Control Performance Research Articles

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.

Position control of a digital electrohydraulic system with limited sensory data using double deep q-network controller

In this study, we propose a reinforcement learning controller for position control of a digital electrohydraulic system with independent metering valve structure. Our study addresses the inherent challenges of traditional control methods, such as the need for continuous pressure measurement and complex control structures. As a solution, a double deep q-network (DDQN) controller which relies solely on position feedback from D-EHS is suggested. A simulation environment was developed to train the DDQN controller, wherein the artificial neural network structure, training hyperparameters, and reward function were optimized through preliminary studies. The DDQN controller was trained using a sinusoidal position reference signal of a single frequency and evaluated experimentally for its position control performance. Tracking performance was assessed against various position reference signals, including ramp and sinusoidal signal with different frequencies. Robustness against external disturbances was also tested by increasing the nominal supply pressure by 20% and 40%. The results demonstrate that the DDQN controller successfully controls the digital electrohydraulic system and is achieving good tracking performance for the training position reference signal. Furthermore, the controller exhibits satisfactory tracking performance for non-training position reference signals, highlighting its ability to learn the digital electrohydraulic system dynamics with limited training data. Even under varying supply pressure conditions, the DDQN controller maintains an acceptable level of control performance. Our findings emphasize the generalizability and reliability of the DDQN reinforcement learning controller, making it a suitable candidate for application in digital electrohydraulic systems and contributing to the advancement of control theory research.

Optimizing Active Disturbance Rejection Control for a Stubble Breaking and Obstacle Avoiding Control System

In order to improve the obstacle avoidance control performance and anti-interference ability of a stubble breaking device of a no-tillage planter, a back-propagation neural network (BPNN)-optimized fuzzy active disturbance rejection control (ADRC) controller was designed to optimize the control performance of a servo motor. Firstly, a negative feedback mathematical model was established for the obstacle avoidance control system. Then, the nonlinear state error feedback (NLSEF) parameters in the fuzzy ADRC were intelligently optimized by the BPNN algorithm. In this way, a fuzzy ADRC controller based on BPNN optimization was formed to optimize the control process of a servo motor. Matlab/Simulink (R2022b) was used to complete the simulation model design and parameter adjustment. Consequently, the response time was 0.089 s using the BPNN fuzzy ADRC controller, which was shorter than the 0.303 s of the ADRC controller and the 0.100 s of the fuzzy ADRC controller. The overshoot was 0.1% using a BPNN fuzzy ADRC controller, which was less than the 2% of the ADRC controller and the 1% of the fuzzy ADRC controller. After noise signal interference was introduced into the control system, the regression steady state time of the BPNN fuzzy ADRC controller was 0.22 s, which was shorter than the 0.56 s of the ADRC controller and the 0.45 s of the fuzzy ADRC controller. A hardware-in-the-loop simulation experimental platform of the obstacle avoidance control system was constructed. The experiment results show that the servo motor control system has a fast dynamic response, small steady-state error and strong anti-interference ability for obstacle avoidance at the target height. Then, the control system error was within the allowable range. The servo motor control effect of the BPNN fuzzy ADRC was better than the ADRC and fuzzy ADRC. This optimized servo motor control method can provide a reference for improving the obstacle avoidance control effect problem of no-tillage seeders in stubble breaking operations on rocky desertification areas.

Machine Learning-Based Control of Autonomous Vehicles for Solar Panel Cleaning Systems in Agricultural Solar Farms

This paper presents a machine learning (ML)-based approach for the intelligent control of Autonomous Vehicles (AVs) utilized in solar panel cleaning systems, aiming to mitigate challenges arising from uncertainties, disturbances, and dynamic environments. Solar panels, predominantly situated in dedicated lands for solar energy production (e.g., agricultural solar farms), are susceptible to dust and debris accumulation, leading to diminished energy absorption. Instead of labor-intensive manual cleaning, robotic cleaners offer a viable solution. AVs equipped to transport and precisely position these cleaning robots are indispensable for the efficient navigation among solar panel arrays. However, environmental obstacles (e.g., rough terrain), variations in solar panel installation (e.g., height disparities, different angles), and uncertainties (e.g., AV and environmental modeling) may degrade the performance of traditional controllers. In this study, a biologically inspired method based on Brain Emotional Learning (BEL) is developed to tackle the aforementioned challenges. The developed controller is implemented numerically using MATLAB-SIMULINK. The paper concludes with a comparative analysis of the AVs’ performance using both PID and developed controllers across various scenarios, highlighting the efficacy and advantages of the intelligent control approach for AVs deployed in solar panel cleaning systems within agricultural solar farms. Simulation results demonstrate the superior performance of the ML-based controller, showcasing significant improvements over the PID controller.

