Control Law Research Articles

Predefined-Time Platoon Control of Unmanned Aerial Vehicle with Range-Limited Communication

In this paper, the predefined-time platoon control for multiple uncertain unmanned aerial vehicles (UAVs) under range-limited communication and external disturbance constraints is considered. A novel control scheme, which can guarantee communication connectivity, collision avoidance, and the predefined convergence time simultaneously, is proposed. To achieve disturbance robustness, an observer-based distributed control law is firstly proposed with a time-varying gain. Then, a radial basis function neural network (RBFNN) with an adaptive tuning law is applied to approximate uncertainties of the system. Under the time and error transformation techniques, uniformly ultimate boundedness (UUB) stability of the closed-loop system is guaranteed within predefined convergence time. Compared with the existing results, the proposed method allows the system to have UUB within any predefined time without depending on the initial conditions or system parameters. Finally, simulation results are presented to verify the derived theorem.

Adaptive synergetic control for electronic throttle valve system

AbstractThis study has developed adaptive synergetic control (ASC) algorithm to control the angular position of moving plate in the electronic throttle valve (ETV) system. This control approach is inspired by synergetic control theory. The adaptive controller has addressed the problem of variation in systems parameters. The control design includes two elements: the control law and adaptive law. The adaptive law is developed based on Lyupunov stability analysis of the controlled system, and it is responsible for estimating the potential uncertainties in the system. The effectiveness of the proposed adaptive synergetic control has been verified by numerical simulation using MATLAB/Simulink. The results showed that the ASC algorithm could give good tracking performance in the presence of uncertainty perturbations. In addition, a comparison study has been made to compare the tracking performance of ASC and that based on conventional synergetic control (CSC) for the ETV system. The simulated results showed that the performance of ASC outperforms that based on CSC. Moreover, the results showed that the estimation errors between the actual and estimated uncertainties are bounded and there is no drift in the developed adaptive law of ASC.

ÜÇ SӘRBӘSTLİK DӘRӘCӘLİ ROBOT MANİPULYATOR SİSTEMİ ÜÇÜN OPTİMAL PİD TӘNZİMLӘYİCİSİNİN MODELLӘŞDİRİLMӘSİ

. In this research work, the application and analysis of the optimization methodology based on the dynamic flying objects algorithm to design the robot manipulator based on the optimal PID controller is considered. Based on the dynamic analysis of robot manipulators, the interaction between the torques created by the actuators and the position and speed of the manipulator was investigated. The optimal PID control law obtained from the proposed algorithm is applied to the robot system. The proposed controller optimizes the trajectory of the robot's end-effector for input during variable time and hardens the robot against perturbing effects. To achieve a highly adaptive optimization process, the correct formulation of the utility function leads to optimal solutions. Three different objective functions were used in the process of optimization of control parameters in the robot system and their results were compared. In this work, a new debugging methodology for trajectory tracking in robotic manipulator systems is presented. Optimum benefit is obtained by using the proposed PID regulation law. The obtained results are satisfactory and competitive. It has been determined that the problems of the smooth control system in the places where the robot system is highly non-linear are regulated by classical methods.

FES Control of a Finger MP Joint with a Proxy-Based Super-Twisting Algorithm

To improve motion accuracy through functional electrical stimulation (FES) of forearm muscles, feedback control laws are applied to the index finger’s metacarpophalangeal (MP) joint. This paper introduces a proxy-based super-twisting algorithm (PSTA) for precise servo control of MP joints via FES. The PSTA combines first-order sliding mode control with a second-order super-twisting algorithm, effectively preventing windup during FES saturation and ensuring robust, accurate control. An implicit Euler method minimizes numerical chattering in the digital implementation. Experiments with Arduino and volunteers confirm the algorithm’s effectiveness.

