Systems In Presence Of Disturbances Research Articles

Scalable exact output synchronization of discrete‐time multi‐agent systems in the presence of disturbances and measurement noise with known frequencies

AbstractThis article aims to achieve scalable exact output and regulated output synchronization for discrete‐time multi‐agent systems in presence of disturbances and measurement noise with known frequencies. Both homogeneous and heterogeneous multi‐agent systems are considered, with parts of agents' states accessible in the latter case. The key contribution of this article is to establish scalable exact synchronization results under disturbances and measurement noise, which is achieved by using distributed protocols that only use information about agent models and no information about the communication network or the number of agents. The validity of the protocol is verified by numerical simulations with arbitrarily chosen number of agents.

Model-Based Sensor Fault Detection and Diagnosis in Closed-Loop Power Converters for Electric Vehicles

Electric Vehicle (EV) involves electric motors equipped with closed loop powered conversion systems which use the energy from storage batteries for vehicle propulsion and rely on sensor measurements as feedback to the controller. Therefore, sensor fault detection and diagnosis are an essential process that ensures performance and safety of EVs. From the National Renewable Energy Laboratory (NREL) study it is confirmed that electrical vehicle’s fault percentages vary, however, sensor faults constitute approximately 20-30% of total faults in them. Thus, sensor fault detection is critical for effective EV operation and requires the utmost focus from EV manufacturers and operators. Model-based techniques and Kalman Filter (KF) make it possible to detect faults on sensors in closed-loop EV power converter. It involves the use of mathematical model, sensor data and KF to identify fault before it becomes a threat and guarantees proper function of the vehicle. In this article, a state-space model of a CUK converter is developed, and an LQR closed-loop control system is designed for the power converter. A sensor fault detection method was developed for the system which is then simulated and tested under sensor bias fault conditions for both open and closed-loops control for determining the difference between them. The results showed a distinctive feature extracted between these two systems in the presence of system disturbances and noise applied to the sensor readings. The second section of simulation results in a closed-loop configuration validated the mathematical equations used for sensor fault detection, demonstrating their resistance to false alarms caused by system or component faults in addition to its ability to diagnose faults.

Synthesis of Output-Feedback Controllers for Mixed Traffic Systems in Presence of Disturbances and Uncertainties

In this paper, we study mixed traffic systems that move along a single-lane ring-road or open-road. The traffic flow forms a platoon, which includes a number of heterogeneous human-driven vehicles (HDVs) together with only one connected and automated vehicle (CAV) that receives information from a subset of neighbors. The dynamics of HDVs are assumed to follow a general continuous-time nonlinear car-following model, which is a function of their velocity, spacing, and the relative velocity. The acceleration of the single CAV is also directly controlled by a dynamical output-feedback controller. The ultimate goal of this work is to present a robust control strategy that can smoothen the traffic flow in the presence of undesired disturbances (e.g. abrupt deceleration) and parametric uncertainties. A prerequisite for synthesizing a dynamical output controller is the stabilizability and detectability of the underlying system. Accordingly, a theoretical analysis is presented first to prove the stabilizability and detectability of the mixed traffic flow system. Then, two <inline-formula> <tex-math notation="LaTeX">$H_\infty$</tex-math> </inline-formula> control strategies, with and without considering uncertainties in the system dynamics, are designed. The efficiency of the two control methods is subsequently illustrated through numerical simulations, and various experimental results are presented to demonstrate the effectiveness of the proposed controller to mitigate disturbance amplification and achieve platoon stability.