Positive temperature coefficient material based on silicone rubber/ paraffin/ graphite/ carbon nanotubes for wearable thermal management devices

PTC materials provide substantial advantages such as adaptive temperature control performance and excellent safety. However, their use in wearable devices has been limited due to the essential requirements for flexibility and stability. This study prepared a new type of flexible PTC material, with a PTC intensity of 6.3 and a resistivity of only 400 Ω·cm in the low-temperature region. The flexibility of the material is evidenced by its 2 mm bending diameter and its ability to maintain structural integrity even after 100 bends. Furthermore, experimental evidence proves that PTC material has excellent thermal stability and can withstand extreme temperatures ranging from −20 °C to 120 °C. In the thermal control experiment, the PTC material maintains a temperature of approximately 25 °C to accommodate the requirements of the human body in a cold environment. When compared to the conventional PI heating sheet, the newly prepared PTC material exhibits advantages such as rapid response, adaptive temperature control performance, and safety. Consequently, it presents a broad application prospect in the field of wearable heating devices.

Joint capacity configuration and demand response optimization of integrated energy system considering economic and dynamic control performance

This paper proposes a joint optimization framework for the demand response-capacity configuration design of the integrated energy system (IES), in which both the economic cost and closed-loop dynamic regulation performance under source-load fluctuations are considered. The joint optimization consists of two layers. The upper layer optimizes the time of use electricity price to adjust the demand response of electric load; while the lower layer optimizes the capacity configuration, scheduling instructions and incentive-based thermal load demand response under typical scenarios. To fairly evaluate the impact of equipment capacity and demand response on the dynamic control performance of the IES, multi-parameter programming-based predictive control approach is applied to develop an offline design and scheduling-perceptive control system. A closed-loop dynamic scheduler is then developed based on the control system, which can optimize the operating instruction of each equipment considering their dynamic operating practice. Simulation studies on a typical off-grid combined heat and power IES show that the proposed approach can effectively match the demand response to the capacity configuration of the IES, helping the system achieve a dynamically flexible and economic energy supply.

Sliding mode control of phase-type T-S fuzzy stochastic semi-Markov jump systems

In this paper, the sliding mode control (SMC) problem of phase-type semi-Markov jump systems (S-MJSs) is studied using Takagi-Sugeno (T-S) fuzzy technology. A novel integral-type sliding surface with artificial delay has been constructed and applied for the first time to phase type S-MJSs, reducing system conservatism and enhancing control performance. Firstly, this paper presents sufficient criteria for the robust random stability of these systems by utilizing model transformation methods, supplementary variable techniques, and modal-dependent Lyapunov-Krasovskii function methods. Secondly, the sliding mode controller is designed using matrix technology. Finally, the effectiveness of this method is demonstrated through a simulation example involving a single-link robotic arm, and the comparison results show the merits to the existing achievements.

Magnetic smart polymer gel with directional plugging for conformance control in oil reservoirs

Polymer gel technology has been proven to be one of the most effective methods for conformance control and enhanced oil recovery. However, to effectively gel highly permeable layers, precise placement of the gelant within the formation is required. Herein, to make the most use of polymer gel in conformance control, a novel magnetic smart polymer gel consisting of polyacrylamide and Fe3O4@polyethylenimine, is designed and prepared as a conformance fluid to achieve directional plugging in target reservoirs. To deeper understanding of the properties of Fe3O4@polyethylenimine and smart gel, as well as how they are formed and function, the microstructure of these materials was characterized through various techniques, including analysis of their crystal and chemical structures, micromorphology, and cross-linking mechanism, et al. The rheological tests suggest that the system has good viscosity and elasticity, and the gel strength can reach 105,463 mPa⋅s after aging 30 days at 120 °C. Furthermore, the controlled manipulation indicated that smart gel can move quickly and repeatedly to a designated location under a magnetic field. Additionally, the influence of the magnetic generator on the intelligent plugging performance of the smart gel system is also explored. It has been found that magnetic plugging can extend the effective lifespan of smart gel. Finally, the conformance control performance of the smart gel system is revealed by the parallel cores flow experiment. This work presents a novel smart gel system designed for directional plugging, to enhance conformance control in high-temperature oil reservoirs.

