Control Of Dynamic Systems Research Articles

Modelling the unsteady lift of a pitching NACA 0018 aerofoil using state-space neural networks

The development of simple, low-order and accurate unsteady aerodynamic models represents a crucial challenge for the design optimisation and control of fluid dynamical systems. In this work, wind tunnel experiments of a pitching NACA 0018 aerofoil conducted at a Reynolds number $Re = 2.8 \times 10^5$ and at different free-stream turbulence intensities are used to identify data-driven nonlinear state-space models relating the time-varying angle of attack of the aerofoil to the lift coefficient. The proposed state-space neural network (SS-NN) modelling technique explores an innovative methodology, which brings the flexibility of artificial neural networks into a classical state-space representation and offers new insights into the construction of reduced-order unsteady aerodynamic models. The work demonstrates that this technique provides accurate predictions of the nonlinear unsteady aerodynamic loads of a pitching aerofoil for a wide variety of angle-of-attack ranges and frequencies of oscillation. Results are compared with a modified version of the Goman–Khrabrov dynamic stall model. It is shown that the SS-NN methodology outperforms the classical semi-empirical dynamic stall models in terms of accuracy, while retaining a fast evaluation time. Additionally, the proposed models are robust to noisy measurements and do not require any pre-processing of the data, thus involving only a limited user interaction. Overall, these features make the SS-NN technique an excellent candidate for the construction of accurate data-driven models from experimental fluid dynamics data, and pave the way for their adoption in applications entailing design optimisation and real-time control of systems involving lift.

Exploring Emergent Properties in Enzymatic Reaction Networks: Design and Control of Dynamic Functional Systems.

The intricate and complex features of enzymatic reaction networks (ERNs) play a key role in the emergence and sustenance of life. Constructing such networks in vitro enables stepwise build up in complexity and introduces the opportunity to control enzymatic activity using physicochemical stimuli. Rational design and modulation of network motifs enable the engineering of artificial systems with emergent functionalities. Such functional systems are useful for a variety of reasons such as creating new-to-nature dynamic materials, producing value-added chemicals, constructing metabolic modules for synthetic cells, and even enabling molecular computation. In this review, we offer insights into the chemical characteristics of ERNs while also delving into their potential applications and associated challenges.

Distributed control of partial differential equations using convolutional reinforcement learning

We present a convolutional framework which significantly reduces the complexity and thus, the computational effort for distributed reinforcement learning control of dynamical systems governed by partial differential equations (PDEs). Exploiting translational equivariances, the high-dimensional distributed control problem can be transformed into a multi-agent control problem with many identical, uncoupled agents. Furthermore, using the fact that information is transported with finite velocity in many cases, the dimension of the agents’ environment can be drastically reduced using a convolution operation over the state space of the PDE, by which we effectively tackle the curse of dimensionality otherwise present in deep reinforcement learning. In this setting, the complexity can be flexibly adjusted via the kernel width or by using a stride greater than one (meaning that we do not place an actuator at each sensor location). Moreover, scaling from smaller to larger domains – or the transfer between different domains – becomes a straightforward task requiring little effort. We demonstrate the performance of the proposed framework using several PDE examples with increasing complexity, where stabilization is achieved by training a low-dimensional deep deterministic policy gradient agent using minimal computing resources.

Physics-informed neural nets for control of dynamical systems

Physics-informed neural networks (PINNs) incorporate established physical principles into the training of deep neural networks, ensuring that they adhere to the underlying physics of the process while reducing the need for labeled data, since the desired output is not a prerequisite for physics-informed training. For modeling systems described by Ordinary Differential Equations (ODEs), traditional PINNs typically take continuous time as an input variable and produce the solution to the corresponding ODE. However, in their original form, PINNs neither accommodate control inputs nor do they effectively simulate variable long-range intervals without experiencing a significant decline in prediction accuracy. In this context, this work introduces a novel framework known as “Physics-Informed Neural Nets for Control” (PINC). PINC presents an innovative PINN-based architecture tailored to control problems, capable of simulating longer-range time horizons that are not predetermined during training. This increased flexibility sets it apart from traditional PINNs. The variable simulation time is achieved by adding inputs to the PINC network that convey the initial condition and the control signal for a particular time interval. Simulating variable long-range intervals involves running the PINC net across a sequence of shorter intervals. In this autoregressive process, the network predictions are linked in a self-feedback mode, with the initial state (input) of the next interval set to the last predicted state (network output) of the previous interval. We showcase the effectiveness of our proposal in identifying and controlling three nonlinear dynamic systems: the Van der Pol oscillator, the four-tank system, and an electric submersible pump. Crucially, these experiments demonstrate that learning the dynamics of these systems can be achieved without relying on any sample collected from the actual process, and it offers faster inference speed compared to numerical simulations.