Event-Triggered Optimal Consensus of Second-Order MASs With Disturbances and Cyber Attacks on Communications Edges

This paper investigates the distributed optimal consensus problem (DOCP) for second-order multi-agent systems (MASs) in the presence of system disturbances and cyber attacks on communication edges. In optimization, the global objective function is the sum of a class of local private objective functions, which are assumed to be strongly convex and continuously differentiable with Lipschitz continuous gradients. Unlike most optimization problems for MASs studied in the literature, in this study, the dynamics of agents are second-order and disturbed, considering cyber attacks among neighboring agents. In order to achieve DOC with low online resource consumption, novel distributed event-triggered controllers are designed for agents. Not only the communication and computation resources can be saved, but also the frequency of controller updates can be reduced. Moreover, the controllers can prevent the information of the states of second-order MASs from leaking to neighbors. Under restrictions on cyber attacks, sufficient conditions are derived, under which all controlled agents can achieve consensus on the optimal solution of the global objective function exponentially. Furthermore, the Zeno-behavior is excluded on triggering time sequences. Finally, three simulation examples are shown to illustrate the effectiveness of the designed controllers in saving online resources.

Extended state observer based current-constrained controller for a PMSM system in presence of disturbances: Design, analysis and experiments

Considering overcurrent and total disturbances, i.e. measurement noise, parameter uncertainties, external load torque disturbance etc., a composite current-constrained control method based on non-cascade single-loop speed regulation control structure is adopted for permanent magnet synchronous motor system (PMSMs). First, a tracking state space model is proposed to transfer the matched and mismatched disturbances into a uniform matched disturbance. Second, a third-order extended state observer (ESO) is developed to satisfactorily attenuate the effect of the total disturbances with their estimation values via feedforward compensation, since a tracking differentiator (TD) is designed to arrange the transition process for the reference input signal to balance the contradiction between overshoot and quick response. Third, a composite ESO-based current-constrained controller with a penalty mechanism of q-axis current, constructed by a special nonlinear gain, is designed to deal with the overcurrent of the PMSMs speed regulation. In addition, a finite time bounded stability theory is introduced to analyze the proposed closed-loop speed regulation system. A bench mark experimental set-up based on NI CompactRIO 9045 and LabVIEW for a PMSMs is designed to verify and compare the performances of the proposed composite controller against conventional PID controller. Finally, the simulation and experiment results demonstrate the effectiveness and superiority of the proposed controller.

Incremental adaptive optimal control for nonlinear systems with disturbance and input time-delay

In this paper, a model-free incremental adaptive optimal control scheme is proposed for nonlinear systems in presence of disturbance and input time-delay. The linear time-varying approximate model of the nonlinear system is obtained by incremental method, in which the relevant matrix parameters are identified by recursive least squares (RLS) estimation. A time-delay matrix function is constructed by neural network (NN) to eliminate the input time-delay, and the external disturbance of nonlinear system is dealt with by [Formula: see text] optimal control; incremental adaptive dynamic programming (IADP) algorithm is used to get the control law by solving Hamilton–Jacobi–Isaacs (HJI) equation. Convergence analysis of the proposed control scheme is provided. Simulations are given to verify the effectiveness of the proposed control scheme.

Managing energy-water-carbon-food nexus for cleaner agricultural greenhouse production: A control system approach

Poverty, food insecurity and climate change are global issues facing humanity, threatening social, economic and environmental sustainability. Greenhouse cultivation provides a potential solution to these challenges. However, some greenhouses operate inefficiently and need to be optimized for more economical and cleaner crop production. In this paper, an economic model predictive control (EMPC) method for a greenhouse is proposed. The goal is to manage the energy-water‑carbon-food nexus for cleaner production and sustainable development. First, an optimization model that minimizes the greenhouse's operating costs, including costs associated with greenhouse heating/cooling, ventilation, irrigation, carbon dioxide (CO2) supply and carbon emissions taking into account both the CO2 equivalent (CO2-eq) emissions caused by electrical energy consumption and the negative emissions caused by crop photosynthesis, is developed and solved. Then, a sensitivity analysis is carried out to study the impact of electricity price, supplied CO2 price and social cost of carbon (SCC) on the optimization results. Finally, a model predictive control (MPC) controller is designed to track the optimal temperature, relative humidity, CO2 concentration and incoming radiation power in presence of system disturbances. Simulation results show that the proposed approach increases the operating costs by R186 (R denotes the South African currency, Rand) but reduces the total cost by R827 and the carbon emissions by 1.16 tons when compared with a baseline method that minimizes operating costs only. The total cost is more sensitive to changes in SCC than that in electricity price and supplied CO2 price. The MPC controller has good tracking performance under different levels of system disturbances. Greenhouse environmental factors are kept within specified ranges suitable for crop growth, which increases crop yields. This study can provide effective guidance for growers' decision-making to achieve sustainable development goals.