Active Fault Tolerant Nonsingular Terminal Sliding Mode Control for Electromechanical System Based on Support Vector Machine

Effective fault diagnosis and fault-tolerant control method for aeronautics electromechanical actuator is concerned in this paper. By borrowing the advantages of model-driven and data-driven methods, a fault tolerant nonsingular terminal sliding mode control method based on support vector machine (SVM) is proposed. A SVM is designed to estimate the fault by off-line learning from small sample data with solving convex quadratic programming method and is introduced into a high-gain observer, so as to improve the state estimation and fault detection accuracy when the fault occurs. The state estimation value of the observer is used for state reconfiguration. A novel nonsingular terminal sliding mode surface is designed, and Lyapunov theorem is used to derive a parameter adaptation law and a control law. It is guaranteed that the proposed controller can achieve asymptotical stability which is superior to many advanced fault-tolerant controllers. In addition, the parameter estimation also can help to diagnose the system faults because the faults can be reflected by the parameters variation. Extensive comparative simulation and experimental results illustrate the effectiveness and advancement of the proposed controller compared with several other main-stream controllers.

Fuzzy sliding mode control with adaptive exponential reaching law for inverters in the photovoltaic microgrid

Photovoltaic inverters are widely utilized in microgrid systems working as the key equipment for converting solar energy into usable electricity. This paper presents a fuzzy sliding mode control (FSMC) method for the photovoltaic inverter in a microgrid. The inverter module uses voltage control to achieve stable AC output voltage. Moreover, to deal with system uncertainty and non-linearity in the photovoltaic inverter, a fuzzy controller was designed to realize real-time adaptation of the gains of the constant term and the reaching term in the sliding mode control law, which serves as a compensating controller for traditional sliding mode control. The gains of the exponential reaching law can be adjusted according to the system state instead of fixed gains, which can effectively reduce the chattering phenomenon and improve the robustness of the photovoltaic microgrid. Finally, the Lyapunov stability theory was used to ensure the stability of the entire control system, achieving a high-performance independent power supply for loads in a microgrid. Simulation results show that the designed control system is more robust to load disturbances and has superior dynamic performance.

Robust H∞ Static Output Feedback Control for TCP/AQM Routers Based on LMI Optimization

This paper proposes a new static output feedback control method to address the congestion control problem in transmission control protocol networks using active queue management routers. Based on linear matrix inequality optimization, this method determines a static output feedback control law to minimize the norm of the transfer function between the controlled queue length of the buffer and the exogenous disturbance affecting the available link bandwidth. A linear matrix inequality formulation is presented as a sufficient condition to guarantee the closed-loop system’s asymptotic stability while maintaining disturbance rejection within a specified level, regardless of round-trip time delays. The proposed robust static output feedback control eliminates the need to measure or estimate all system states, thus simplifying practical implementation. The effectiveness of the proposed design method is demonstrated by applying it in a practical process, as illustrated through a numerical example.

Combining finite-time control and prescribed tracking performance for uncertain PMSM driven steer-by-wire system with unknown disturbance

To solve the uncertainties in permanent magnet synchronous motor (PMSM) driven steer-by-wire (SbW) system, a prescribed-performance-control (PPC) based finite-time fuzzy controller is proposed. Specifically, the dynamics of SbW system driven by PMSM is analyzed, and combine PPC technology to transform the tracking error coordinates. Then, the velocity signal is obtained from the sigmoid function-based tracking differentiator by using the position signal. To solve the problem of mismatched disturbance, fuzzy logic system (FLS) are used as a universal approximator to estimate the lumped nonlinear friction and other unknown functions, the boundary of lumped disturbance is estimated by the designed adaptive updating law. By fusing the designed virtual controller and the intermediate control law, the controller needs no precise parameters of system, under which the effect of uncertainty of control gain can be compensated completely. Finally, rigorous theoretical analysis based on the Lyapunov stability theory is provided to demonstrate the finite-time stability of the closed-loop system under consideration. Simulation and experiment results are presented to show the effectiveness of the developed control approach, and some comparisons are given to show the rapid and accurate position tracking control performance.

Data‐driven adaptive control design for active stabilization of stratospheric airship imaging platform: A characteristic model approach

AbstractIn response to the problem of disturbance in the imaging equipment of stratospheric airships, this study designs a vibration isolation and stability system for carrying imaging equipment. The system mainly consists of microprocessor, gimbal, voice coil motor(VCM) and rubber ring. By using adaptive control law based on characteristic model to control the double closed control loop of the motor composed of position loop and speed loop, the camera deflection caused by disturbance is effectively eliminated. Furthermore, in vertical vibration suppression, the adaptive control law based on the characteristic model is also applied to the double closed control loop of the voice coil motor, which removes most of the vertical vibration and realizes active vibration isolation. The passive isolation of the system is completed by rubber rings. The experimental results show that the vibration isolation and stabilization system have good performance in complex disturbance environments, effectively stabilizing the imaging equipment.

