Time-varying Control Research Articles

Time-varying command filters-based neural adaptive control with fixed-time prescribed performance for the PMSM with external disturbance and time delays

Time-varying command filters-based neural adaptive control with fixed-time prescribed performance for the PMSM with external disturbance and time delays

An emergency operation strategy and motion planning method for autonomous vehicle in emergency scenarios

Ensuring driving operational safety in emergency scenarios is paramount for autonomous vehicles to prevent accidents, particularly when vehicle motion completely depends on autonomous systems. Numerous factors must be evaluated when designing emergency collision avoidance strategies for critical situations, such as trajectory feasibility, vehicle motion stability, and driver comfort. Therefore, this study proposes a framework for emergency operation that uses collision-free area calculations to inform maneuver decisions and facilitate collision avoidance trajectory planning, preventing vehicle collisions. In case of danger, the emergency maneuver decision module evaluates the safety level and selects safety terminal state by considering a pre-specified cluster of candidate maneuvers before generating trajectories. This process avoids infeasible trajectories and selects maneuvers for greater driver comfort when available. Subsequently, the dynamic trajectory planning module converts the collision-free area into mixed-integer constraints, utilizing time-varying Nonlinear Model Predictive Control (NMPC) for trajectory planning and ensuring vehicle motion stability by integrating dynamic and collision-free constraints throughout the motion planning process. Eventually, simulations and field testing validate the framework’s effectiveness, mitigating collisions in emergency scenarios with prompt and safe operations. The framework is designed to function autonomously, independent of the intelligent driving system, engaging only during risk events and restoring control to the driver or the intelligent system after the event.

Stability analysis and asynchronous control of discrete-time cyclic switched systems with unstable subsystems

This paper studies the stability analysis and asynchronous control problems of discrete-time cyclic switched systems consisting of both stable and unstable subsystems based on slow/fast cycle-dependent average dwell time (ADT) switching. By Kronecker delta function, the cycle-dependent convex Lyapunov function (CLF) is first devised for the cyclic switched systems. It should be pointed out that the Lyapunov function has remarkably less conservativeness and more flexibility compared to traditional multiple Lyapunov function (MLF). Asynchronous cycle-dependent CLF and time-varying controller are proposed to investigate the asynchronous switching control of cyclic switched systems, and the relationship between the synchronous running time of stabilizable and unstabilizable subsystems and asynchronous time is characterized in the cycle. Finally, a numerical example and an application example are provided to illustrate the effectiveness of the proposed method.

Finite Time Hybrid Synchronization of Heterogeneous Duplex Complex Networks via Time-varying Intermittent Control

Abstract This paper study the finite time internal synchronization and the external synchronization (hybrid synchronization) for duplex heterogeneous complex networks by time-varying intermittent control. There few study hybrid synchronization of heterogeneous duplex complex networks. Therefore, we research the finite time hybrid synchronization of heterogeneous duplex networks, which employs the time-varying intermittent control to drive the duplex heterogeneous complex networks to achieve hybrid synchronization in finite time. To be specific, the switch frequency of the controllers can be changed with time by devise Lyapunov function and boundary function, the internal synchronization and external synchronization are achieved simultaneously in finite time. Finally, numerical examples are presented to illustrate the validness of theoretical results.

Prescribed-time stabilisation of stochastic high-order nonlinear systems with time-varying powers

A control strategy for stochastic nonlinear systems with time-varying high-order dynamics is presented in this paper, which is specifically designed for prescribed-time state-feedback stabilisation. The approach, based on the backstepping design, constructs a time-varying controller, utilising scaled quartic Lyapunov functions and the given scaling function is not used for coordinate transformation. The fundamental characteristic is that the system has a unique and strong solution, almost surely, is implemented for arbitrary initial conditions. The designed scheme ensures mean-square convergence of both state and input within a prescribed time. The validity of the theoretical framework is vividly verified through a numerical example, showcasing the practical application and efficiency of the control method.

