Stabilization Control Problem Research Articles

Stability analysis of ‘roof-coal pillar’ structure in longwall multi-section mining of steeply dipping coal seam

To solve the problem of stability control of coal pillar in steeply dipping coal seam (SDCS), based on the characteristics of surrounding rock structure and stress environment of the coal pillar, a special bearing structure of ‘roof-coal pillar’ combination is proposed through theoretical analysis. The mechanical model of inclined ‘roof-coal pillar’ structure is established, and the stress and strength characteristics of each element in the structure are deduced. Combining numerical calculations (PFC2D-FLAC2D coupling model) and physical similarity simulation, the stress evolution characteristics and failure mechanism of the ‘roof-coal pillar’ structure under the influence of mining were revealed. The results show that the coal pillar of SDCS is easy to form a ‘roof-coal pillar’ combined bearing structure with the roof strata. Under the influence of mining, the stress distribution and deformation evolution within the ‘roof-coal pillar’ structure show strong non-uniform and asymmetric characteristics. Among them, the stress distribution shows an ‘ellipse’ shape which is offset to the upper section, and the main deformation zone of the structure is a diagonal deformation distribution zone that runs through the coal and rock in the same direction as the stress offset. The failure of the ‘roof-coal pillar’ structural firstly occurs in the coal area near the coal-rock interface on the side of the lower section, and finally forms a diagonal failure crack running through the coal and roof, which is manifested as a diagonal shear failure mode. The findings can provide a theoretical basis for the stability control of coal pillars in multi-section mining of SDCSs and improve the safety production technology level of the working face.

Electronic brake system-based hierarchical motion control of commercial vehicle for autonomous obstacle avoidance

As a high-level autonomous driving technology, efficient and safe automatic obstacle avoidance on highway is one of the critical and ongoing topics that requires in-depth research for commercial vehicles. Electronic control air pressure brake system, owing to its fast response and active braking ability, is an effective active-chassis technology to function path tracking and stability control in such condition. Yaw motion stability control problem of autonomous driving commercial vehicle based on active braking control with electronic air brake system for automatic obstacle avoidance is investigated. First, a hierarchical yaw motion dynamics control architecture is introduced after vehicle and brake system modeling. The quintic polynomial curve-based trajectory planning method and a Model Predictive Control based trajectory tracking control strategy are formulated. Second, to evade the possible stability losing issue in this circumstance, Cubature Kalman Filter (CKF) is utilized to estimate the vehicle motion states, and a fuzzy PID control-based differential brake stability control strategy is designed for the electronic brake system composed of front-and-rear two sets of dual-channel pressure regulator and ABS solenoid valve. Finally, the proposed hierarchical yaw stability control strategy for a commercial vehicle in typical obstacle avoidance conditions is comparatively verified based on the joint simulation tools. After the yaw stability control, the peak value of the lateral displacement error of trajectory tracking can be reduced by 44.7%. The yaw stability control strategy based on fuzzy PID control can reduce the yaw rate by 46%–57% and the sideslip angle by 60%–73% under different conditions. The results show that the proposed control strategy can effectively improve the yaw stability of the vehicle while avoiding obstacles at high speed.

Lyapunov-based prescribed-time stabilisation control of quantum systems

This paper addresses the prescribed-time stabilisation control problem for quantum systems characterised by a single control input. A control law based on the relative state distance is formulated. Through the application of prescribed-time Lyapunov control techniques, the stabilisation of the quantum system to one of the eigenstates of the internal Hamiltonian within a prescribed-time is established. Notably, in contrast to existing quantum system control methods, our proposed approach ensures stabilisation within a specified time, allowing for the flexible selection of the settling-time upper bound according to preference, independent of the initial conditions.

