Unmeasured Disturbances Research Articles

Output tracking anti-disturbance control for turbofan systems

A new output tracking (OT) control strategy is established for the turbofan systems involved by multiple disturbances, the measurable disturbance and unmeasurable disturbance. The control aim is to improve the OT performance, while restraining the effect of the multiple disturbances on the turbofan systems. First, an observer is formed to estimate the unmeasurable modeled disturbance. Next, a controller depending on the observer is built to capture the OT performance and the multiple disturbance restrain performance. The designed controller not only combines the disturbance estimation and the output error, but also takes advantage of the available signal to be tracked. By such a controller, the OT performance is enhanced. In the end, the developed OT anti-disturbance control scheme is performed on a hardware-in-loop simulation platform inserted with the turbofan system model to improve the OT performance of the turbofan system.

Composite Observer-based Finite-time Command Filtered Adaptive Control for Full-state Constraints Nonlinear Systems

In this paper, we look at a type of strict-feedback nonlinear systems that have neural adaptive finite-time command filtered control and full-state constraints. To begin with, indeterminate nonlinear functions are estimated using the NN. The system’s capacity to detect unmeasurable states and disturbances is done while using a composite observer, which significantly boosts the system’s anti-interference performance. Next, the issue of “complex explosion” that arose in the backstepping technique is resolved by adding a modified fractional-order filter. In order to address the full-state constraint issues, the BLF is also introduced. An adaptive finite-time tracking controller is created using Lyapunov’s theory analysis to make sure that all signals are confined to the finite time period. Lastly, a simulation study is conducted to evaluate the performance of the suggested control strategy.

Neural Network Trajectory Tracking Control on Electromagnetic Suspension Systems

A new adaptive-like neural control strategy for motion reference trajectory tracking for a nonlinear electromagnetic suspension dynamic system is introduced. Artificial neural networks, differential flatness and sliding modes are strategically integrated in the presented adaptive neural network control design approach. The robustness and efficiency of the magnetic suspension control system on desired smooth position reference profile tracking can be improved in this fashion. A single levitation control parameter is tuned on-line from a neural adaptive perspective by using information of the reference trajectory tracking error signal only. The sliding mode discontinuous control action is approximated by a neural network-based adaptive continuous control function. Control design is firstly developed from theoretical modelling of the nonlinear physical system. Next, dependency on theoretical modelling of the nonlinear dynamic system is substantially reduced by integrating B-spline neural networks and sliding modes in the electromagnetic levitation control technique. On-line accurate estimation of uncertainty, unmeasured external disturbances and uncertain nonlinearities are conveniently evaded. The effective performance of the robust trajectory tracking levitation control approach is depicted for multiple simulation operating scenarios. The capability of active disturbance suppression is furthermore evidenced. The presented B-spline neural network trajectory tracking control design approach based on sliding modes and differential flatness can be extended to other controllable complex uncertain nonlinear dynamic systems where internal and external disturbances represent a relevant issue. Computer simulations and analytical results demonstrate the effective performance of the new adaptive neural control method.

Data-Driven Predictive Control With Improved Performance Using Segmented Trajectories

A class of data-driven control methods has recently emerged based on Willems' fundamental lemma. Such methods can ease the modelling burden in control design but can be sensitive to disturbances acting on the system under control. In this paper, we propose a restructuring of the problem to incorporate segmented prediction trajectories. The proposed segmentation leads to reduced tracking error for longer prediction horizons in the presence of unmeasured disturbance and noise when compared to an unsegmented formulation. The performance characteristics are illustrated in a set-point tracking case study in which the segmented formulation enables more consistent performance over a wide range of prediction horizons. The method is then applied to a building energy management problem using a detailed simulation environment. The case studies show that good tracking performance is achieved for a range of horizon choices, whereas performance degrades with longer horizons without segmentation.

Observer‐based neural adaptive fixed‐time tracking control for multi‐input multi‐output nonlinear systems with actuator faults

SummaryThis study developed a new strategy for adaptive fixed‐time output feedback control of multi‐input multi‐output (MIMO) nonlinear systems involving unmeasurable external disturbances and actuator faults. Two actuator faults, loss of effectiveness and lock‐in‐place were simultaneously considered. Furthermore, a composite observer was used in MIMO systems to estimate unmeasurable states and external disturbances simultaneously, and radial basis function neural networks were employed to identify unknown internal nonlinearities. Additionally, the command‐filtered backstepping approach was applied to avoid tedious analytic calculations of the backstepping framework, and a compensation item was constructed to attenuate the filter error. A fault‐tolerant controller was developed to ensure that the overall states of the controlled system were practically fixed‐time bounded while the tracking error was regulated to the equilibrium region having a fixed‐time convergence ratio. Illustrative studies showed the validity and practicability of the proposed theory.