Prescribed-time Leader-follower Consensus of Multi-agent Systems by Event-triggered Linear Time-varying Control

Prescribed-time Leader-follower Consensus of Multi-agent Systems by Event-triggered Linear Time-varying Control

New order-dependent conditions to control a class of nonlinear real-order systems

In many application points of interest, controlling the responses of real-world applications that vary with time seems quite challenging and puzzling. A linear time-varying state feedback controller is widely known and can be useful to estimate bounds to the state control matrix in the design of many control systems. The issue of initialization of real-order control system enhancement remains a challenging issue in the systems analysis subject to random initial-time placed on a real number line. We address a new design of a class of real-order control systems that consists of separated linear and nonlinear terms affected by input functions to be controlled with an implemented time-varying linear state feedback controller. We utilize the fractional comparison method and under Lipschitz nonlinearity with a constant bounding matrix of time-varying coefficients of control systems to address new order-dependent conditions that provide local and global stabilization to controlled systems. Applications of results that include practical real-order single-machine-infinite-bus power systems have been illustrated to control the responses by the utility of theoretical conditions examined along with validation of numerical simulations. It is shown that the proposed controller is practically convenient and demonstrates the efficiency of measuring the performances of control systems.

Time-varying formation control for heterogeneous multi-agent systems with random network and measurement noise

Time-varying formation control for heterogeneous multi-agent systems with random network and measurement noise

A smooth gradient approximation neural network for general constrained nonsmooth nonconvex optimization problems.

Nonsmooth nonconvex optimization problems are pivotal in engineering practice due to the inherent nonsmooth and nonconvex characteristics of many real-world complex systems and models. The nonsmoothness and nonconvexity of the objective and constraint functions bring great challenges to the design and convergence analysis of the optimization algorithms. This paper presents a smooth gradient approximation neural network for such optimization problems, in which a smooth approximation technique with time-varying control parameter is introduced for handling nonsmooth nonregular objective functions. In addition, a hard comparator function is introduced to ensure that the state solution of the proposed neural network remains within the nonconvex inequality constraint sets. Any accumulation point of the state solution of the proposed neural network is proved to be a stationary point of the nonconvex optimization under consideration. Furthermore, the neural network demonstrates the ability to find optimal solutions for some generalized convex optimization problems. Compared with the related neural networks, the constructed neural network has weaker convergence conditions and simpler algorithm structure. Simulation results and an application in optimizing condition number verify the practical applicability of the presented algorithm.

Model-free time-varying controller parameters optimization based on constrained extremum seeking approach for batch processes

Model-free time-varying controller parameters optimization based on constrained extremum seeking approach for batch processes

Consensus Control of Nonlinear Stochastic Multiagent Systems With Unknown and Time-Varying Control Coefficients Based on Novel Nussbaum Functions

Consensus Control of Nonlinear Stochastic Multiagent Systems With Unknown and Time-Varying Control Coefficients Based on Novel Nussbaum Functions

Adaptive Time-Varying Formation Maneuvering Control for Multi-Robot Systems in Complex Obstacle-Rich Environments

To tackle the challenge of time-varying formation control for underactuated robots under model parameter uncertainties and environmental disturbances, this study proposes an affine formation control approach enhanced by an Extended State Observer. Initially, using affine positioning theory and polynomial interpolation, guidelines for selecting leader vehicles and trajectory planning methods are established, whereby the trajectory of follower vehicles is uniquely determined through the stress matrix. To address the cumulative disturbances arising from model uncertainties and environmental factors impacting the formation, an Extended State Observer with optimized parameters is introduced. Furthermore, a distributed affine formation control method with disturbance rejection is designed specifically for underactuated robots with “nonlinear, strongly coupled” dynamics, ensuring that the formation system can track the target configuration within bounded error margins. Finally, theoretical analysis and simulation outcomes ultimately confirm the efficacy of the control approach in achieving resilient formation tracking.