Model‐free adaptive load frequency control for power systems with wind penetration under deregulation environment

AbstractWith the gradual deregulation of the power system by the power department, the power system has developed into a large‐scale and multiregional control system. Because of the power system internal complexity enhancing, the stable operation of power system becomes increasingly difficult. This paper analyzes the load frequency control problem of multiregional interconnected power system with wind energy. This study designs an improved model‐free adaptive control algorithm based on I/O data. It avoids model establishment of the multiregional power system. It also effectively solves the problem of frequency stability control under the influence of load change, introducing the generation participation matrix to simulate bilateral contracts under the power market. The dynamic evolution relationship of the system with the generation participation matrix is established, taking a three‐regional power system with wind energy as an example. Frequency fluctuations in all three regions are between . Convergence times of frequency deviation are all within 30 s, much less than the response time of load frequency control. The simulation results further demonstrate the effectiveness of the proposed algorithm, comparing the control algorithm proposed in this paper with other algorithms, which proves that the proposed algorithm has good control performance.

Event-triggered lifted robust MPC stabilization control for space telescope on dual-rate actuated rigid spacecraft systems

This study addresses the pose stabilization control problem for enhancing a space telescope mounted on a spacecraft system under a dual-rate actuated setup. An input-lifting approach is utilized to manage the dual rates in the control components, specifically the orbital space telescope and its loading platform. To counteract external spatial perturbations and spacecraft system uncertainties, an integrated control scheme is proposed, combining real-time model parameter estimation, active compensation control, and event-triggered lifted robust model predictive control (ET-LRMPC). The event-triggered mechanism in the proposed algorithm minimizes computational resource consumption while maintaining effective spacecraft control. Numerical simulations demonstrate that the proposed control scheme achieves favorable results under persistent external perturbations and model uncertainties. In terms of the overshoot, the algorithm proposed reduces the control effect by up to 87.1% in the spatial displacement of the payload and 98.6% in the Euler attitude angle compared to the conventional control algorithm. For the control of the base, the amount of overshoots with respect to the conventional control in spatial displacement and Euler attitude angle is reduced by 49% and 97.1%.

Output Stabilizing Control of Complex Biological Networks Based on Boolean Algebra Analysis.

Output stabilizing control of biological systems is of utmost importance in systems biology since key phenotypes of biological networks are often encoded by a small subset of their phenotypic marker nodes. This study addresses the challenge of output stabilizing control for complex biological systems modeled by Boolean networks (BNs). The objective is to identify a set of constant control inputs capable of driving the BN toward a desirable long-term behavior with respect to specified output nodes. Leveraging the algebraic properties of Boolean logic, we develop a novel control algorithm that reformulates the output stabilizing control problem into a simple graph theoretic problem involving auxiliary BNs, the scale of which significantly decreases compared to the original BN. The proposed method ensures superiority over previous results in terms of both the number of control inputs and computational loads, since it searches for the solution within the reduced BNs while retaining essential structures needed for output stabilization. The efficacy of the proposed control scheme is demonstrated through extensive numerical experiments with complex random BNs and real biological networks. To support the reproducible research initiative, detailed results of numerical experiments are provided in the supplementary material, and all the implementation codes are made accessible at https://github.com/choonlog/OutputStabilization.

Anti-windup design for supercavitating vehicle based on sliding mode control combined with RBF network

During the longitudinal motion of a supercavitating vehicle, the stability control problem is complicated because of the nonlinear planing force on the tail part. The dynamic model of a supercavitating vehicle in longitude plane is nonlinear, simultaneously, the control instructions of a supercavitating vehicle may exceed the physical limits of an actuator. Therefore, designing a longitudinal stability control system for a supercavitating vehicle, not only the treatment of nonlinear planing force, but also the physical constraints of the actuator should be considered. For the longitudinal motion model of supercavitating vehicle, a cascade model is proposed, which decomposes the longitudinal motion of supercavitating vehicle into two subsystems. Sliding mode control based on RBF neural network compensation is adopted in the controller design process, and RBF neural network is exploited to approach the deviation caused by actuator saturation. The proposed control method can effectively compensate the performance degradation caused by control variable saturation, and has strong robustness.

