Full State Measurements Research Articles

Observer-based Economic Model Predictive Control for Direct Contact Membrane Distillation

In this work, we present an Observer-based Economic Model Predictive Control (OEMPC) scheme for Direct Contact Membrane Distillation (DCMD). To compute control actions via MPC strategy, full knowledge of the system states is necessary to predict the future evolution of the system. However, the full state measurement for DMCD is either difficult or costly. In this work, an integrated framework of an observer design and EMPC paradigm is proposed for the DMCD process. Numerical simulations are presented to illustrate the performance of the proposed observer design. Closed-loop simulations show that the proposed OEMPC scheme was able to operate the process economically while satisfying process and input constraints.

Approximation-Based Event-Triggered Control Against Unknown Injection Data in Full States and Actuator of Uncertain Lower-Triangular Nonlinear Systems

This paper addresses an approximation-based adaptive event-triggered control problem against unknown injection data in full state measurements and an actuator of systems with unknown strict-feedback nonlinearities. It is assumed that full state variables measured for state-feedback control are corrupted by unknown injection data that denote cyber attacks or fault signals, and all system nonlinearities are unknown. Owing to the corrupted state feedback information, error surfaces using exactly measured state variables become unknown during the recursive control design procedure for strict-feedback nonlinear systems. Thus, they cannot be used to implement the adaptive event-triggered controller. To address this problem, an approximation-based adaptive recursive event-triggered control design using the corrupted state variables is established to ensure that error surfaces using exactly measured state variables converge to an adjustable neighborhood of the origin in the Lyapunov sense. The adaptive controller and its event-triggering law using corrupted states are designed under uncertain injection data where the adaptive injection data compensators using the neural networks are constructed to deal with the unknown injection data effects. The stability of the closed-loop systems and the exclusion of Zeno behavior are analyzed.

Adaptive Pitch Control of Variable-Pitch PMSG Based Wind Turbine

This paper presents an adaptive pitch-angle control approach for a permanent magnet-synchronous generator-based wind turbine (PMSG-WT) connecting with a power grid to limit extracted power above the rated wind speed. In the proposed control approach, a designed perturbation observer is employed for estimating and compensating unknown parameter uncertainties, system nonlinearities, and unknown disturbances. The proposed control approach does not require full state measurements or the accurate system model. Simulation tests verify the effectiveness of the proposed control approach. The simulation results demonstrate that compared with the feedback linearizing controller, conventional vector controller with proportional-integral (PI) loops, and PI controller with gain scheduling, the proposed control approach can always maintain the extracted wind power around rated power, and has higher performance and robustness against disturbance and parameter uncertainties.

Fault Ride-Through Capability Enhancement of Type-4 WECS in Offshore Wind Farm via Nonlinear Adaptive Control of VSC-HVDC

This paper proposes a perturbation estimation-based nonlinear adaptive control (NAC) for a voltage-source converter-based high voltage direct current (VSC-HVDC) system which is applied to interconnect offshore large-scale wind farms to the onshore main grid in order to enhance the fault ride-through (FRT) capability of Type-4 wind energy conversion systems (WECS). The VSC-HVDC power transmission system is regraded as a favourable solution for interconnecting offshore wind farms. To improve the FRT capability of offshore power plants, a de-loading strategy is investigated with novel advanced control of the VSC-HVDC systems. The proposed NAC does not require an accurate and precise model and full state measurements since the combinatorial effects of nonlinearities, system parameter uncertainties, and external disturbances are aggregated into a perturbation term, which are estimated by a high-gain perturbation observer (HGPO) and fully compensated for. As the proposed NAC is adaptive to system model uncertainties (e.g., mismatched output impedance of the converters and the line impedance of transmission line), time-varying disturbance (e.g., AC grid voltage sags and line to ground faults), and unknown time-varying nonlinearities of the power-electronic system (e.g., unmodelled dynamics existed in valve and VSC phase-locked loop system), a significant robustness can be provided by the de-loading strategy to enhance the FRT capability. Simulation results illustrated that the proposed strategy can provide improved dynamic performance in the case of operation with a variety of reduced voltage levels and improved robustness against model uncertainties and mismatched system parameters comparing with conventional vector control.

