Control Problem For Systems Research Articles

Adaptive fault-tolerant containment control for heterogeneous uncertain nonlinear multi-agent systems under actuator faults

As modern control systems involve a growing number of actuators, the consequent number of faults occurrence, which may deteriorate the closed-loop system performance, is increasing. Focusing on the containment control problem of generic nonlinear high-order heterogeneous uncertain Multi-Agent Systems, this study proposes a novel distributed adaptive fault-tolerant Proportional-Integral-Derivative control protocol to improve the reliability and the resilience of the whole networked systems against actuator failures. The Lyapunov theory, combined with the Barbalat lemma, provides the design of suitable adaptive mechanisms that adjust the PID control actions to ensure the asymptotic convergence of the closed-loop containment errors for the overall network, hence implying the convergence of each agent state toward the convex region shaped by the multiple leaders, despite the occurrence of actuators faults. The fulfillment of the fault-tolerant containment objective is ensured by requiring a lower computational burden with respect to alternative solutions, hence resulting in more attractive for wider practical engineering applications. Numerical simulation results, also including a comparison analysis with a state-of-the-art controller, corroborate the efficiency and the advantages of the proposed distributed PID controller.

Generalized Homogeneous Formation Control for Unicycle Multi-Agent Systems

In this paper, the formation control problem for unicycle multi-agent systems is considered. The design of a generalized homogeneous leader–follower formation control protocol is studied which shows that such a control protocol can be obtained by “upgrading” from the classical linear control. With this proposed control protocol, a finite-time stability of the formation error is ensured if the acceleration of the leader is bounded by some known value. In addition, if the leader acceleration is unknown but bounded, the robust formation is achieved by ensuring the formation error’s input-to-state stability. Simulations are carried out to verify the effectiveness of the proposed control protocol.

Fault-tolerant control of nonlinear networked control systems based on a dynamic event-triggered mechanism under dual cyber attacks

In this paper, the fault-tolerant control problem of actuator failures for nonlinear networked control systems under deception attacks and denial-of-service attacks is investigated based on a dynamic event-triggered mechanism. Firstly, denial-of-service attacks and deception attacks are modeled as two independent variables that conform to the Bernoulli probability distribution, and the fault-tolerant control problem of nonlinear networked control systems is considered under the possible actuator failures. Then, an appropriate Lyapunov-Krasovskii functional is constructed, and sufficient conditions for ensuring asymptotic stability of the closed-loop systems are obtained using Jensen inequality. Meanwhile, the designed fault-tolerant controller can handle possible actuator failures. Finally, the effectiveness of the proposed method is proved through an example.

Low-complexity tracking control of hydraulic excavator systems with asymmetric full-state constraints

This work is devoted to solving the control problem of hydraulic excavator systems under full-state constraints. The standout aspect of the proposed control approach lies in the fact that not only full-state constraints can be accommodated, but also the settling time and tracking precision can be explicitly specified in advance, irrespective of the arbitrary initial poses of the hydraulic excavator. Additionally, a novel nonlinear state-dependent function is introduced, enabling the system to handle a larger range of initial conditions. The proposed controller, which solely relies on the state variables after nonlinear transformation, has lower complexity in both structure and expression. The effectiveness of the proposed approach is demonstrated through simulations and experiments.

Enhanced predictive PDF control of stochastic distribution systems with neural network compensation and its application

Compared to the conventional control algorithms that use mean and variance as indicators, predictive probability density function (PDF) control can effectively handle the output PDF control problem of non-Gaussian stochastic distribution systems. However, the existing predictive PDF control method does not consider the construction error between output PDF and weight, thus the control performance is still unsatisfactory. Therefore, this paper proposes a new Enhanced Predictive PDF control method (En-PDF) to improve the output PDF control performance of stochastic distribution systems. The proposed method mainly consists of two parts: the predictive PDF control part and the neural network compensation control part aiming to reduce the bias of output PDF. First, the Radical Basis Functions (RBFs) are used to approximate the PDF of the stochastic systems output, and then a prediction model representing the relationship between input and weight is established using the subspace identification algorithm to design the predictive PDF control for the stochastic systems. Next, the Kullback-Leibler (KL) divergence is used to measure the similarity between the output PDF and the set PDF, combined with the weight error and compensation to design a new performance index. Based on this, the parameters of the neural network are adjusted using the gradient descent algorithm to obtain the optimal compensation, and the stability and tracking performance of the proposed algorithm are analyzed using inductive reasoning method. Finally, the predictive PDF control input with the compensation work together on the controlled plant to achieve high-performance control of the output PDF of non-Gaussian stochastic distribution systems. Both simulation experiments and physical control experiments validate the effectiveness and superiority of the proposed method.