Lyapunov-guided deep reinforcement learning for delay-aware online task offloading in MEC systems

With the arrival of 5G technology and the popularization of the Internet of Things (IoT), mobile edge computing (MEC) has great potential in handling delay-sensitive and compute-intensive (DSCI) applications. Meanwhile, the need for reduced latency and improved energy efficiency in terminal devices is becoming urgent increasingly. However, the users are affected by channel conditions and bursty computational demands in dynamic MEC environments, which can lead to longer task correspondence times. Therefore, finding an efficient task offloading method in stochastic systems is crucial for optimizing system energy consumption. Additionally, the delay due to frequent user–MEC interactions cannot be overlooked. In this article, we initially frame the task offloading issue as a dynamic optimization issue. The goal is to minimize the system’s long-term energy consumption while ensuring the task queue’s stability over the long term. Using the Lyapunov optimization technique, the task processing deadline problem is converted into a stability control problem for the virtual queue. Then, a novel Lyapunov-guided deep reinforcement learning (DRL) for delay-aware offloading algorithm (LyD2OA) is designed. LyD2OA can figure out the task offloading scheme online, and adaptively offload the task with better network quality. Meanwhile, it ensures that deadlines are not violated when offloading tasks in poor communication environments. In addition, we perform a rigorous mathematical analysis of the performance of Ly2DOA and prove the existence of upper bounds on the virtual queue. It is theoretically proven that LyD2OA enables the system to realize the trade-off between energy consumption and delay. Finally, extensive simulation experiments verify that LyD2OA has good performance in minimizing energy consumption and keeping latency low.

Co-design of distributed dynamic event-triggered scheme and extended dissipative consensus control for singular Markov jumping multi-agent systems under periodic Denial-of-Service jamming attacks

This paper introduces a distributed dynamic event-triggered (DDET) controller meticulously crafted to achieve resilient consensus control in singular Markov jumping multi-agent systems (SMJMAs) even in the face of periodic Denial-of-Service (DoS) jamming attacks. With a dual focus on optimizing network resource utilization and fortifying against cyber threats, we propose a distributed dynamic event-triggered scheme (DDETS) grounded in the principles of sampled data consensus. In the presence of cyber attacks, we demonstrate the transformation of the original consensus control challenge within the singular multi-agent system into a robust stability control problem, centering around a singular switching error system governed by the DoS attack signal. Acknowledging the disturbances that typify real-world scenarios, this paper establishes criteria for attaining exponential stability and stochastic admissibility within the singular Markov jumping switching error system under the extended dissipative performance index. Furthermore, we present a hybrid design for the consensus controller and DDETS, delineated through a set of solvable linear matrix inequalities (LMIs). Finally, a comprehensive array of numerical examples meticulously illustrates the compelling effectiveness and unequivocal superiority of our proposed methodology.

Finite‐time stabilization control of nonlinear systems with full‐state constraints

AbstractThis paper studies the finite time stabilization control (FTSC) problem of nonlinear systems with unknown functions and full‐state constraints (FSCs). A lemma dealing with unknown functions is put forward so that the assumptions and the “explosion of terms” (EOT) of backstepping are avoided. Compared with approximation algorithms, this algorithm can make the system states converge to the origin in a finite time. On the other hand, a new logarithmic constraint function is utilized to solve the drawback of barrier Lyapunov functions (BLFs). Then, combined with finite time control (FTC), an FTSC algorithm with FSCs is proposed. Finally, simulations of Chua's circuit system are given to verify the effectiveness and superiority of this algorithm. Compared with the approximation algorithm, this algorithm can theoretically make the states of Chua's circuit system to the origin in a finite time.

Finite-Time Contraction Stability and Optimal Control for Mosquito Population Suppression Model

Releasing Wolbachia-infected mosquitoes into the wild to suppress wild mosquito populations is an effective method for mosquito control. This paper investigates the finite-time contraction stability and optimal control problem of a mosquito population suppression model with different release strategies. By taking into account the average duration of one reproductive cycle and the influences of environmental fluctuations on mosquitoes, we consider two cases: one with a time delay and another perturbed by stochastic noises. By employing Lyapunov’s method and comparison theorem, the finite-time contraction stabilities of these two cases under a constant release strategy are analyzed. Sufficient conditions dependent on delay and noise for these two systems are provided, respectively. These conditions are related to the prespecified bounds in finite-time stability (FTS) and finite-time contraction stability (FTCS) of the system, and FTCS required stronger conditions than FTS. This also suggests that the specified bounds and the delay (or the noise intensity) play a critical role in the FTCS analysis. And finally, the optimal control for the stochastic mosquito population model under proportional releases is researched.