Unified Control of Flight Dynamics and Actuators Using Local Accelerations

Control surface actuators usually come with their own servo control algorithm, which ensures that the deflection commanded by the flight control laws is quickly achieved. Therefore, flight control and servo control together form a cascaded control structure. Traditionally, the flight control laws (outer loop) and servo control laws (inner loop) are designed independently. However, recent applications demand an increasingly dynamic design of flight control, with actuator dynamics becoming a limiting factor. In this paper, a unified control design for actuators and flight dynamics is investigated. The local acceleration at the lifting surface (empennages and wing), close to the actuator, is fed back within an inner actuator control loop, allowing for faster compensation. The theoretical properties of the suggested control structure are discussed analytically first. Later, the concept is applied to a linear model for longitudinal aircraft motion. A classical state controller serves as a reference for comparison of the proposed strategy against the standard one with the actuator internal control law. Both controllers are designed using eigenstructure assignment, and the pole locations are numerically optimized. The closed-loop behavior is assessed in the time and frequency domains. The results promise significant improvements in terms of disturbance rejection and robustness.

Improved contactless attitude control law of uncooperative spacecraft by laser ablation

Laser ablation technology enables contactless debris removal, providing the advantages of safety and cost efficiency. To deorbit space debris using laser ablation, the attitude motion of the debris must be controlled in advance. Although a previous study derived an attitude control procedure, it empirically determines control gains and is easily affected by assumed preconditions. To solve this problem, this paper proposes an improved attitude control law using laser ablation. Previous methods use feedback control based on the angular velocity error and error quaternions to obtain a reference torque. By analogy with a magnetic attitude control law, this study designs a reference torque based on the angular momentum errors and presents theoretical condition for designing the control gains. Numerical examples using the proposed control law are conducted to control the target attitude from a random initial rotation of 1 rpm to an arbitrary attitude, which verify the effectiveness of the proposed method. Furthermore, the position, direction, and magnitude uncertainties of laser irradiation are introduced to numerically examine their effects on the control accuracy under environmental disturbances.

Integral sliding mode-based event-triggered optimal fault tolerant tracking control of continuous-time nonlinear systems

In this paper, the integral sliding mode-based event-triggered optimal fault tolerant tracking control of continuous-time nonlinear systems is investigated via adaptive dynamic programming. The developed control scheme consists of two parts, i.e., integral sliding mode control and event-triggered optimal tracking control. For the first part, an integral sliding mode controller is designed to eliminate the affect of actuator fault and the dynamics of nominal nonlinear systems is obtained. For the second part, a novel quadratic cost function with respect to the tracking error and its dynamics is developed such that the feedforward control law or the discount factor is not required, which reduces the complexity of the control method and guarantees the tracking performance. Moreover, a critic-only structure is established to obtain the solution of tracking Hamilton–Jacobi–Bellman equation. It should be noted that the optimal tracking control law is updated only at triggering moments in order to preserve computing and communication resources. Finally, the effectiveness of the present approach is demonstrated through simulation examples of a robotic arm system and a Van der Pol circuit system.

Advanced control scheme of doubly fed induction generator for wind turbine using second sliding mode control

This paper describes a speed control device for generating electrical energy on an electricity network based on the doubly fed induction generator(DFIG) used for wind power conversion systems. At first, a double-fed induction generator model was constructed. A control law is formulated to govern the flow of energy between the stator of a DFIG and the energy network using three types of controllers: proportional integral (PI), sliding mode controller (SMC) and second order sliding mode controller (SOSMC).Their different results in terms of power reference tracking, reaction to unexpected speed fluctuations, sensitivity to perturbations, and resilience against machine parameter alterations are compared. MATLAB/Simulink was used to conduct the simulations for the preceding study. Multiple simulations have shown very satisfying results, and the investigations demonstrate the efficacy and power-enhancing capabilities of the suggested control system.