Hierarchical Stackelberg Control for a Two-Stroke Linear System in Population Dynamics

We study a unique hierarchical control problem for a two-stoke linear system, adjoint to an age and space dependent population dynamics problem. Using Stackelberg's method, we introduce two levels of control: a boundary control to achieve optimal flow regulation and a distributed control for null controllability. Our approach employs Carleman inequalities to address non-homogeneous Dirichlet boundary conditions, leading to new insights in controlling invasive species populations. These results highlight the applicability of hierarchical controls in ecological systems, providing a robust framework for future studies in control theory and population dynamics.

Nonsingular Terminal Sliding Mode Control for Vehicular Platoon Systems with Measurement Delays and Noise

Platooning of vehicular systems has been considered an effective solution for alleviating traffic congestion and reducing energy consumption. Because of limitations in onboard sensors, the measurement system inevitably suffers from measurement delays and noise, yet it receives insufficient attention. In this article, to deal with the measurement delays and noise while improving convergence performance, the platoon control problem of vehicular systems is studied under the nonsingular terminal sliding mode control (NTSMC) framework. A sliding mode observer (SMO) is proposed to estimate the states affected by measurement delays and noise. A distributed NTSMC scheme is developed for the platooning of the vehicular systems and ensures the convergence of the sliding mode surface affected by measurement delays and noise. One salient feature of the proposed SMO is that it can handle time-varying measurement delays rather than constant ones. Moreover, the control law is free of initial spacing error conditions under the employed coupled spacing policy. Numerical simulations are finally provided to demonstrate the effectiveness and efficiency of the proposed algorithm.

Fixed-time second-order sliding mode output feedback trajectory tracking control for an autonomous surface vehicle with model uncertainties and prescribed performance

This paper investigates the fixed-time trajectory tracking control problem of autonomous surface vehicle system with asymmetric performance constraints. The external disturbances, model uncertainties and the difficulty of velocity measurement are all taken into account in the controller design. Asymmetric performance constraints are firstly studied to improve the transient and steady-state performance of the tracking error system. Next, a fixed-time extended state observer is developed to estimate the unknown velocity and external disturbances. Both the fixed-time first-order and the second-order PID-type sliding surfaces are designed. Further, two continuous output feedback controllers are built to achieve high control accuracy and low energy consumption and to achieve fixed-time stability of the closed-loop system. Finally, abundant simulations for CyberShip II demonstrate the effectiveness of the proposed algorithms.

Applications and analyses of ammonia slip control based on precise ammonia injection technologies guided by big data

Abstract After the implementation of ultra-low emission policies, the pollutant emissions from the coal-fired power generation systems in China have been further reduced, which creates more critical requirements for the control accuracies of denitrification systems using selective catalytic reduction technology and controls of ammonia slip. This article presents big data-based technologies for controlling ammonia slip, through precise ammonia injections, which were applied and demonstrated for a flue gas denitrification system of a Chinese coal-fired power plant. Through examinations and analyses of the basic operating conditions of the unit and parameters, such as NOx emission control efficiency, ammonia injection amount of the denitrification system, and non-uniformity of NOx concentrations in the denitrification zone were compared before and after the implementation of these technologies, the outcome proves that this artificial intelligence algorithm based on big data can effectively solve the automatic control problem of denitrification system under complex working conditions such as varied unit loads, day and night operation changed coal source. In addition to the effective controls of the ammonia slip of the system, the controls of NOx emission of the system become more stabilized, creating a successful example for wider applications of these precise ammonia injection control technologies in the future. Analyses show that NOx non-uniformities can be reduced by more than 50% under both stable and variable load conditions. NOx fluctuations at the unit outlet are tightly controlled within ±10 mg/Nm3 under variable load conditions and within ±5 mg/Nm3 under stable conditions. The average ammonia injection amounts under various load conditions have decreased by 15.7%.