Editorial for the special issue “Control‐theoretic approaches for systems in the life sciences”

Editorial for the special issue “Control<scp>‐theoretic</scp> approaches for systems in the life sciences”

Bilinear observer-based robust adaptive fault estimation for multizone building VAV terminal units

In this paper, we propose a novel bilinear observer-based robust fault detection, isolation and adaptive fault estimation methodology to precisely estimate a class of actuator faults, namely bias in damper position and lock-in-place faults, in Variable-Air-Volume (VAV) terminal units of Heating Ventilation and Air-Conditioning (HVAC) systems. The proposed adaptive fault estimator is robust in the sense that the fault estimates are not affected by the unmeasured disturbance variable and that the effects of measurement noises on fault estimates are attenuated. The fault estimation algorithm with the integrated building control system improves occupants comfort and reduces the operation, maintenance, and utility cost, thereby reducing the impact on the environment. The effectiveness of the methodology for adaptive estimation of multiple or single VAV damper faults is successfully demonstrated through different simulation scenarios with SIMBAD (SIMulator of Building And Devices), which is being used in industries for testing and validation of building energy management systems.

Sliding mode output feedback control for continuous systems without rank constraint

SummaryThis paper addresses the sliding mode control problem for continuous systems in the output feedback framework. To remove the conventional rank constraint imposed on the input and output matrices, a novel compensator‐based sliding surface is proposed firstly. Then, by designing an unmeasured state and disturbance (USD) observer to estimate the lumped unmeasurable state and external disturbance, both the discontinuous and continuous sliding mode output feedback controllers are synthesized. Finally, the validity of the proposed methods is illustrated by two simulation examples.

Grey-box model and neural network disturbance predictor identification for economic MPC in building energy systems

This paper presents a novel model identification procedure to treat the occupancy induced unmeasured heat disturbance in the building model identification step for a model predictive control (MPC) implementation. First, we present a grey-box identification framework to estimate a dynamic building model that systematically accounts for the large unmeasured disturbance in the training data. Then, we discuss an approach to compute the confidence intervals (CIs) on the parameters estimated in the building model. Next, we develop a framework to identify a neural network (NN) using historical operational data to give the feedforward predictions of the heat disturbance in the MPC optimization problem. Further, we discuss the application of the composite building model and the NN disturbance predictor for energy cost optimization using an economic MPC formulation. We present case studies using a simple two time scale based building model, and demonstrate the effectiveness of the proposed grey-box model and the NN disturbance predictor identification procedures. We perform closed-loop simulations to highlight that additional energy cost savings of up to 4–5% are provided by including the feedforward NN disturbance predictions in the MPC controller. Finally, we also demonstrate the suitability of the proposed two-step model identification procedure to estimate accurate and reliable models using an industrial data set collected from a real office building.

Design of a nonlinear dynamic output feedback controller based on a fixed-time RBF disturbance observer for a PEMFC air supply system

The efficiency and service life of proton exchange membrane fuel cells (PEMFCs) are critically related to the control performance of the air supply system. The unmeasured state variables and system disturbances seriously affect the control performance of the air supply system. This research focuses on developing a nonlinear dynamic output feedback controller to address the multivariable, nonlinear and unmeasurable state of the air supply system. First, a simplified control-oriented model of the air supply system dynamic behavior is developed, and the modeling uncertainties are estimated and compensated using two fixed-time radial basis function neural network disturbance observers (FRBFNN-DOBs). Second, a nonlinear reduced-order cathode pressure observer is designed, and the observer error is bounded for the unknown bounded disturbance. Third, an observer-based nonlinear cathode pressure tracking controller and supply manifold pressure tracking controller are designed using backstepping method. Considering the estimation error of the FRBFNN-DOBs, the robustness of the closed-loop system is proven using the input-to-state stability theory; furthermore, the upper bound of the tracking error and the convergence rate of the initial error are obtained, which is useful to design the controller parameters. Finally, simulation results demonstrate that the designed controller is more advantageous and effective than the PID and MPC.