Autonomous vehicle tracking control for a curved trajectory

Recently, research about trajectory tracking of autonomous vehicles has significantly contributed to the development of autonomous vehicle technology, particularly with novel control methods. However, tracking a curved trajectory is still a challenge for autonomous vehicles. This research proposes a state feedback linearization with observer feedback to overcome some difficulties arising from such a path. This approach suits a complex nonlinear system such as an autonomous vehicle. This method also has been compared with the linear-quadratic regulator (LQR) method. So, the goal of this research is to improve the control system performance of autonomous vehicles that are stable enough to navigate a curved path. Moreover, the study shows that the developed control law can track the curved path and solve existing problems. However, improvements are still necessary for the vehicle's performance and robustness.

Adaptive synchronous sliding control for a robot manipulator based on neural networks and fuzzy logic

Robot manipulators have become important equipment in production lines, medical fields, and transportation. Improving the quality of trajectory tracking for robot hands is always an attractive topic in the research community. This is a challenging problem because robot manipulators are complex nonlinear systems and are often subject to fluctuations in loads and external disturbances. This article proposes an adaptive synchronous sliding control scheme to improve trajectory tracking performance for a robot manipulator. The proposed controller ensures that the positions of the joints track the desired trajectory, synchronize the errors, and significantly reduces chattering. First, the synchronous tracking errors and synchronous sliding surfaces are presented. Second, the synchronous tracking error dynamics are determined. Third, a robust adaptive control law is designed, the unknown components of the model are estimated online by the neural network, and the parameters of the switching elements are selected by fuzzy logic. The built algorithm ensures that the tracking and approximation errors are ultimately uniformly bounded (UUB). Finally, the effectiveness of the constructed algorithm is demonstrated through simulation and experimental results. Simulation and experimental results show that the proposed controller is effective with small synchronous tracking errors, and the chattering phenomenon is significantly reduced.

Attitude and position control for formation flying of space robots equipped with a robotic manipulator

This paper presents a novel six degree-of-freedom guidance and control algorithm for free-flyer robots equipped with a robotic manipulator operating on the International Space Station. The proposed algorithm combines a sliding mode controller for rotational motion and a model predictive controller for translational motion. A novel prediction-twisting sliding mode controller (PT-SMC) is introduced to stabilize configuration changes induced by the manipulator and correlated disturbances and to achieve a trade-off among control law flexibility, robustness, and accuracy. The proposed PT-SMC is shown to have improved stability compared to a first-order sliding mode controller. Model predictive control is investigated as a position controller to achieve formation flight between multiple robots in a constrained environment, and a leader-follower approach is used to move a triangular formation of robots. A control allocation problem is solved to address the subdivision of attitude and position controls between twelve thrusters. Simulation results of multiple robots approaching a target object demonstrate the improved performance of the proposed control approaches.

Genetic programing control of self-excited thermoacoustic oscillations

In this experimental study, we use a data-driven machine learning framework based on genetic programing (GP) to discover model-free control laws (individuals) for suppressing self-excited thermoacoustic oscillations in a prototypical laminar combustor. This GP framework relies on an evolutionary algorithm to make decisions based on natural selection. Starting from an initial generation of individuals, we rank their performance based on a cost function that accounts for the trade-off between the state cost (thermoacoustic amplitude) and the input cost (actuator power). We then breed subsequent generations of individuals via a tournament in which the direct forwarding of elite individuals occurs alongside genetic operations such as mutation, replication, and crossover. We implement this GP control framework in both closed-loop and open-loop forms, followed by benchmarking against conventional open-loop control based on time-periodic forcing. We find that while all three control strategies can achieve similarly large reductions in thermoacoustic amplitude, GP closed-loop control consumes the least actuator power, making it the most efficient. It achieves this efficiency by learning an actuation mechanism that exploits the strong heat-release-rate amplification of the open flame at its preferred mode, even though the GP algorithm has never seen the open flame itself. This study demonstrates the feasibility of using GP to discover new and more efficient model-free individuals for suppressing self-excited thermoacoustic oscillations, providing a promising approach to data-driven feedback control of combustion devices.