Adaptive output feedback command filter control based on extreme learning machine for nonaffine time delay systems with input saturation

This paper is concerned with the tracking control problem of nonlinear time delay systems. For time delay systems, we consider nonaffine pure-feedback structure. Additionally, the case where the time delay systems are subject to input saturation and unknown states is also taken into account. To begin with, a mean value theorem and an implicit function theorem are exploited to transform the systems into an affine form. Afterwards, a state observer is developed to estimate the unknown state variables and an extreme learning machine (ELM) is used to approximate the unknown functions. The output of the command filter replaces the virtual control signal'derivative, thereby circumventing the problem of ‘explosion of complexity’. Meanwhile, compensation signals are introduced to eliminate the filtering errors in a dynamic surface control (DSC). A Lyapunov–Krasovskii functional is designed to mitigate the unknown constant state time delay. During the controller design process, combining a prescribed performance control (PPC) with a command filter control, the tracking error can converge to a predetermined bound. Additionally, the effect of input saturation is eliminated with the aid of an auxiliary system. Finally, the effectiveness of such controller is further proved by taking an electromechanical system as an application object.

Adaptive neural network backstepping control for the magnetorheological semi-active air suspension system with uncertain mass and time-varying input delay

Aiming at rejecting external disturbance induced by the random road profile, this paper proposes an adaptive robust control strategy to address the control problem of semi-active air suspension system installed with magnetorheological dampers (MRD) for enhancing ride comfort and handling performance. To effectively depict the inherent nonlinear dynamics of the adjustable air spring, a radial basis function neural network (RBFNN) model is trained by the experimental data offline, and an adaptive learning algorithm is introduced to maintain the modeling accuracy. Moreover, to handle the prevalent sensitive parameter variation (such as sprung mass), a nonlinear estimator is designed to estimate the uncertain sprung mass. Furthermore, a delay compensator is integrated into the control law to mitigate the impact of the time-varying input delay caused by the actuator of MRD to restrain the chattering phenomena. Additionally, the stability of the closed-loop system is rigorously proven by employing a Lyapunov–Krasovskii functional, guaranteeing the boundedness of both tracking error and estimation error. Simulation results are displayed and analyzed, illustrating the feasibility and efficiency of the proposed control strategy in depressing the sprung mass acceleration and tire deflection, showing its significance in both control performance enhancement and chattering elimination.

Controllability and minimum energy control problem of two-dimensional continuous-time fractional linear systems

The effectiveness of this paper lies in a new formulation and solution of the minimum energy control problem for the fractional two-dimensional systems described by the Fornasini–Marchesini first models. The problem of controllability and minimum energy control of a general fractional two-dimensional Fornasini–Marchesini model is newly considered by the authors in this work. Necessary and sufficient controllability conditions are then established using the Gramian matrix. To carry out this study, a new test was described and used in order to give the existence conditions of the solution to the minimum energy control problem and the procedures for calculating an input minimising the given performance index. The usefulness and the precision of the proposed procedure is demonstrated by a numerical examples, Darboux equation and metal rolling process model as a practical application.