Lyapunov-Bregman functions for speed-gradient adaptive control of nonlinear time-varying systems

The paper is devoted to application of Bregman divergence to nonlinear adaptive control. Using Bregman divergence instead of quadratic function leads to a new class of Lyapunov functions in adaptive control that can be called Lyapunov-Bregman functions. An extended control algorithms design scheme is proposed and the properties of the designed control systems in presence of disturbances and time-varying parameters are studied. A special case of model reference adaptive control for nonlinear time-varying systems is studied based on the speed-gradient methodology.

Bio-Inspired Feedback Linearized Adaptive Control For a Thrust Vectoring Free-Flyer Vehicle

Intelligent unmanned robotic systems have recently gained popularity due to their ability to potentially explore inaccessible and dynamically changing environments. In these environments, these vehicles might be subjected to unique types of disturbances that may lead to mission performance degradation. This paper describes the design, development and proof of concept of a novel adaptive control that combines concepts from model reference and feedback linearization and it is augmented via nonlinear bounded functions typical in immune system responses of living organisms. Proof of stability of the proposed control law using Circle Criterion is presented. Numerical hardware in the loop simulations along with actual implementation are performed using a gimbaled mini-free flyer vehicle that uses thrust vectoring control actuation. A set of performance index metrics are used to quantify and assess the performance of the adaptive control system which shows stabilizing capabilities in the presence of system disturbances and uncertainties.

Discrete-Time State-Dependent Proportional-Integral Control for Torque Tracking of Hydrostatic Transmissions

This paper presents both design and implementation of a discrete-time proportional-integral (PI) tracking control for the desired output torque of a nonlinear hydrostatic transmission system affected by disturbances and uncertainties. The control is conceived in a decentralized form, in which the bent-axis angle and the torque of the hydraulic motor are controlled separately. The motor bent-axis angle is adjusted by a pure feedforward control law, whereas the torque of the hydraulic motor is controlled using a PI state feedback from a online-solution of the state-dependent Riccati equation. State variables and external disturbances are reconstructed by a discrete-time nonlinear observer. The stability of the closed-loop system as well as the observer are investigated by linear matrix inequalities. The achieved tracking performance indicates the robustness of the overall control structure in the presence of system disturbances and uncertainties. The proposed control is evaluated by means of simulations and experiments using the dedicated test rig at the Chair of Mechatronics, University of Rostock.

A novel model predictive control strategy for constrained and unconstrained systems in presence of disturbance

A novel model predictive control strategy for constrained and unconstrained systems in presence of disturbance

Robust Speed Control for Permanent Magnet Synchronous Motors Using a Generalized Predictive Controller With a High-Order Terminal Sliding-Mode Observer

This paper reports the optimal speed control of a permanent magnet synchronous motor (PMSM) system. The predictive control method is an effective strategy for a fast dynamic response. The undesirable performance in the presence of system disturbances, including internal model uncertainties and external load disturbances, is analyzed. To achieve a fast response and ensure stronger robustness and improved disturbance rejection performance simultaneously, a robust generalized predictive controller (GPC) with a high-order terminal sliding-mode observer (HOTSMO) is proposed for a PMSM control system. The proposed observer can estimate the unknown disturbances with chattering elimination. A feed-forward compensation based on the estimated disturbances is provided to the predictive speed controller. The simulation and experimental results show that the proposed control method can achieve a better speed dynamic response and a stronger robustness.