Exponential super-twisting control for nonlinear systems with unknown polynomial perturbations

The study focuses on the control of nonlinear dynamic systems in the presence of parameter uncertainties, unmodeled dynamics, and external disturbances. The lumped perturbation is assumed to be bounded within a polynomial in the system state with the polynomial parameters and degrees unknown a priori such that it accommodates a quite wider range dynamic systems. Based on the studies in recent super-twisting algorithm designs and the idea from adaptive sliding mode control for nonlinear systems with uncertainties, we propose a novel adaptive super-twisting algorithm with exponential reaching law, or exponential super-twisting algorithm (ESTA), for the high-stability and acceptable accuracy control of the aimed nonlinear dynamics. The stability analysis and practical finite-time (PFT) convergence are proven using Lyapunov theory and an intuitive analysis of the control behaviour. Simulations are performed to compare the proposed ESTA with the existing super-twisting method and the traditional proportional integral differential control. The simulation results demonstrate the effectiveness of the proposed ESTA in terms of the fastest settling time and the smallest overshoot.

Predictor-Based Self-Organizing Control for Unknown Nonlinear Dynamical Systems

Predictor-Based Self-Organizing Control for Unknown Nonlinear Dynamical Systems

Convexified Open-Loop Stochastic Optimal Control for Linear Systems With Log-Concave Disturbances

We consider open-loop solutions to the stochastic optimal control of a linear dynamical system with an additive non-Gaussian, log-concave disturbance. We propose a novel, sampling-free approach, based on characteristic functions and convex optimization, to cast the stochastic optimal control problem as a difference-of-convex program. Our method invokes higher moments, resulting in less conservatism compared to moment-based approaches. We employ piecewise affine approximations and the convex-concave procedure for efficient solution via standard conic solvers. We demonstrate that the proposed solution is competitive with sampling and moment based approaches, without compromising probabilistic constraints.

Finite-time stochastic control for complex dynamical systems: The estimate for control time and energy consumption

Controlling complex dynamical systems has garnered significant attention within academic circles in recent decades. While most existing works have focused on closed-loop control schemes with infinite-time durations, this paper introduces a novel finite-time, closed-loop stochastic controller that considers control time, energy consumption, and their dependence on system parameters. This stochastic control technique not only enables finite-time control in chaotic dynamical systems but also facilitates finite-time synchronization in unidirectionally coupled systems. Importantly, our proposed scheme offers several advantages over existing deterministic finite-time controllers, particularly in terms of time and energy consumption. Through numerical experiments utilizing random ecosystems, neural networks, and Lorenz systems, we provide evidence of the effectiveness of our analytical results. We anticipate that this stochastic scheme will find wide-ranging applications in the control of complex dynamical systems and in achieving network synchronization.

Security-aware analytical framework : A mathematical model and machine learning for dynamical system control in secure environments

This study presents a Security-Aware Analytical Framework (SAAF) that is meant to make dynamic system control better in safe places. The framework uses a new mathematical model and advanced machine learning methods to make vital systems more resistant to possible security threats. The mathematical model gives a defined picture of how the system works and where its security holes are. This makes it possible to measure risks and come up with proactive control strategies. Using machine learning techniques, the system changes with changing danger scenarios, allowing for identification and reaction to threats in real time. A risk evaluation tool, a dynamic danger prediction model, and an adaptable control system are some of the most important parts of the SAAF. The risk assessment tool checks for weaknesses in the system, and the dynamic threat prediction model uses machine learning to guess when security might be broken. These guesses are used by the adaptive control method to change system settings on the fly, which improves security without lowering working efficiency. The suggested framework works well by being simulated in a number of safe settings. These settings show how it can reduce security risks and make sure that dynamic systems are strong even as threats change. This study helps to improve methods that focus on security for protecting key assets.

Prescribed-Time Optimal Control of Nonlinear Dynamical Systems With Application to a Coupled Tank System

Prescribed-Time Optimal Control of Nonlinear Dynamical Systems With Application to a Coupled Tank System

Secure Bipartite Consensus Control for Dynamic Event-Triggered Multi-Agent Systems Based on Co-Estimation of State and Attacks

Secure Bipartite Consensus Control for Dynamic Event-Triggered Multi-Agent Systems Based on Co-Estimation of State and Attacks

Comparison of single and multi-coil self-powered MR damper subjected to cyclic and earthquake loading for structural vibration control

A promising solution for semi-active vibration control of different dynamic systems is to use magnetorheological (MR) dampers. In the present study, the investigation focuses on developing a novel single and multi-coil self-powered magnetorheological (MR) damper system using electromagnetic induction (EMI) for seismic mitigation. The conventional MR dampers, which rely on external power sources, can be unreliable and impractical in earthquake-prone locations. Thus, the EMI device connected to the MR damper can be used as an effective and alternative power source for the MR damper, results in a self-powered system. The proposed energy-harvesting system is an MR damper placed above the top of the piston. The coil is wound around the piston, and the outer casing with a ferrite magnet is fixed. As the mechanical energy of the piston is converted into electrical energy, its self-tuning capacity is a perfect fit for structural vibration control applications. The MR damper is subjected to cyclic and time-history loading. At a maximum amplitude of 15 mm, the damper generated 1767.8 N for cyclic loading and -1780 N for earthquake loading, the El Centro earthquake 1940 is considered for the study. By placing EMI, the mechanical energy is converted into electrical energy and powers the damper to avoid external power. The experimental results showcase enhanced damping forces and adaptability, thereby establishing it as an innovative and effective it can be alternative to conventional MR dampers for vibration control in future

Synthesis of an Optimal Control for Linear Stationary Discrete Dynamical Systems

Synthesis of an Optimal Control for Linear Stationary Discrete Dynamical Systems

UKF-Based Optimal Tracking Control for Uncertain Dynamic Systems With Asymmetric Input Constraints.