Bounded bilinear control of coupled first-order hyperbolic PDE and infinite dimensional ODE in the framework of PDEs with memory

In this work, we consider the problem of bounded bilinear tracking control of a system of coupled first-order hyperbolic partial differential equation (PDE) with an infinite dimensional ordinary differential equation (ODE). This coupled PDE-infinite ODE system can be viewed as a degenerate system of two coupled first-order hyperbolic PDEs, the velocity of the ODE part vanishing. First, we convert this PDE-infinite ODE system into a first-order hyperbolic PDE with memory and investigate the bounded bilinear control problem in this framework. We consider as manipulated variable the constrained wave propagation velocity, which makes the control problem bounded and bilinear, and we take the measurements at the boundaries. To account for the actuator's constraints, we develop conditions under which the bounded control law ensures stability and tracking performances. This leads to a specification of the state-space region that enforces the desired system's closed-loop behaviour. To overcome the lack of full-state measurements, we design an observer-based bounded output-feedback control law which guarantees the reference tracking and uniform asymptotic stability of the system in closed-loop. A strong motivation of our work is the control problem of the solar collector parabolic trough where the manipulated control variable (the pump volumetric flow rate) is bilinear with respect to the PDE-infinite ODE model, and the measurements are taken at the boundary (tube's outlet). Simulation results illustrate the efficiency of the proposed control strategy.

Control of multi-agent systems with input delay via PDE-based method

This paper deals with the control of collective dynamics of a large scale multi-agent system (MAS) moving in a 3-D space under the occurrence of arbitrarily large boundary input delay. The collective dynamics is described by a pair of reaction–advection–diffusion partial differential equations (PDEs) consisting of a complex-valued state whose real-part and imaginary-part denote the position coordinates x and y, respectively, and a real-valued state equation governing the evolution of the collective dynamics in the z coordinate. A 2-D cylindrical surface represents the indexes in a continuum and defines the topology where the agents communicate with each other based on a leader–follower strategy. The agents located at the boundary are chosen as the leaders and are subject to input delays that are practically induced by communication, measurement or even actuator constraints. A boundary control, which compensates the effect of the delay and drives all the agents to the desired formation is designed based on PDE-backstepping method. Here, the 2-D cylindrical coordinate leads to the existence of singular points in the gain kernels, which makes the stability analysis non-trivial in comparison to the 1-D problem. The proposed controller ensures the exponential stability of the MAS in H2 norm under full-state measurement and transitions from one formation to another are achievable as illustrated by the simulation results.

Distributed Fault-Tolerant Control of Multiagent Systems: An Adaptive Learning Approach.

This paper focuses on developing a distributed leader-following fault-tolerant tracking control scheme for a class of high-order nonlinear uncertain multiagent systems. Neural network-based adaptive learning algorithms are developed to learn unknown fault functions, guaranteeing the system stability and cooperative tracking even in the presence of multiple simultaneous process and actuator faults in the distributed agents. The time-varying leader's command is only communicated to a small portion of follower agents through directed links, and each follower agent exchanges local measurement information only with its neighbors through a bidirectional but asymmetric topology. Adaptive fault-tolerant algorithms are developed for two cases, i.e., with full-state measurement and with only limited output measurement, respectively. Under certain assumptions, the closed-loop stability and asymptotic leader-follower tracking properties are rigorously established.

Data-driven adaptive dynamic programming for partially observable nonzero-sum games via Q-learning method

ABSTRACTThis paper concerns with a class of discrete-time linear nonzero-sum games with the partially observable system state. As is known, the optimal control policy for the nonzero-sum games relies on the full state measurement which is hard to fulfil in partially observable environment. Moreover, to achieve the optimal control, one needs to know the accurate system model. To overcome these deficiencies, this paper develops a data-driven adaptive dynamic programming method via Q-learning method using measurable input/output data without any system knowledge. First, the representation of the unmeasurable inner system state is built using historical input/output data. Then, based on the representation state, a Q-function-based policy iteration approach with convergence analysis is introduced to approximate the optimal control policy iteratively. A neural network (NN)-based actor-critic framework is applied to implement the developed data-driven approach. Finally, two simulation examples are provided to demonstrate the effectiveness of the developed approach.