Nonlinear PID actuator speed control in inverter based electro-hydraulic systems subjected to external leakage

ABSTRACT Actuators of constant speed operation in hydraulic power systems are highly required in many applications such as steel sheet rolling and pressing operations. To achieve this, a variable pump and/or control valve may be used to control the actuator speed in different operating conditions. A mathematical model of the selected hydraulic system and its components has been carried out using SIMULINK to study its static and dynamic characteristics. A direct comparison between the mathematical model results and experimental results of the same hydraulic system published by the author has been introduced. For the experimental and the mathematical models, a linear and nonlinear PID control algorithm has been applied for pump speed control and proportional directional control valve in the presence of system disturbance, herein is an external leakage, to maintain the actuator velocity constant either by controlling the pump speed or the proportional directional control valve with achieving the minimum error. The nonlinear control PID showed a better system response than the PID controller and reduced the error hence improved the system response. It has been shown that the proportional valve control is better than the pump speed control for different external leakage, 5–10%, with an error of the order 3% from the setting velocity.

Advanced Control Of Plasma Medical Devices

Atmospheric pressure plasma jets (APPJs) have widespread use in plasma medicine. This presentation aims to demonstrate the importance of using advanced control strategies for safe, reproducible, and therapeutically effective application of APPJs for dose delivery to a target substrate. Key challenges in advanced control of APPJs arise from: (i) the multivariable, nonlinear nature of system dynamics, (ii) the need to constrain the system operation within an operating region that ensures safe plasma treatment, and (iii) the cumulative, non-decreasing nature of dose metrics. To systematically address these challenges, we propose a model predictive control (MPC) strategy for real-time control of a radiofrequency APPJ in argon. To this end, a lumped-parameter, physics-based model is developed for describing the jet dynamics, and cumulative dose metrics are defined for quantifying the thermal and non-thermal energy effects of the plasma on substrate. [1] The closed-loop performance of the MPC strategy is compared to that of basic proportional-integral control. Simulation results indicate that MPC provides a versatile framework for dose delivery in the presence of system disturbances, while fulfilling the safety and practical constraints of APPJ operation. In addition, we demonstrate the use of advanced control in experimental APPJ systems. Advanced control can lead to unprecedented opportunities for effective dose delivery in plasma medicine.

Robust Periodic Economic Model Predictive Control using Probabilistic Set Invariance for Descriptor Systems

Abstract This paper proposes a robust periodic economic model predictive control (MPC) using probabilistic set invariance for discrete-time descriptor systems. The considered systems are affected by additive disturbances that are bounded and follow a zero-mean Gaussian distribution. To guarantee robustness of the proposed robust economic MPC, a probabilistic invariant set with a given probability is used to contract the state and input constraints. An auxiliary input variable is defined to satisfy algebraic equations from descriptor systems in presence of disturbances. Finally, an application of the proposed MPC strategy to a smart micro-grid is provided to illustrate the performance.

Robust Periodic Economic Predictive Control based on Interval Arithmetic for Water Distribution Networks

This paper addresses a robust periodic economic model predictive control (EMPC) based on interval arithmetic with unknown-but-bounded additive disturbances for the management of water distribution networks (WDNs). The system constraints in presence of system disturbances are tightened along the prediction horizon by means of the proposed interval arithmetic and considering that system variables in the WDN model are also subject to some algebraic equations. These algebraic equations should be satisfied when the disturbances have effects on those system variables. The EMPC controller is designed with adding the terminal state constraint. The periodically optimal steady states are obtained by employing a periodic EMPC planner with the nominal model. This periodic steady states are subsequently used to be terminal states. The on-line robust constraint satisfactions are implemented into the robust periodic EMPC controller. Finally, the proposed control strategy is verified using a case study.