A control-oriented magnetorheological elastomer isolator for high-tech facilities: development and experimental verification

Abstract To protect high-tech facilities from micro-amplitude vibrations, a control-oriented magnetorheological (MR) elastomer (MRE) isolator (MRE-I) is developed. The MRE-I, which combines features of high bearing capacity and large MR effect, works in a stacked type compression and shear mixed mode. Meanwhile, index of high magnetic-energy ratio for MRE-I, with considering both the MR effect and power consumption is defined in this paper. Firstly, the magnetic field function of the MRE-I works in mixed mode is deduced. Taking high magnetic-energy ratio as the optimization objective, and using a genetic algorithm to solve the problem, the structure parameters are obtained. Subsequently, force to displacement tests with multi-operating conditions are conducted on the prototype, demonstrating an increase of 1604.29% in output force under a medium level of magnetic field. Lastly, vibration control tests using a fuzzy controller are carried out under an excitation of 1 m s−2 in a frequency sweep condition ranging from 90 Hz to 135 Hz, reaching 40% reduction in root mean squared of acceleration response, demonstrating the feasibility of this MRE-I for time-varying, micro-amplitude and wide-band vibration control of high-tech facilities.

Time-varying formation consensus for fractional-order multi-agent systems based on sliding mode control

This article addresses the problem of time-varying formation control when the fractional derivatives of the expected formation are different from the fractional multi-agent system. To achieve the infinite convergence of the relative position among agents, the synthetic error of the agent is introduced. Then according to the sliding mode control theory, the sliding mode function is designed based on the defined error. The time-varying formation control problem converts into the asymptotic stability problem of the system. Sufficient conditions for fractional-order multi-agent systems are derived by employing the Lyapunov stability theory, matrix theory, graph theory, and property of fractional-order systems. Finally, two numerical simulations are presented to validate the theoretical analysis.

Online pre-perception of forming state based on real-time measurement in spinning of thin-walled shell component

Die-less spinning is an advance incremental forming process widely used for the manufacturing of thin-walled shell components. During the spinning, workpiece shape and forming state both change continuously, and the influence of process parameters on the forming results is also time-varying, which make it difficult to control the forming quality. To this end, this work develops a real-time measurement system and an online pre-perception method to provide technical support for dynamic control of the spinning process. Specifically, the real-time measurement system is constructed firstly by placing two laser profilers bilaterally at two sides of the workpiece. Based on the measuring data of two laser profilers, the profile data of workpiece cross-section is obtained by coordinate transformation, data denoising and correcting the error caused by inclined workpiece surface. Then, an algorithm is proposed to identify the critical geometric parameters (flange width, roller action radius, wall thickness and flange fluctuation degree) of workpiece shape from the profile data. In contrast to the measurement results by a three-coordinate measuring machine, the developed system presents a real-time measurement error less than 4 %. Moreover, an online pre-perception method of wall thickness and wrinkling defect is developed based on the real-time measured workpiece shape parameters. The online pre-perception of wall thickness and wrinkling defect presents high accuracy with the relative error less than 2.1 %. The above results indicate both the workpiece dimensions and spinning state can be well real-time measured and online pre-perceived, which can provide important foundation for the study of time-varying influence and dynamic control of the spinning process.

A time-varying EWMA-CV control chart based on Upper Triangular Minimax ranked set sampling method

When the process mean fluctuates with time and process standard deviation is a linear function of process mean, the coefficient of variation is usually used to measure the process variation in industry. On the other hand, the use of Ranked Set Sampling as a smart sampling technique enables the ranking of samples before measurement. The purpose of this research is to introduce a new sampling method referred to as Upper Triangular Minimax Ranked Set Sampling and design a time-varying Exponentially Weighted Moving Average control chart to monitor the coefficient of variation. Also, in this research, average run length, standard deviation of run length, median of run length, extra quadratic loss, relative average run length and performance comparison index criteria under two states including zero-state and steady-state have been used to compare the performance of the designed control chart with the ones provided in the literature. The results show the superiority of the proposed control chart based on the novel sampling method rather than the control charts available in the literature. The application of the proposed control chart is also shown through a real case and compared with available competitors in the literature.