Adaptive Stabilization of Uncertain Cortex Dynamics Under Joint Estimates and Input Constraints

Recently, an approximate mathematical model of the cortex dynamics was introduced for treatment of the epilepsy, Parkinson’s, etc., where it is found applicable in real-time applications. However, designed control methods for treatments are based on the assumption of the exact mathematical model of the cortex and full state measurements are also available. In fact, the mathematical model is not exact, full state measurements may not always possible, and the measurements are always noisy. Therefore, in this brief, an adaptive unscented Kalman filter (UKF) based optimal controller is proposed to control of uncertain cortex dynamics with a single membrane potential measurement. The unmeasurable states and uncertainty function of the dynamics are estimated using adaptive UKF with noisy output measurement that are used in the production of control signal. For the security of treatments, a constrained input signal is also proposed to apply. In numerical applications, the epileptic state of the cortex dynamics is handled and accurately stabilized to a normal state with a constrained input signal. The estimation and control results are presented for future applications of uncertain and unmeasurable cortex dynamics.

High Gain Observer-Based Saturated Controller for Feedback Linearizable System

The presence of pulse width modulation driven components, limiters, and electronic switches constrain the magnitude of control input in electrical and electro-mechanical systems. The presence of nonlinearities in these system models and unavailability of full state measurements, further complicate the controller design process. This brief introduces a novel saturated output feedback design to solve the tracking problem for a class of feedback linearizable circuits. The feedback scheme comprises of a modified approximate dynamic inversion controller in conjunction with a standard high gain observer. Unlike the Lyapunov-based approaches, the proposed contraction theory-based methodology details a quantitative framework for the convergence analysis of closed loop trajectories. This, in turn, serve the dual purpose of proving ultimate boundedness and quantifying the convergence bounds in terms of known parameters.

Experimental Study of a Command Governor Adaptive Depth Controller for an Unmanned Underwater Vehicle

Unmanned Underwater Vehicles (UUVs) are increasingly being used in advanced applications that require them to operate in tandem with human divers and around underwater infrastructure and other vehicles. These applications require precise control of the UUVs which is challenging due to the non-linear and time varying nature of the hydrodynamic forces, presence of external disturbances, uncertainties and unexpected changes that can occur within the UUV’s operating environment. Adaptive control has been identified as a promising solution to achieve desired control within such dynamic environments. Nevertheless, adaptive control in its basic form, such as Model Reference Adaptive Control (MRAC) has a trade-off between the adaptation rate and transient performance. Even though, higher adaptation rates produce better performance they can lead to instabilities and actuator fatigue due to high frequency oscillations in the control signal. Command Governor Adaptive Control (CGAC) is a possible solution to achieve better transient performance at low adaptation rates. In this study CGAC has been experimentally validated for depth control of a UUV, which is a unique challenge due to the unavailability of full state measurement and a greater thrust requirement. These in turn leads to additional noise from state estimation, time-delays from input noise filters, higher energy expenditure and susceptibility to saturation. Experimental results show that CGAC is more robust against noise and time-delays and has lower energy expenditure and thruster saturation. In addition, CGAC offers better tracking, disturbance rejection and tolerance to partial thruster failure compared to the MRAC.

Discovery of Nonlinear Multiscale Systems: Sampling Strategies and Embeddings

A major challenge in the study of dynamical systems is that of model discovery: turning data into models that are not just predictive, but provide insight into the nature of the underlying dynamical system that generated the data. This problem is made more difficult by the fact that many systems of interest exhibit diverse behaviors across multiple time scales. We introduce a number of data-driven strategies for discovering nonlinear multiscale dynamical systems and their embeddings from data. We consider two canonical cases: (i) systems for which we have full measurements of the governing variables, and (ii) systems for which we have incomplete measurements. For systems with full state measurements, we show that the recent sparse identification of nonlinear dynamical systems (SINDy) method can discover governing equations with relatively little data and introduce a sampling method that allows SINDy to scale efficiently to problems with multiple time scales. Specifically, we can discover distinct governing equations at slow and fast scales. For systems with incomplete observations, we show that the Hankel alternative view of Koopman (HAVOK) method, based on time-delay embedding coordinates, can be used to obtain a linear model and Koopman invariant measurement system that nearly perfectly captures the dynamics of nonlinear quasiperiodic systems. We introduce two strategies for using HAVOK on systems with multiple time scales. Together, our approaches provide a suite of mathematical strategies for reducing the data required to discover and model nonlinear multiscale systems.