Disturbance-observer-based finite time sliding mode controller with unmatched uncertainties utilizing improved cubature Kalman filter

Complex reactions are difficult to control as the reactants not only produce desired product but may also further undergo unwanted reactions or the original reactants may degrade through alternate paths. This can be improved by selecting proper measurement and control. In this paper, a robust augmented derivative-free Kalman filter (augmented cubature Kalman filter, ACKF) is propounded for the continuous stirred tank reactor (CSTR) carrying out Van-de-Vusse type complex reactions where the intermediate is the desired product in the midst of consecutive and parallel reactions. Non-isothermal CSTR shows strong non-linearity with inherent challenges as the reaction kinetics depends on process temperature through Arrhenius laws and accompanies non-ideal flow behaviour and unmeasurable disturbances. First, the process exhibits inverse or non-minimum phase behaviour. Second, to control product temperature and concentration, a finite time sliding mode controller (FSMC) is suggested which effectively tracks servo response to the desired set point. Third, a disturbance filter has been proposed in association with FSMC, which constructively eliminates the input disturbances. Realistic simulations for servo-regulatory compliance, elimination of measurement noise, impact on large perturbation, and parametric uncertainty with a state-of-the-art simulator ensure the efficacy of the proposed controller. The results are analysed further with a derivative-free FSMC control approach, considering various performance indices (root mean square error (RMSE), total variation (TV), mean square deviation (MSD)) to assess the goodness of the newly developed control strategy. It has been found that improved control law (improved augmented cubature Kalman filter (IACKF)-FSMC) yields better RMSE (7.2645e–05) than ACKF-FSMC (7.5814e–05).

Hybrid MPC-P controller for the core power control system at TRIGA reactor

The Training, Research, Isotopes, General Atomics (TRIGA) research nuclear reactor is utilized in a wide range nuclear applications and has three versions, Mark I, Mark II, and Mark III. The TRIGA reactor that is available in Malaysia is a Mark II type, Reaktor TRIGA PUSPATI (RTP). The core power control system in the RTP is designed based on many components including the Feedback Control Algorithm (FCA), and the control rod selection algorithm (CRSA). The CRSA is used in the RTP operation to provide external control to the reactor core reactivity. Even though the CRSA provide stability to the reactor operation, it has relatively complex software structure which produce delayed response time for the control rods selection. Thus, the Single Control Absorbing Rod (SCAR) algorithm is designed to improve the reactor performance by minimizing the time response for the rods selection. However, the SCAR yields a non-constant tracking performance due to the highly non-linear dynamic of the control rods. Thus, to compensate the drawbacks of the SCAR with the existing FCA controller, a hybrid controller is proposed based on the integration of a linear model predictive control (MPC) and Proportional (P) controller named as Hybrid MPC-P-SCAR. The MPC application in the SCAR enable the controller to handle multiple constraints through built-in prediction ability to select the best sequence of future control rod velocity during transient state. Whereas at the steady-state, the MPC is optimized by a scheduled P control switches within the allowable range of power variation. The performance of the proposed Hybrid MPC-P-SCAR, the MPC-SCAR and the existing Feedback Control Algorithm with SCAR (FCA-SCAR) is compared via simulation and evaluated in terms of tracking performance, workload of the control rod drive mechanism (CRDM), and the offset elimination in the unmeasured disturbance. Overall, the Hybrid MPC-P-SCAR controller reduces the settling time by 45% and the steady-state error by 61% of the nominal value compared to the FCA-SCAR.

Adaptive fuzzy connectivity-preserving consensus protocols for stochastic strict-feedback nonlinear MASs subject to unmeasured periodic disturbances

Two adaptive fuzzy connectivity-preserving consensus control schemes, in continuous and event-triggered manners, are proposed for stochastic strict-feedback nonlinear multi-agent systems subject to unmeasured periodic disturbances. Differently from the existing works on connectivity-preserving consensus where the deterministic dynamics are investigated, stochastic multi-agent systems are considered. An error transformation is incorporated to preserve the initial interaction pattern determined by initial positions and communication ranges of the agents. Moreover, the stochastic system model suffers from unmeasured periodic disturbances in a nonlinear manner. By associating Fourier series expansion with fuzzy logic system, a novel function approximator is constructed to describe the packaged disturbance-dependent nonlinear functions. Meanwhile, a nonlinear filter is designed to avert the ”explosion of complexity” problem in conventional backstepping recursive design procedure. In addition, the proposed continuous controller is developed to event-triggered one. Then, it is proved that the consensus is achieved and the initial connectivity of communication graph is preserved in the sense of probability. In simulations, two examples are provided to substantiate the effectiveness of the proposed consensus protocols.