A Novel Deep Deterministic Policy Gradient Assisted Learning-Based Control Algorithm for Three-Phase DC/AC Inverter With an RL Load

This paper proposes a novel deep deterministic policy gradient (DDPG) assisted integral reinforcement learning (IRL) based control algorithm for the three-phase DC/AC inverter feeding a resistive-inductive (RL) load. The proposed controller autonomously updates its control gains online without the need to know the system model. Excellent steady-state and dynamic system responses are achieved by the proposed control algorithm with reasonably low computational complexity. Moreover, the important initial stabilizing control problem is solved through offline training that uses the DDPG technique. Details of the DDPG based training procedures are presented. Experimental results are presented to verify the efficacy of the proposed IRL based control method.

Finite-Time Stability Control of Uncertain Nonlinear Systems With Self-Limiting Control Terms.

In this brief, we define a self-limiting control term, which has the function of guaranteeing the boundedness of variables. Then, we apply it to a finite-time stability control problem. For nonstrict feedback nonlinear systems, a finite-time adaptive control scheme, which contains a piecewise differentiable function, is proposed. This scheme can eliminate the singularity of derivative of a fractional exponential function. By adding a self-limiting term to the controller and the virtual control law of each subsystem, the boundedness of the overall system state is guaranteed. Then the unknown continuous functions are estimated by neural networks (NNs). The output of the closed-loop system tracks the desired trajectory, and the tracking error converges to a small neighborhood of the equilibrium point in finite time. The theoretical results are illustrated by a simulation example.

Application of linear ordinary differential equations to the stability control of long time lag networks

Abstract Optimal control based on the exact synchronization of linear ordinary differential equations can provide conditions for the existence of optimal control of long-time lag network stability. In this paper, we first generalize network control systems, time lag systems, and modern control system stability theory to discuss long-time lag network stability analysis and control problems. The numerical solution method for solving ordinary differential equations with boundary conditions is proposed by combining the numerical solution method for initial value problems of differential equations and the iterative method for solving nonlinear equations. Finally, the fixed-time synchronization problem of complex networks with time-varying time lags under periodic intermittent control is studied. Two intermittent control strategies are designed based on the linear ordinary differential equation algorithm, and the convergence analysis of the synchronization error and the synchronization criterion are given. Numerical values show that the synchronization error converges to zero (< 1.0e − 5) in 0.32s, while the convergence times are 0.55s and 0.90s. The fixed times of the three methods are calculated to be T * = 0.52, T 1 = 0.71 and T 2 = 1.32, respectively, and the synchronization error system converges faster under the method in the paper. The numerical simulation results verify the effectiveness of linear ordinary differential equations in controlling the stability of long-time lag networks.

Single-Wheel Failure Stability Control for Vehicle Equipped with Brake-by-Wire System

In order to solve the problem of vehicle stability control after a single-wheel brake failure in the brake-by-wire system, a control strategy of braking force redistribution with a yaw moment is proposed to ensure the braking efficiency and stability of vehicles. In this strategy, a two-layer architecture is adopted. In the upper layer control, a fault factor is introduced to represent the real-time failure degree of the wheel, and the driver’s braking intention is perceived through the pedal travel and pedal speed of the driver. The braking force redistribution algorithm of the remaining three wheels is designed based on the wheel failure degree and braking intensity. In the lower control, according to the state parameters of the vehicle, the additional yaw moment, which controls the yaw rate and the sideslip angle of the vehicle, is calculated by using the sliding mode control theory, and the yaw moment is reasonably allocated to the normal wheel. By using MATLAB/Simulink and Carsim co-simulation, different braking strength and failure types are selected for simulation analysis. The simulation results show that the proposed control strategy can improve the braking efficiency and stability of the vehicle under different braking conditions.