Robust nonlinear control of atomic force microscope via immersion and invariance

SummaryThis paper reports an immersion and invariance (I&I)–based robust nonlinear controller for atomic force microscope (AFM) applications. The AFM dynamics is prone to chaos, which, in practice, leads to performance degradation and inaccurate measurements. Therefore, we design a nonlinear tracking controller that stabilizes the AFM dynamics around a desired periodic orbit. To this end, in the tracking error state space, we define a target invariant manifold, on which the system dynamics fulfills the control objective. First, considering a nominal case with full state measurement and no modeling uncertainty, we design an I&I controller to render the target manifold exponentially attractive. Next, we consider an uncertain AFM dynamics, in which only the displacement of the probe cantilever is measured. In the framework of the I&I method, we recast the robust output feedback control problem as the immersion of the output feedback closed‐loop system into the nominal full state one. For this purpose, we define another target invariant manifold that recovers the performance of the nominal control system. Moreover, to handle large uncertainty/disturbances, we incorporate the method of active disturbance rejection into the I&I output feedback control. Through Lyapunov‐based analysis of the closed‐loop stability and robustness, we show the semiglobal practical stability and convergence of the tracking error dynamics. Finally, we present a set of detailed, comparative software simulations to assess the effectiveness of the control method.

Model-based strategies for sensor fault accommodation in uncertain dynamic processes with multi-rate sampled measurements

This work presents model-based strategies for the accommodation of sensor faults in dynamic processes with multi-rate sampled state measurements. The developed strategies account explicitly for both closed-loop stability and performance considerations. Initially, a model-based feedback control system, in which a model predictor compensates for the unavailability of state measurements between sampling times, is designed. The stability and performance characteristics of the multi-rate sampled-data closed-loop system are analyzed and explicitly characterized in terms of the state sampling rates, the fault parameters, and the various process, model and controller design parameters. The resulting characterizations provide insight into the robustness and margins of tolerable faults that can be accommodated, and are used to develop both stability-based and performance-based fault accommodation schemes that aim to maintain closed-loop stability and minimize the degradation of closed-loop performance in the presence of sensor faults. Two types of sensor faults are considered. These include faults that manifest themselves as improper sensor readings, as well as faults that cause drift in the sensor sampling rate. The first type of fault introduces errors in the model state updates at the sampling times, while the second type of fault alters the rate at which the sensors sample the process states. The developed methods are illustrated through a case study involving a cascade of two non-isothermal continuous-stirred tank reactors with plant-model mismatch and access to full state measurements that are sampled at different rates. The case study gives some insight into the effects of sensor faults on the stability and performance of the sampled-data state feedback control system, and how these effects can be mitigated through use of fault-tolerant control.

Path Following Control of Fully Actuated Autonomous Underwater Vehicle Based on LADRC

Abstract This paper presents an active disturbances rejecter controller (ADRC) for position and path following control of a fully actuated autonomous underwater vehicle (AUV). The unmodeled, undesirable dynamics and disturbances reduce the performances of classical controllers and complicate the design of appropriate and efficient controllers. In the proposed approach, the different modeling complexities; such as uncertain parameters, non-linearities, and external disturbances are considered all as a part of disturbance which is estimated in real-time by the extended state observer ESO, and effectively compensated from the control law. The ESO is also able to estimate the position and velocity of the system in real-time, in case where the full state measurement of the AUV is not possible during experiments. Computer simulations demonstrate the high ability of the AUV tracking control based on ADRC, to follow the desired trajectory in the horizontal plane and space with high precision, and showed high robustness and efficiency in rejecting the external and internal disturbances caused by significant changes in parameters of the system, and the added position disturbances.

