy and how it affects the agility of the maneuvering process. In addition, simulations and experimental validation are also conducted using a position-velocity dual-loop control algorithm. The design is based on the control theory and the operating characteristics of the actual flexible scanning mechanism, the control law is designed using the control method based on the permanent magnet synchronous motor model, and the interference is simulated and analyzed. The results show that the design can combine the control performance indexes of practical applications and meet the requirements of in-orbit high-precision scanning, which can provide a reference for the subsequent development of space high-precision scanning control.