Uncertain Systems Research Articles

Distributed Adaptive Formation Tracking for a Class of Uncertain Nonlinear Multiagent Systems: Guaranteed Connectivity Under Moving Obstacles.

This article explores a guaranteed network connectivity problem during moving obstacle avoidance within a distributed formation tracking framework for uncertain nonlinear multiagent systems with range constraints. We investigate this problem based on a new adaptive distributed design using nonlinear errors and auxiliary signals. Within the detection range, each agent regards other agents and static or dynamic objects as obstacles. The nonlinear error variables for formation tracking and collision avoidance are presented, and the auxiliary signals in formation tracking errors are introduced to maintain network connectivity under the avoidance mechanism. The adaptive formation controllers using command-filtered backstepping are constructed to ensure closed-loop stability with collision avoidance and preserved connectivity. Compared with the previous formation results, the resulting features are as follows: 1) the nonlinear error function for the avoidance mechanism is considered an error variable, and an adaptive tuning mechanism for estimating the dynamic obstacle velocity is derived in a Lyapunov-based control design procedure; 2) network connectivity during dynamic obstacle avoidance is preserved by constructing the auxiliary signals; and 3) owing to neural networks-based compensating variables, the bounding conditions of time derivatives of virtual controllers are not required in the stability analysis.

Event-Triggered Adaptive Neural Network Tracking Control for Uncertain Systems With Unknown Input Saturation Based on Command Filters.

This brief presents a modified event-triggered command filter backstepping tracking control scheme for a class of uncertain nonlinear systems with unknown input saturation based on the adaptive neural network (NN) technique. First, the virtual control functions are reconstructed to address the uncertainties in subsystems by using command filters. A piecewise continuous function is employed to deal with the unknown input saturation problem. Next, an event-triggered tracking controller is developed by utilizing the adaptive NN technique. Compared with standard NN control schemes based on multiple-function-approximators, our controller only requires a single NN. The closed-loop system stability is analyzed based on the Lyapunov stability theorem, and it is shown that the Zeno behavior is also avoided under the designed event-triggering mechanism. Simulation studies are performed to validate the effectiveness of our controller.

Asymptotic Tracking Control for Uncertain MIMO Nonlinear Systems With Guaranteed Performance and Enhanced Controllability

Asymptotic Tracking Control for Uncertain MIMO Nonlinear Systems With Guaranteed Performance and Enhanced Controllability

Regret optimal control for uncertain stochastic systems

We consider control of uncertain linear time-varying stochastic systems from the perspective of regret minimization. Specifically, we focus on the problem of designing a feedback controller that minimizes the loss relative to a clairvoyant optimal policy that has foreknowledge of both the system dynamics and the exogenous disturbances. In this competitive framework, establishing robustness guarantees proves challenging as, differently from the case where the model is known, the clairvoyant optimal policy is not only inapplicable, but also impossible to compute without knowledge of the system parameters. To address this challenge, we embrace a scenario optimization approach, and we propose minimizing regret robustly over a finite set of randomly sampled system parameters. We prove that this policy optimization problem can be solved through semidefinite programming, and that the corresponding solution retains strong probabilistic out-of-sample regret guarantees in face of the uncertain dynamics. Our method naturally extends to include satisfaction of safety constraints with high probability. We validate our theoretical results and showcase the potential of our approach by means of numerical simulations.

Output Tracking Predictive Control of Uncertain Networked Control Systems

Output Tracking Predictive Control of Uncertain Networked Control Systems

Adaptive Prescribed-Time Stabilization of Uncertain Nonlinear Systems With Unknown Control Directions

Adaptive Prescribed-Time Stabilization of Uncertain Nonlinear Systems With Unknown Control Directions

Finite-Time Stabilization of Uncertain Markovian Jump Systems: An Adaptive Gain-Scheduling Control Method

Finite-Time Stabilization of Uncertain Markovian Jump Systems: An Adaptive Gain-Scheduling Control Method

Evaluating alternatives to improve drinking water supply within a complex and uncertain human-environment system: case of the Port-au-Prince Metropolitan Region

ABSTRACT Improving decision making in urban water supply systems under a wide range of changing human-environmental scenarios represents a challenging issue, especially when there is a high level of uncertainty and the available information is limited or scarce. To address this concern, the present study applied a decision aiding process to evaluate long-term planning solutions for the Port-au Prince Metropolitan Region’s water supply in Haiti, which is recognized for having a low coverage of water services and great lack of monitoring data. The methodological framework assessed pre-specified alternatives to face uncertain future conditions, with the main emphasis of being aware of how these uncertainties can be framed in the water management practices. To discover the most robust management solutions, a tradeoff analysis and multi-objective optimization allowed to explore the performance of alternatives across many future states of the world and multiple criteria. The results highlighted that human factors associated with the increase of per-capita water use and population growth posed a greater risk on water supply coverage than climatic factors. In addition, the decision support can offer a comprehensive representation of potential supply/demand futures, and provides decision-makers with the opportunity to discuss a posteriori the most appropriate water supply solution under an extensive variety of information with severe uncertainty.