ESN-Observer-Based Adaptive Stabilization Control for Delayed Nonlinear Systems with Unknown Control Gain

This paper investigates the observer-based adaptive stabilization control problem for a class of time-delay nonlinear systems with unknown control gain using an echo state network (ESN). In order to handle unknown functions, a new recurrent neural network (RNN) approximation method called ESN is utilized. It improves accuracy, reduces computing cost, and is simple to train. To address the issue of unknown control gain, the Nussbaum function is used, and the Lyapunov–Krasovskii functionals are used to address the delay term. The backstepping strategy and command filtering methodology are then used to create an adaptive stabilization controller. All of the closed-loop system’s signals are predicted to be confined by the Lyapunov stability theory. Finally, a simulation example is used to demonstrate the effectiveness of the suggested control mechanism.

Distributed observer-based consensus tracking for nonlinear MASs with nonuniform input delays and disturbances

In this paper, we investigate the consensus tracking problem of nonlinear MASs with nonuniform time-varying input delays and external disturbances. For each follower, the composited disturbance observer and the state observer are employed to estimate bounded composited disturbances and unmeasured states, and a distributed observer based on output-feedback is proposed to approximate the leader’s states approachably. Sequentially, the consensus tracking control is converted into a stability control problem for the nonlinear MASs with nonuniform time-varying input delays. Subsequently, a distributed controller based on the truncated prediction approach is presented, which only depends on the boundary value of time-varying input delays. The distributed controller can render each follower synchronically stable via the Lyapunov stability theory. Finally, a group of single-link manipulators is used as an example to verify the effectiveness of the theoretical results.

Adaptive control and almost disturbance decoupling for uncertain HOFA nonlinear systems

SummaryIn this paper, the problem of adaptive stability control for uncertain high‐order fully actuated (HOFA) nonlinear systems with disturbances is solved. The method of almost disturbance decoupling (ADD) is used to cope with unknown disturbances. Meanwhile, the tuning function is applied to design an adaptive update law. According to the full‐actuation feature of HOFA systems and Lyapunov stability theory, a novel adaptive controller is designed. In the end, the effectiveness of the control strategy is validated by a numerical simulation example and the attitude control of a spacecraft.

Stability analysis based on a control adjuster for switched neural networks by trajectory similarity

This paper focuses on a class of exponential stability control problem for the stochastic neural networks (SNNs) with time‐varying delays and Markov jump (TVDMJ). As a prerequisite to the main theorem, the existence and uniqueness of the solution for the main system are proved via contraction map principle. For the stability control of the system, we design a controller which can be adjusted the parameters to make the system stabilization. We use this controller to actively control the stability behavior of the system, rather than the papers by setting the appropriate parameters to make the system achieve a stable state. Based on the intermittent control, we design a novel trajectory similarity process control and obtain exponential stability results, which are less conservative than the existing theoretical results. Two examples based on some numerical calculation and simulation diagrams are presented to validate the effectiveness for the utilized techniques.

Global stabilization control for multiple input multiple output nonlinear systems with unknown function vector

AbstractIn this article, a control algorithm is proposed to solve the global stabilization control problem of multiple input multiple output (MIMO) nonlinear systems with unknown function vectors (UFVs). Firstly, a Lemma dealing with UFVs is proposed. Then, combined with the backstepping method, the controller is designed such that all signals of the closed‐loop system are globally stable. Compared with the approximation method, the algorithm in this article solves the global stabilization control problem. Compared with the assumptions of UFVs, the algorithm in this article reduces the conservatism problem. At the same time, the algorithm in this article also solves the “explosion of terms” problem of backstepping. Compared with the methods to solve this problem: dynamic surface control (DSC) and direct fuzzy control, the algorithm in this article can make all signals of the closed‐loop system converge to the origin. Finally, the algorithm is applied to the model of spacecraft with modified Rodrigues parameters, and the simulation results show the effectiveness of this algorithm.