To enhance system robustness in the face of uncertainty and achieve adaptive optimization of control strategies, a novel algorithm based on the unscented Kalman filter (UKF) is developed. This algorithm addresses the finite-horizon optimal tracking control problem (FHOTCP) for nonlinear discrete-time (DT) systems with uncertainty and asymmetric input constraints. An augmented system is constructed with asymmetric control constraints being considered. The augmented problem is addressed with a DT Hamilton-Jacobi-Bellman equation (DTHJBE). By analyzing convergence with regard to the cost function and control law, the UKF-based iterative adaptive dynamic programming (ADP) algorithm is proposed. This algorithm approximates the solution of the DTHJBE, ensuring that the cost function converges to its optimal value within a bounded range. To execute the UKF-based iterative ADP algorithm, the actor-estimator-critic framework is built, in which the estimator refers to system state estimation through the application of UKF. Ultimately, simulation examples are presented to show the performance of the proposed method.

Comparison of Reinforcement Learning and Model Predictive Control for Automated Generation of Optimal Control for Dynamic Systems within a Design Space Exploration Framework

Comparison of Reinforcement Learning and Model Predictive Control for Automated Generation of Optimal Control for Dynamic Systems within a Design Space Exploration Framework

Optimal Control Approach To Non-Linear Robust Control System With Two Uncertainties And Matching Condition

In this paper, we introduce the robust control problem (RCP) of non-linear uncertain systems with the matching condition. A relationship has been developed between the robustness with perturbations (uncertainties) and the optimality condition of the current control problem. A computational algorithm for resilient control of nonlinear dynamic systems is proposed, as well as its equivalence to a specific optimal control problem (OCP.

Command filter‐based finite‐time adaptive tracking control for MIMO dynamic systems with external disturbances and prescribed performance constraints

AbstractThis paper investigates command filter‐based finite‐time stability of multi‐input multi‐output (MIMO) dynamic systems with prescribed performance constraints and external disturbances. A novel finite‐time differentiator is introduced into command filter‐based control scheme, which improves transient performance of each subsystem. Meanwhile, disturbance observers are utilized to eliminate negative effects on control system caused by external disturbances. Furthermore, featured with a selected performance function, it can be guaranteed that tracking errors remain in prescribed performance region. Stability analysis of the proposed controller is presented by using a Lyapunov function including transformed filter errors, parameter errors of neural networks, and observed errors of lumped disturbances. Effectiveness of proposed control method is verified by a numerical example and a practical system of inverted pendulums, respectively.

NON-MINIMUM PHASE SYSTEMS

The aim of this paper is to create a mathematical model of a manipulator of a dynamic system with a non-minimum phase. The first introductory part of the paper deals with the actual problem of manipulator control. The second part deals with the control of dynamic systems and the formulation of the control problem. The third and last section deals with the construction of the mathematical model of the manipulator.

A Deep Learning Approach to Improve the Control of Dynamic Wireless Power Transfer Systems

In this paper, an innovative approach for the fast estimation of the mutual inductance between transmitting and receiving coils for Dynamic Wireless Power Transfer Systems (DWPTSs) is implemented. To this end, a Convolutional Neural Network (CNN) is used; an image representing the geometry of two coils that are partially misaligned is the input of the CNN, while the output is the corresponding inductance value. Finite Element Analyses are used for the computation of the inductance values needed for CNN training. This way, thanks to a fast and accurate inductance estimated by the CNN, it is possible to properly manage the power converter devoted to charge the battery, avoiding the wind up of its controller when it attempts to transfer power in poor coupling conditions.

A Shared Control Approach Based on First-Order Dynamical Systems and Closed-Loop Variable Stiffness Control

In this paper, we present a novel learning-based shared control framework. This framework deploys first-order Dynamical Systems (DS) as motion generators providing the desired reference motion, and a Variable Stiffness Dynamical Systems (VSDS) (Chen et al. 2021) for haptic guidance. We show how to shape several features of our controller in order to achieve authority allocation, local motion refinement, in addition to the inherent ability of the controller to automatically synchronize with the human state during joint task execution. We validate our approach in a teleoperated task scenario, where we also showcase the ability of our framework to deal with situations that require updating task knowledge due to possible changes in the task scenario, or changes in the environment. Finally, we conduct a user study to compare the performance of our VSDS controller for guidance generation to two state-of-the-art controllers in a target reaching task. The result shows that our VSDS controller has the highest successful rate of task execution among all conditions. Besides, our VSDS controller helps reduce the execution time and task load significantly, and was selected as the most favorable controller by participants.