Neural network-based prescribed performance tracking control for a class of nonlinear systems with mismatched disturbances

This paper addresses the prescribed performance tracking control problem for a class of nonlinear systems subject to mismatched uncertainties. A novel disturbance observer-based control (DOBC) scheme is proposed through combining radial basis function neural network (RBFNN) and prescribed performance function (PPF). In contrast to traditional DOBC, the proposed approach employs RBFNN technology to approximate unknown nonlinear functions in the system, instead of treating them as part of lumped disturbances. A novel disturbance observer is developed to estimate disturbances characterised by a nonlinear exogenous system. To enhance control performance, a PPF that characterises both transient and steady-state behaviour is used for the transformation of tracking errors. It can be proved that all the states of the closed-loop system can be guaranteed to be uniformly ultimately bounded (UUB), and that the tracking error evolves within the prespecified boundaries. Theoretical results are validated and supported by two simulation examples.

Investigation of rank order centroid method for optimal generation control.

Multi-criteria decision-making (MCDM) presents a significant challenge in decision-making processes, aiming to ascertain optimal choice by considering multiple criteria. This paper proposes rank order centroid (ROC) method, MCDM technique, to determine weights for sub-objective functions, specifically, addressing issue of automatic generation control (AGC) within two area interconnected power system (TAIPS). The sub-objective functions include integral time absolute errors (ITAE) for frequency deviations and control errors in both areas, along with ITAE of fluctuation in tie-line power. These are integrated into an overall objective function, with ROC method systematically assigning weights to each sub-objective. Subsequently, a PID controller is designed based on this objective function. To further optimize objective function, Jaya optimization algorithm (JOA) is implemented, alongside other optimization algorithms such as teacher-learner based optimization algorithm (TLBOA), Luus-Jaakola algorithm (LJA), Nelder-Mead simplex algorithm (NMSA), elephant herding optimization algorithm (EHOA), and differential evolution algorithm (DEA). Six distinct case analyses are conducted to evaluate controller's performance under various load conditions, plotting data to illustrate responses to frequency and tie-line exchange fluctuations. Additionally, statistical analysis is performed to provide further insights into efficacy of JOA-based PID controller. Furthermore, to prove the efficacy of JOA-based proposed controller through non-parametric test, Friedman rank test is utilized.

A compound twisted and coiled actuators with payload-insensitive untwisting characteristics

Compound Twisted and Coiled Actuators (CTCAs) are promising candidates for artificial muscles due to their outstanding competences in producing large output forces and long strokes. However, as their thermal actuation performance depends on not only the driving temperature, but also the spandex deformation caused by the payload, it is rather difficult to obtain accurate actuation models for the conventional CTCAs and thus seriously restricts their control performance and application development. To tackle such difficulties, a CTCA with a novel strategic fabrication method is proposed in this work. Motivated by the kinetostatic performance of a stiffening spring with a high preload, the fiber draw ratio of the CTCA is significantly increased through pre-stretching the fiber during the fabrication process, which makes the thermal actuation performance of the CTCA insensitive to the external payload. The effectiveness of the proposed payload-insensitive characteristics of the CTCA is validated through experimental results, which shows that the CTCA has almost the same untwisting strain–temperature curves under different payloads. Consequently, a simplified yet accurate actuation model is obtained such that the displacement of the CTCA is mainly determined by the driving temperature within a meaningful range of payloads. Experiments are conducted to evaluate the accuracy of the actuation model and the model error is less than 7.6%. Such a simplified actuation model is implemented for the pose control of a 2-DOF CTCA-driven continuum robot joint module, in which the average steady-state error of the bending angle is less than 1.0∘.