Reconfigurable adaptive fuzzy fault-hiding control for greenhouse climate control system

Modern greenhouses are equipped with various components for providing a comfortable climate for plant growth. A greenhouse component malfunction may significantly crumble the greenhouse performance and hence result in loss of production. Therefore, it is required to design a control system, which is stable, and is able to provide an acceptable performance even in a severe fault situation. In this paper, an active fault-tolerant control scheme to compensate for actuator and/or sensor faults in the greenhouse climate system is designed. The proposed control system consists of a self-tuned fuzzy PI control system, a sensitive and reliable observer-based fault detection and diagnosis (FDD) mechanism for different types of faults in the presence of system disturbances, and a robust fault-hiding-based reconfigurable control design able to compensate for faulty effects. In this approach, a set of virtual sensors and actuators are used to restore the closed-loop stability and to ensure accurate tracking of reference inputs. The efficiency of the proposed approach has been demonstrated through a set of simulation experiments on a nonlinear multi-input multi-output greenhouse climate system.

ToMFIR-based Detection and Estimation for Incipient Actuator Faults in a class of Closed-Loop Nonlinear Systems

Since incipient faults often have small amplitudes and obscure symptoms, those observer-based or output residual-based fault indicators are ineffective for incipient faults, especially in the presence of system disturbances. The ToMFIR-based fault detection approaches use both controller and output residuals to construct a fault indicator, which make them sensitive to incipient faults. This paper extends the ToMFIR-based fault detection approaches to a class of closed-loop nonlinear systems under the consideration of system disturbances. The restriction on the fault type is removed compared with the existing approaches, and ToMFIR-based fault estimation algorithm has been derived. Verification results in a near space hypersonic vehicle (NSHV) simulation system can demonstrate the effectiveness of the proposed approach.

Seeker Optimization Algorithm based Load Frequency Control of Interconnected Power Systems

paper, a Seeker Optimization Algorithm (SOA) based optimal proportional-plus-integral (PI) controller is designed for Load Frequency Control (LFC) of two area interconnected thermal power systems. Swarm intelligence algorithms have been successfully applied to solve optimization problems, Seeker optimization algorithm is one of the latest members of that class. Seeker optimization algorithm (SOA) is based on simulating the act of human searching, which has been shown to be a promising candidate among search algorithms for function optimization. The effectiveness of the proposed scheme is confirmed via extensive study using MATLAB/SIMULINK software. Simulation results are carried out by introducing 1% load disturbance in any one area of an interconnected power system. Comparison of performance responses of conventional Proportional-Integral (PI) controller with Proportional-Integral controller using Seeker Optimization Algorithm show that the Seeker Optimization Algorithm tuned controller has better dynamic response in the presence of system disturbances. to the power systems and variable operating conditions, the conventional methods for tuning of PI controller may be unsuitable. Many different control strategies have been suggested in literature based on intelligent/soft computing techniques (3). Seeker Optimization Algorithm is based on human understanding and searching capability for finding an optimum solution. In the SOA, optimum solution is regarded as one which is searched out by a seeker population. The underlying concept of the Seeker Optimization Algorithm is very easy to model and relatively easier than other optimization techniques. In this study, Seeker Optimization algorithm is used to optimize the parameters of optimal PI controller gains.

A neural network-based sliding-mode control for rotating stall and surge in axial compressors

A decoupled sliding-mode neural network variable-bound control system (DSMNNVB) is proposed to control rotating stall and surge in jet engine compression systems in presence of disturbance and uncertainty. The control objective is to drive the system state to the original equilibrium point and it proves that the control system is asymptotically stable. In this controller, an adaptive neural network (NN) control scheme is employed for unknown dynamic of nonlinear plant without using a model of the plant. Moreover, no prior knowledge of the plant is assumed. The proposed DSMNNVB controller ensures Lyapunov stability of the nonlinear dynamic of the system.