Event-based adaptive neural resilient formation control for MIMO nonlinear MASs under actuator saturation and denial-of-service attacks

This paper focuses on the distributed event-triggered adaptive neural resilient time-varying formation control problem for a class of multiple-input multiple-output nonlinear multi-agent systems, where all network communication links between agents are subjected to denial-of-service (DoS) attacks simultaneously. A second-order resilient time-varying formation estimator is designed to obtain the unknown leader information in DoS attack active intervals. Meanwhile, a state-triggering mechanism (STM) is designed to save system communication resources. Nevertheless, the STM can lead to virtual control laws being non-differentiable. To circumvent the problem, we first design an adaptive neural resilient formation control scheme. Then, based on the adaptive neural resilient formation control scheme, we replace continuous states with intermittent ones. By utilizing a dynamic filtering technique, an event-based adaptive neural resilient formation control scheme is designed. The key technology of control scheme design is to establish an improved first-order auxiliary system to deal with the negative impact of actuator saturation. It is proved that formation tracking errors can converge to a residual set around zero, and all signals in the closed-loop system are semi-globally uniformly ultimately bounded. Finally, simulation results are presented to show the effectiveness of the control scheme.

Fixed-Time Control and Estimation of Discontinuous Fuzzy Neural Networks: Novel Lyapunov Method of Fixed-Time Stability.

This article considers the fixed-time stability (FXTS) issue for discontinuous system described by differential equation (DE) with time-varying parameters. Using the tool of differential inclusion (DI), several improved FXTS criteria and estimation formulas of settling time (SET) are derived by employing a relaxed Lyapunov method. The special novelty of the developed Lyapunov method is that the derivative of Lyapunov function possesses indefiniteness. As an important application, the established FXTS theorems are utilized to deal with fixed-time (FXT) synchronization issue for fuzzy neural networks (FNNs) possessing state discontinuity, where the fuzzy operation is used in the synaptic law computing, and one typical time-varying switching control protocol is designed. Moreover, a series of estimations of SET for FXT synchronization are given out. Finally, the simulation examples are present to substantiate the validity of the obtained results.

Adaptive iterative learning control for enhancing the dynamic path tracking accuracy of 6-degrees of freedom industrial robots

In this article, an adaptive iterative learning control (AILC) scheme has been proposed to enhance the accuracy of the dynamic path tracking of 6-degrees of freedom industrial robots. Based on the memorized data and current feedback from a three-dimensional visual measurement instrument, an adaptive algorithm is developed to update the time-varying control parameters of the AILC scheme iteratively. A new compensation signal is calculated to adjust the control inputs produced by the dynamic path tracking control module at each time interval. Through the adaptation algorithm, the identical initial conditions can be relaxed to some extent with the AILC scheme. Moreover, the stability analysis of the proposed AILC scheme is presented. Experimental results on FANUC M20iA, using C-Track 780 as a photogrammetry sensor, demonstrate the superior performance of the developed AILC scheme in terms of pose accuracy, disturbance rejection ability, and control performance.

Fully Distributed Dynamic Event-triggered Formation-Containment Control for Networked Unmanned Surface Vehicles with Intermittent Wireless Network Communications

Favorable neighboring interactions and economical transmission costs are the foundations of formation-containment control (FCC), while the complex marine environments hamper its expansion on networked unmanned surface vehicles (USVs). In this context, this paper investigates an intermittent dynamic event-triggered control scheme for USVs experiencing communication interruptions to achieve FCC. Specifically, the control architecture consists of two synchronously working sub-layers. In the first layer, an intermittent communications-based formation tracking controller is initially developed to endow USVs with higher endurance against communication interruptions, such that the leader USVs can form a desired formation pattern while following a virtual leader. Meanwhile, a dynamic event-triggered mechanism (DETM) is incorporated into the intermittent controller to reduce the update frequency of control signals with computable minimum inter-event time (MIET). Similarly, an intermittent DETM-based controller is proposed for followers to achieve containment missions in the second layer. Moreover, the global information is unnecessary with time-varying control gains. Finally, the simulations are provided to verify the effectiveness and superiority of the proposed control scheme.