Control strategies and performance analysis of doubly approximation and large-scale system control using hybrid model reduction approach

Control strategies and performance analysis of doubly approximation and large-scale system control using hybrid model reduction approach

Development of a fed-batch-scale anaerobic co-digestion control system based on multivariable output error state space and model predictive control

This paper describes a feeding control system for fed-batch-scale anaerobic co-digestion. The proposed system is based on the multivariable output error state space and uses model predictive control to regulate the biogas flow output of anaerobic co-digestion. This control system combines predictive modeling, receding-horizon optimization, and state feedback. An accurate linear model of anaerobic co-digestion is obtained through the multivariable output error state space method using experimental data, and the accuracy of the model is evaluated using a goodness-of-fit index and the root mean square error. After augmenting the model, a predictive control system is established. The system states are observed through a Kalman filter, and the constrained receding-horizon optimization problem is solved using quadratic programming. Finally, the stability of the control system is demonstrated from both open- and closed-loop perspectives. The control system is then constructed and used in actual experiments, with the target flow output set at 100 NmL/h. The final flow output indicates that the control system achieves good control performance. This research enables the biogas output of anaerobic co-digestion to be controlled from a linear perspective through a concise control algorithm with strong practical application prospects.

Nonlinear Attitude and Altitude Trajectory Tracking Control of a Bicopter UAV in Geometric Framework

In this paper, we deal with a Bicopter drone that has two thrusters and two tilting servos. Both the position and attitude dynamics of Bicopter are globally expressed on the Special Euclidean group SE3. A simple control allocation method is proposed to map between the control wrench and actuator inputs for the Bicopter. A geometric nonlinear attitude and altitude tracking controller is developed for the Bicopter and the asymptotic stability analysis is performed using the Lyapunov method for the closed-loop nonlinear system. The performance of the proposed altitude and attitude stabilization controller is validated through experimental hardware developed in-house. The attitude controller performance is validated through simulations and shown to be comparable against an linear matrix inequality-based control law.

Economic Performance of Model Predictive Control at Back-off Operating Point

In this paper, we address the economic performance of Model Predictive Control (MPC) while operating at a backed-off operating point. Operating the plant at a constrained optimal point will often cause constraint violations due to uncertainties such as disturbances and measurement errors, etc. To ensure dynamic feasibility, the concept of economic back-off is used. In this work, we select the set point as the economic back-off point such that the dynamic operating region should have the least variability in the active constrained variables while ensuring the feasibility of other variables. In other words, the dynamic operating region is oriented by the proper design of a controller such that variability in active constrained variables is as low as possible. This controller design can be transformed into equivalent objective function weights of the MPC controller. In this study, we demonstrate that the determined back-off point is optimal for both linear controller and MPC controller when there are no unconstrained degrees of freedom. For the case with unconstrained degrees of freedom, the back-off point determined using the presented approach is optimal only for a linear controller but suboptimal for an MPC controller. Demonstrative case studies are presented to illustrate the economic performance of the MPC controller at the determined economic back-off point.

Dynamic protocol-based sliding mode control for nonlinear semi-Markovian jump systems with deterministic switching

This study investigates the sliding mode control for semi-Markov jump systems with dynamic self-triggered protocol. Utilizing an average dwell time strategy, the variation of semi-Markov chain is regulated by a deterministic switching signal. To improve transmission efficiency and achieve better control performance, a dynamic self-triggered protocol is developed, which predicts the next triggered time based on the current sampled data, obviating the need for dedicated hardware to continuously monitor system states and eliminating the Zeno phenomenon. A sliding mode controller related to the self-triggered protocol is proposed, which ensures the finite-time reachability. Based on the proposed self-triggered protocol and Lyapunov theory, a sufficient criterion is established to guarantee mean-square exponential stability under partial unknown transition rates. Finally, the effectiveness of the proposed method is verified through simulation examples.

An Adaptive Control Scheme Based on Non-Interference Nonlinearity Approximation for a Class of Nonlinear Cascaded Systems and Its Application to Flexible Joint Manipulators.