Fuzzy Fixed-Time Event-Triggered Consensus Control for Uncertain Nonlinear Multiagent Systems With Memory-Based Learning

Fuzzy Fixed-Time Event-Triggered Consensus Control for Uncertain Nonlinear Multiagent Systems With Memory-Based Learning

Strong Prescribed-Time Stabilization of Uncertain Nonlinear Systems by Periodic Delayed Feedback

Strong Prescribed-Time Stabilization of Uncertain Nonlinear Systems by Periodic Delayed Feedback

Event-Triggered Consensus of Uncertain Euler-Lagrange Multiagent Systems Over Jointly Connected Digraphs.

In this article, a fully distributed event-triggered protocol is proposed to solve the consensus problem of uncertain Euler-Lagrange (EL) multiagent systems (MASs) under jointly connected digraphs. First, distributed event-based reference generators are proposed to generate continuously differentiable reference signals via event-based communication under jointly connected digraphs. Unlike some existing works, only the states of agents rather than virtual internal reference variables need to be transmitted among agents. Second, adaptive controllers are exploited based on the reference generators so that each agent can track the reference signals. The uncertain parameters converge to their real values under an initially exciting (IE) assumption. It is proved that the uncertain EL MAS achieves state consensus asymptotically under the proposed event-triggered protocol composed of the reference generators and the adaptive controllers. A unique feature of the proposed event-triggered protocol is its fully distributed property: the protocol does not depend on global information about the jointly connected digraphs. Meanwhile, a minimum interevent time (MIET) is guaranteed. Finally, two simulations are conducted to show the validity of the proposed protocol.

A Small-Gain Co-Design Approach to Adaptive Neural Sampling Control for Uncertain Nonholonomic Systems

A Small-Gain Co-Design Approach to Adaptive Neural Sampling Control for Uncertain Nonholonomic Systems

Distributed Feedback Optimization of Nonlinear Uncertain Systems Subject to Inequality Constraints

Distributed Feedback Optimization of Nonlinear Uncertain Systems Subject to Inequality Constraints

A New Saturation Control for Uncertain System Using Diffeomorphism Approach: Application to Turbofan Engine

A New Saturation Control for Uncertain System Using Diffeomorphism Approach: Application to Turbofan Engine

Robust Output Regulation of a Class of Minimum-Phase Linear Uncertain Systems by Event-Triggered Control

Robust Output Regulation of a Class of Minimum-Phase Linear Uncertain Systems by Event-Triggered Control

Adaptive Kalman Filtering With Uncertain System Noises Based on Variance Component Estimation Theory

Adaptive Kalman Filtering With Uncertain System Noises Based on Variance Component Estimation Theory

An advanced adaptive algorithm driven by online blind noise level estimation for pedestrian positioning

An effective and universal positioning algorithm is essential for a highly reliable and accurate foot-mounted inertial pedestrian navigation system. Algorithm performance is significantly influenced by the accuracy of noise level estimation. This study introduces a novel blind noise estimation algorithm to reduce the impact of uncertain and variable system noise characteristics on positioning accuracy. The algorithm combines a quasi-population growth wavelet transform threshold with an adaptive window length adjustment strategy. Firstly, a quasi-population growth wavelet threshold function is employed to separate motion signals from noise and assess noise levels. Secondly, an adaptive sliding window strategy based on motion-noise correlation optimizing the balance between estimation precision and computational efficiency. These methods enable robust noise level estimation without prior knowledge and achieve real-time adjustments to the system noise matrix in Extended Kalman Filter of the inertial navigation system, thereby significantly enhancing navigation error suppression. The effectiveness of above methodologies has been experimentally validated.

New Adaptive Super-Twisting Extended-State Observer-Based Sliding Mode Scheme with Application to FOWT Pitch Control

This paper details the transformation of the velocity or position-tracking problem of a class of uncertain systems using finite time stability control for first-order uncertain systems. A new composite extended-state observer sliding mode (ESOSM) scheme is proposed, which includes an adaptive super-twisting-like ESO and an adaptive super-twisting controller. The adaptive super-twisting controller is implemented through a barrier function-based second-order sliding mode algorithm. To further reduce control chattering and improve control performance, the adaptive super-twisting-like ESO, which employs high-order terms in the super-twisting algorithm to accelerate convergence, is designed to observe the lumped uncertainty in real time. The advantages of the proposed scheme are verified by a numerical example and application with regard to floating offshore wind turbine (FOWT) pitch control. Compared with proportional integral (PI) and adaptive super-twisting sliding mode (ASTSM) schemes, better results are obtained in velocity tracking and fatigue load suppression. For the FOWT pitch control application, the platform roll, pitch, and yaw are decreased by 3%, 2%, and 4%, respectively, compared to the PI scheme at an average turbulent wind speed of 17 m/s and turbulence intensity of 17.27%.

A generalized Udwadia–Kalaba control design for uncertain belt conveyor systems with inequality constraints

Uncertainty can lead to jitter or overshoot in mechanical systems, necessitating the design of multiple constraints to stabilize them. This paper proposes a control structure based on the generalized Udwadia–Kalaba equation to address these constraints simultaneously. An uncertain dynamical model is developed, incorporating both equality and inequality constraints. By integrating diffeomorphism theory, a robust control strategy is designed to ensure compliance with these constraints. Utilizing the Lyapunov approach, the uniform boundedness and uniform ultimate boundedness of the dynamical system are demonstrated. Finally, the feasibility of the proposed control method is validated through its application to a belt conveyor system.

Robust adaptive control of uncertain dynamic systems using self-evolving neural learning technique

Robust adaptive control of uncertain dynamic systems using self-evolving neural learning technique