Flatness-based control in successive loops for dual-arm robotic manipulators

Dual-arm robotic manipulators are used in industry and for assisting humans since they enable dexterous handling of objects and more agile and secure execution of pick-and-place, grasping and lifting, or assembling tasks. In this article, a multi-loop flatness-based controller is proposed for the dynamic model of a dual-arm robot. In the considered application, the dual-arm robotic system has to lift and transfer an object under synchronized motion of its two end-effectors while it has also to compensate for the grasping forces between the two end-effectors and the object. The dynamic model of this robotic system is formulated while it is proven that the state-space description of the robot’s dynamics is differentially flat. The control problem for this robotic system is solved with the use of a flatness-based control approach which is implemented in successive loops. To apply the multi-loop flatness-based control method, the state-space model of the dual-arm robotic manipulator is separated into subsystems, which are connected in cascading loops. Each one of these subsystems can be viewed independently as a differentially flat system and control about it can be performed with inversion of its dynamics as in the case of input–output linearized flat systems. The state variables of the preceding i-th subsystem become virtual control inputs for the subsequent ( i + 1)-th subsystem. In turn, real control inputs are applied to the last subsystem. The whole control method is implemented in successive loops and its global stability properties are also proven through Lyapunov stability analysis. The multi-loop flatness-based control approach is an exact nonlinear control scheme which (i) does not use approximate linearization of the dynamic model of the controlled system and (ii) does not need changes of state variables and complicated state-space model transformations.

Adaptive visual servoing control for uncalibrated robot manipulator with uncertain dead-zone constraint

This paper addresses the adaptive visual tracking control problem of an uncalibrated camera robot system with unknown dead-zone inputs. The uncertainties include camera extrinsic and intrinsic parameters, robot dynamic parameters, and feature depth parameters. The control achieves a better performance by establishing a smooth inverse model of the dead-zone input, which eliminates the nonlinear effect of the actuator constraint. A novel adaptive control scheme is presented which does not require a priori knowledge of the parameter intervals of dead-zone model, to update the parameter values online. Furthermore, adaptive laws are provided to estimate the uncertainties that exist both in robot and in camera models. Meanwhile, in order to avoid the singularity of the image Jacobian matrix, a projection algorithm is also introduced. Subsequently, a novel robust adaptive controller together with dead-zone compensation is established so that the tracking error image space converges to the origin. Simulations and experimental studies are conducted to verify the effectiveness of the proposed method.

Machine learning model‐based optimal tracking control of nonlinear affine systems with safety constraints

AbstractThis work focuses on the development of a machine learning (ML) model‐based framework for safe optimal tracking control of a class of nonlinear control‐affine systems to ensure simultaneous closed‐loop stability and safety. Specifically, a novel multilayer feedforward neural network (FNN) with a control‐affine architecture is designed to model nonlinear dynamic systems. Subsequently, a model‐based reinforcement learning (RL) framework is presented, utilizing a novel cost function with Control Lyapunov‐Barrier Function (CLBF) properties, to learn both the control policy and the optimal value function for an infinite‐horizon optimal tracking control problem for nonlinear systems with safety constraints. The efficacy of the proposed methodology is demonstrated through simulations of a one‐link robot manipulator and a chemical process example.

Event-driven intelligent fault-tolerant containment control for nonlinear multiagent systems with unknown disturbances

This paper investigates the event-driven intelligent fault-tolerant containment control problem for nonlinear multiagent systems affected by unknown disturbances, and conducts a comprehensive analysis of the dynamic stability of the system. Reduced-order modelling is employed for system modelling and identification, while radial basis function neural networks are utilised to estimate the nonlinear functions of the system model. An event-driven control scheme is designed based on the backstepping method and Lyapunov functional approach. In contrast to existing consensus control schemes, the proposed event-driven control schemes are specifically tailored to improve the energy efficiency and endurance of nonlinear multiagent systems operating in complex environments. Under the proposed control law, the output of each follower is converge to the convex hull spanned by the outputs of the leaders. The effectiveness of the proposed method is rigorously validated through numerical simulations. Furthermore, a practical example involving the design of a marine surface vehicle using advanced robotics technology is presented to further substantiate the efficacy of the proposed method.