A variety of inflammatory shadows caused by COVID-19 on chest CT that closely resembled lung cancer in annual lung cancer screening.

e23122 Background: Chest X ray (CXR) has been the most common screen test procedure for detection of lung cancer. Before COVID-19, we have shown that repeat visitors (R) had a significantly lower proportion of CXR abnormalities requiring medication than the first-time visitors (F) at annual lung cancer screening. (Kimura T. Health Prim Car, 2021). During COVID-19, we reported that the influence of large waves of COVID-19 in annual lung cancer screening. (Kimura T. abstract No. e18596, ASCO annual meeting 2023) Because of the widespread use of vaccines, the XBB variants, derived from Omicron BA.2, has mainly cold-like symptoms. These variants spreads more easily than the original or the Delta variants. In Japan, since May 2023, COVID-19 has been made less dangerous under the Infectious Disease Control Law, which loosens the restrictions on wearing masks and having close contacts. No longer visits the hospital unless there is a high fever or dyspnea. Hypothesis is that there is a difference in lung cancer screening after a COVID-19 infestation compared to during and before COVID-19. Methods: Our clinic “MedCity21” is a university outpatient clinic for medical check-ups as a part of private health screening programs. Our facility allows COVID-19 diagnosed participants (pts) 5 days after onset or 24 hours after symptoms have abated. When the abnormalities were detected on CXR, pts were notified in a week by telephone request and invited to our specialty clinic for chest CT scan as further examination. By year from 2018 to 2023, we examined the varieties of abnormal shadows on CXR and CT and compared the differences using the chi-squared tests. Results: In 2023, there were 15072pts were visited our clinic for private health screening, among them, 13451pts (F:23.8%, R:75.2%) had chest X-rays performed. The January 2022, percentage of pts with a history of COVID was 1.68%, gradually increased, reaching 46.6% in December 2023. There were 226pts (1.68%) with CXR abnormalities requiring further investigation. The rates in F and R were 3.06% and 1.23%, respectively. The distributions of CXR and CT variations on R group were consistent with our previous report. However, significantly increased rate of inflammatory changes in CT findings (16.3%) in F group compared to each previous year (3.2-6.9%). (p = 0.014) We experienced a variety of inflammatory shadows on chest CT that closely resembled lung cancer. Such shadows were confirmed to have shrunk or disappeared on CT scan 1-2 months later. Conclusions: The pts who had ever had COVID-19 gradually increased. This was accompanied by an increase in the number of inflammatory shadows that were difficult to differentiate from lung cancer. It is better to follow up patients with CT or CXR for a month instead of immediately examining them. COVID-19 may cause concomitant bacterial or viral pneumonia; COVID-19 may affect the immune mechanisms of healthy individuals.

Robust Predictive Fault-Tolerant Control under Actuator Random Failures

A robust predictive fault-tolerant switching control method is presented for a class of industrial processes that are prone to random actuator failures. The primary contribution is the combination of stochastic control theory with robust predictive control to represent the occurrence of system failure in the form of probability, thus significantly enhancing the conventional fault-tolerant control approach. First, a state-space model of the system is established, which is then transformed into an enhanced form that includes state deviations and output tracking errors. This augmented model enables the formulation of fault-tolerant switching control laws with fault probabilities, ensuring system convergence and tracking capabilities. Subsequently, linear matrix inequality constraints are given to find the switching controller gains under recovery and fault probabilities. Under certain probabilities, failure triggers fault-tolerant control, while recovery prompts a shift back to conventional control. Final, we validate the feasibility of our approach with a case study involving pressure regulation.

Output-feedback path-following control of underactuated AUVs via singular perturbation and interconnected-system technique

This paper focuses on the output-feedback control for path-following of underactuated autonomous underwater vehicles subject to multiple uncertainties and unmeasured velocities. First, a novel extended state observer is proposed to estimate the mismatched lumped disturbance and recover the unmeasured velocities. Based on this premise, to overcome the limitation of relying solely on the accurate kinematic model, a disturbance observer-based stabilizing controller is developed. The difference in bandwidths between the observer and the vehicle dynamics allows for a mathematical setup amenable to standard singular perturbation theory. In the fast mode, a kinematic observer is designed to reject system uncertainty caused by unknown attack angular velocity and prohibitive path-tangential angular velocity, using a novel physical perspective. In the slow mode, an interconnected-system control law is proposed by integrating the backstepping technique with the time scale decomposition method. Furthermore, the stability of the overall closed-loop system is established. Finally, simulation results are presented to demonstrate the effectiveness of the proposed method for path-following of underactuated autonomous underwater vehicles in the vertical plane.