Design of robust MPPT controller for grid-connected PMSG-Based wind turbine via perturbation observation based nonlinear adaptive control

This paper presents a robust maximum power point tracking (MPPT) control scheme for a grid-connected permanent magnet synchronous generator based wind turbine (PMSG-WT) using perturbation observation based nonlinear adaptive control. In the proposed control scheme, system nonlinearities, parameter uncertainties, and external disturbances of the PMSG-WT are represented as a lumped perturbation term, which is estimated by a high-gain perturbation observer. The estimate of the lumped perturbation is employed to compensate the actual perturbation and further achieve adaptive feedback linearizing control of the original nonlinear system, without requiring the detailed system model and full state measurements. The effectiveness of the proposed control scheme is verified through both simulation studies and experimental tests. The results show that, compared with the conventional vector controller and the standard feedback linearizing controller, the proposed control strategy provides higher power conversion efficiency and has better dynamic performances and robustness against parameter uncertainties and external disturbances.

Cooperative Fault Diagnosis for Uncertain Nonlinear Multiagent Systems Based on Adaptive Distributed Fuzzy Estimators

This paper presents a cooperative fault diagnosis scheme for a class of uncertain nonlinear multiagent systems component and sensor faults in individual agents. Since the faulty system affects the healthy systems through interconnections, for each agent an estimator is designed to collect neighboring output estimations errors to consider its faulty effects on others, when computing its estimations for local state and faulty parameters. A new structure of distributed estimators is proposed by filtering regressor signals and sharing them among agents. Then, the sharings of signals are planned by properly constructing auxiliary graphs for undirected and directed networks. Two conditions are given to preselect estimators parameters for the convergences of the estimation errors. Unlike the existing results dealing with one common parameter with full state measurement and only for undirected graphs, this paper presents an output measurement-based approach for multiple parameters in undirected/directed networks. It shows that for the faults not providing persistent excitation in a signal agent, it is possible to estimate the faults exactly if the they excite all agents persistently. A simulation example of a group of single-link flexible-joint robots is given to verify the effectiveness of the proposed method.

Observer Design for Semilinear Time Fractional Diffusion Systems with Spatially Varying Parameters

This paper describes how to design observers for the semilinear time fractional diffusion systems with spatially varying parameters. The motivation is that the availability of full-state measurements in some practical cases may be impossible for direct measurement due to the difficulties in measuring. For this, an integral transformation is first proposed to equivalently convert the considered systems into a simpler form with Robin boundary conditions. Besides, we build an extended Luenbergertype observer that uses measurements of the inputs and the outputs of system under consideration to determine its state. MittagLeffler stabilization problem of the corresponding semilinear observer error dynamic systems is then explored via backstepping technique. A numerical illustration is finally included.

Adaptive estimation for nonlinear systems using reproducing kernel Hilbert spaces

This paper extends a conventional, general framework for online adaptive estimation problems for systems governed by unknown or uncertain nonlinear ordinary differential equations. The central feature of the theory introduced in this paper represents the unknown function as a member of a reproducing kernel Hilbert space (RKHS) and defines a distributed parameter system (DPS) that governs state estimates and estimates of the unknown function. Under the assumption that full state measurements are available, this paper (1) derives sufficient conditions for the existence and stability of the infinite dimensional online estimation problem, (2) derives existence and stability of finite dimensional approximations of the infinite dimensional approximations, and (3) determines sufficient conditions for the convergence of finite dimensional approximations to the infinite dimensional online estimates. A new condition for persistency of excitation in a RKHS in terms of its evaluation functionals is introduced in the paper that enables proof of convergence of the finite dimensional approximations of the unknown function in the RKHS. This paper studies two particular choices of the RKHS, those that are generated by exponential functions and those that are generated by multiscale kernels defined from a multiresolution analysis.

Event-triggered boundary feedback control for networked reaction-subdiffusion processes with input uncertainties

This paper is concerned with the event-triggered boundary feedback control problems for networked reaction-subdiffusion processes governed by time fractional reaction-diffusion systems with unknown time-varying input uncertainties over sensor/actuator networks. The event-triggered boundary state feedback controller is first designed and implemented via backstepping technique. Moreover, we realize that the availability of full-state measurements in many practical applications may be impossible due to the difficulties in measuring. To solve this limitation, we design an extended Luenberger observer that embeds within the networked sensor to estimate the whole states of the systems under consideration. Based on this, the boundary output feedback event-triggered implementation of the studied system in the context of sensor/actuator networks is then proposed. It is shown that both two kinds of event-triggered strategies could significantly asymptotically stabilize the estimation with the Zeno phenomenon being excluded. Two numerical illustrations are finally included.