Comparison of active and passive vibration control techniques for electric drive power electronic subsystem: Experimental validation

Reducing vibration and noise from power electronics components is vital for enhancing the performance and acceptance of electric vehicles, emphasizing the need for effective mitigation strategies in sustainable transportation. This paper compares passive and active vibration techniques, with a specific focus on utilizing particle damping for passive vibration control. The method achieves vibration reduction through viscoelastic deformation and friction resulting from the movement and collisions of granular material within the cavity. This contribution outlines diverse design strategies for integrating particle dampers into power electronic components. The active control approach, operating typically in the 50 Hz to 1200 Hz frequency range, can concurrently support passive noise treatments. Piezoelectric ceramics serve as both actuators and sensors in active noise reduction. Following the identification of dominant mode shapes, appropriate actuator and sensor positions are chosen. The control problem of the smart system is then addressed, opting for a velocity feedback control algorithm in a real collocated design. This algorithm computes input signals for actuators bonded to the outer surface, achieving significant active damping effects. Lab-tested techniques are validated in real-world conditions with a full-scale electric-drive toolkit, affirming their efficacy in reducing vibration and noise and underscoring their potential for implementation in electric vehicles.

Predefined-time adaptive fuzzy control for nonlinear output constraint systems with unknown backlash-like hysteresis via command filtering

In this article, the predefined-time adaptive fuzzy control problem for strict-feedback nonlinear systems with asymmetric time-varying output constraint and unknown backlash-like input hysteresis is considered. An innovative predefined-time adaptive control method, in which utilises unified barrier function (UBF) to deal with the output constraint problem and adopts a differential equation to represent the unknown backlash-like hysteresis, is presented by combining a novel command filter with backstepping technology. Fuzzy logic systems (FLSs) are employed to estimate the unknown nonlinearities. Moreover, by designing appropriate parameters, the constructed control strategy ensures that all signals are predefined-time bound and the tracking error stays in a small neighbourhood adjacent zero. Ultimately, two simulation examples are given to prove the effectiveness of the presented control algorithm.

Robust Predefined Output Containment for Heterogeneous Nonlinear Multiagent Systems Under Unknown Nonidentical Leaders' Dynamics.

This article discusses the robust predefined output containment (RPOC) control problem for heterogeneous nonlinear multiagent systems having multiple uncertain nonidentical leaders. In order to solve this problem, a new kind of distributed observer-based RPOC control framework is presented. First, for obtaining the information of nonidentical leaders' dynamics, including uncertain parameters in leaders' system matrices, output matrices, states, and outputs, four kinds of adaptive observers are constructed in a fully distributed form without any knowledge of the dynamics of nonidentical leaders, exactly. Second, on the basis of adaptive learning technique, a new RPOC controller is then developed by using the presented observers, where the adaptive observers can make up for the uncertain parameter in followers' dynamics, and the solutions of output regulation equations can be obtained adaptively by the developed adaptive strategy. Furthermore, with the help of the output regulation method and Lyapunov stability theory, the RPOC criteria for the considered system under unknown nonidentical leaders' dynamics are derived from the constructed controller. Finally, a simulation example is provided to demonstrate the effectiveness of the proposed RPOC controller.

Adaptive Distributed Control of Nonlinear Multiagent Systems With Event-Triggered for Communication Faults and Dead-Zone Inputs.

This article studies the containment control problem of nonlinear multiagent systems (MASs) subjected to communication link faults and dead-zone inputs. In case of an unknown fault in the communication link, there is no constant Laplacian matrix anymore and each follower agent cannot be informed of the global information simultaneously. To deal with this problem, an adaptive compensating estimator is constructed to estimate the signal spanned by the leaders. Instead of using the linear filter, a nonlinear filter is employed, which both solves the classical complexity explosion in the traditional backstepping method and flushes out the usefulness of the boundary layer error. Considering the dead zone input, we propose two event-triggered schemes, that is, the update-triggered scheme and the transmit-triggered scheme. In the former, the threshold function involves the tracking errors and additional dynamic variable, which can provide the desirable tradeoff between the containment control performance of the considered MASs and saving communication resources. In the latter, the triggered condition is designed according to the characteristic of dead zone, which makes the communication burden be reduced further. Following the backstepping design framework, an adaptive containment control is constructed, it is shown that the containment error can converge to an adjustable residual set even if MASs are subjected to the unknown and bounded communication link faults and dead-zone inputs. Finally, an example is given to show the effectiveness of the proposed results.