Control design for the nonlinear cascaded system is challenging due to its complicated system dynamics and system uncertainty, both of which can be considered some kind of system nonlinearity. In this paper, we propose a novel nonlinearity approximation scheme with a simplified structure, where the system nonlinearity is approximated by a steady component and an alternating component using only local tracking errors. The nonlinearity of each subsystem is estimated independently. On this basis, a model-free adaptive control for a class of nonlinear cascaded systems is proposed. A squared-error correction procedure is introduced to regulate the weight coefficients of the approximation components, which makes the whole adaptive system stable even with the unmodeled uncertainties. The effectiveness of the proposed controller is validated on a flexible joint system through numerical simulations and experiments. Simulation and experimental results show that the proposed controller can achieve better control performance than the radial basis function network control. Due to its simplicity and robustness, this method is suitable for engineering applications.

Sliding mode active disturbance rejection control for manipulator considering actuator saturation

<p style="text-align: justify;">Considering the issue of low control accuracy in joint trajectory tracking control for manipulator systems with actuator saturation due to external disturbances, modelling inaccuracies, and joint friction, a sliding mode active disturbance rejection control approach was proposed. An improved extended state observer is employed to observe and estimate the lumped disturbances affecting the system, providing feedback compensation. A variable gain reaching law is devised, coupled with a fast non-singular terminal sliding mode to design the system control law, which mitigates chattering inherent and ensuring precision control. Additionally, a novel output error compensation-based anti-windup scheme is introduced to compensate for the detrimental impacts caused by actuator saturation. Simulation results collectively demonstrate that the proposed tracking control algorithm exhibits better control performance and robustness against disturbances.</p>

Enhanced patient-based real-time quality control using the graph-based anomaly detection.

Patient-based real-time quality control (PBRTQC) is an alternative tool for laboratories that has gained increasing attention. Despite the progress made by using various algorithms, the problems of data volume imbalance between in-control and out-of-control results, as well as the issue of variation remain challenges. We propose a novel integrated framework using anomaly detection and graph neural network, combining clinical variables and statistical algorithms, to improve the error detection performance of patient-based quality control. The testing results of three representative analytes (sodium, potassium, and calcium) and eight independent variables of patients (test date, time, gender, age, department, patient type, and reference interval limits) were collected. Graph-based anomaly detection network was modeled and used to generate control limits. Proportional and random errors were simulated for performance evaluation. Five mainstream PBRTQC statistical algorithms were chosen for comparison. The framework of a patient-based graph anomaly detection network for real-time quality control (PGADQC) was established and proven feasible for error detection. Compared with classic PBRTQC, the PGADQC showed a more balanced performance for both positive and negative biases. For different analytes, the average number of patient samples until error detection (ANPed) of PGADQC decreased variably, and reductions could reach up to approximately 95 % at a small bias of 0.02 taking calcium as an example. The PGADQC is an effective framework for patient-based quality control, integrating statistical and artificial intelligence algorithms. It improves error detection in a data-driven fashion and provides a new approach for PBRTQC from the data science perspective.

A design an optimized fuzzy adaptive proportional-integral-derivative controller for anti-lock braking systems

This paper introduces a new control theory for optimizing anti-lock braking systems (ABS) in automotive applications. Anti-lock systems play a critical role in ensuring vehicle safety during braking by preventing wheel lock-up. However, conventional ABS control algorithms often struggle to adapt to changing road conditions and vehicle dynamics. The error tuning mechanism was not perfect because the occurrence of error is unique based on each application. In response to this challenge, we propose a new approach that combines fuzzy logic and adaptive proportional-integral-derivative (PID) control. The controller more robust to noise and uncertainties in the system. The present research work has concentrated on designing a novel Wavelet Fuzzy adaptive-hybrid Lion-strawberry Proportional-Integral-Derivative (WFA-HLSPID) for the ABS to maximize the control performance while it applied in the vehicle. Simulation results and comparative analysis demonstrate the superior performance of the proposed control theory when compared to traditional ABS systems. The optimized fuzzy adaptive PID control not only enhances vehicle stability during braking but also improves stopping distance. In all cases, the stopping distance, slip rate, and friction have been validated using MATLAB/Simulink.