Adaptive Robust Path Tracking Control for Autonomous Vehicles Considering Multi-Dimensional System Uncertainty

As the bottom layer of the autonomous vehicle, path tracking control is a crucial element that provides accurate control command to the X-by-wire chassis and guarantees the vehicle safety. To overcome the deterioration of control performance for autonomous vehicle path-tracking controllers caused by modeling errors and parameter perturbation, an adaptive robust control framework is proposed in this paper. Firstly, the 2-DOF vehicle dynamic model is established and the non-singular fast terminal sliding mode control algorithm is adopted to formulate the control law. The unmeasured model disturbance and parameter perturbation is regarded as the system uncertainty. To enhance the control accuracy, the radial basis forward neural network is introduced to estimate such uncertainty in real time. Then, the dynamic model of an active front steering system is established. The model reference control algorithm is applied for the steering torque control considering model uncertainty brought by the dissipation of manufacturing and mechanical wear. Finally, the Simulink–CarSim co-simulation platform is used and the proposed control framework is validated in two test scenarios. The simulation results demonstrate the proposed adaptive robust control algorithm has satisfactory control performance and good robustness against the system uncertainty.

A Study on the Design of Error-Based Adaptive Robust RBF Neural Network Back-Stepping Controller for 2-DOF Snake Robot’s Head

In this paper, we will propose the controller of a 2-DOF head in a snake robot for effective image data reading when the snake robot is driving, and present an error-based adaptive robust radial base function neural network back-stepping controller. The snake robot head system is a nonlinear system, and there are unmeasurable disturbances that occur while driving. To solve this problem, we use back-stepping controller and radial basis function neural network to compensate for unmeasurable disturbances to improve the steady state. In order to further compensate for the network approximation error that occurs during training or large signal changes, we use adaptive coefficients and error functions to approximate the network approximation error and design adaptive robust terms. Compared to the previous controller, the proposed controller can actively compensate for large signal changes and has the advantage of not generating residual input in a steady state. The proposed controller is based on Lyapunov function candidate to design an adaptive law and prove the system stability. The stability of the control system is proven through Lyapunov analysis and bounded. The proposed controller compared and verified the controller performance for two inputs through simulation and presented the efficiency of the controller.

Robust Tube-Based Model Predictive Control for Wave Energy Converters

This paper proposes an efficient robust tube-based model predictive control (RTMPC) strategy for energy-maximization control of wave energy converters (WECs) subject to constraints due to safety considerations. Compared with the existing MPC strategies developed for the WEC control problem, the RTMPC method provides an effective approach to explicitly handle plant-model mismatches with guaranteed constraint satisfaction, contributing to improved energy capture efficiency. The fundamental idea is to integrate disturbance invariant sets into the MPC scheme for energy-maximization control to form a tube-based predictive controller, which enhances the robustness of MPC for a WEC without increasing online computational complexity. The resulting RTMPC controller can bound the WEC plant trajectories in a tube centered around a nominal WEC model trajectory, and uncertainties from un-modeled WEC dynamics and unmeasured disturbances can be mitigated by an error feedback portion. Numerical simulations demonstrate the effectiveness of the proposed control strategy.

Robust Output Regulation for Uncertain Linear Minimum Phase Systems under Unknown Control Direction⋆

This paper investigates the problem of disturbance rejection for SISO uncertain linear minimum phase systems perturbed by an unmeasurable external disturbance under the framework of robust output regulation. The model parameters of systems in question are largely uncertain, including the control direction. In addition, the external disturbance is unstructured but bounded. Towards this end, a novel Unknown Input Observer (UIO)-based regulator is developed to reject the external disturbance, and a switching mechanism with a monitor function is designed to handle the control direction uncertainty. Notable features are that the unstructured external disturbance can be directly estimated and completely rejected by a sliding mode-based observer, and this new scheme can be applied for systems with non-unity relative degree under unknown control direction. The boundedness of closed-loop system and its asymptotic convergence properties are rigorously proved, which is verified by a numerical example.

Synthesis of synergetic laws of control of nonlinear dynamic plants

Given in the work the facilities of applying the methods of the synergistic approach for the synthesis of the law of control of nonlinear dynamic plants. To give, robust properties of the control law were proposed by applying a principle of integral adaptation, which permitted compensation for the impact of the external and parametric disturbances. The realization synergistic law of control is carried out by when constructing the analytical design of aggregated controllers, which procure asymptotic sustainability of the control system for nonlinear dynamic plants. For procuring technological invariance proposed principle of the synergistic control. Formulated an extended dynamical model of the system, which included external and internal disturbances. The essence of proposing the approach is to use an extended model of the plant, taking into account disturbances due to the switching on of integrators and procuring compensation for the influence of disturbances, in distinction from known ones, where uses separate models for each disturbance. The suggested methodology has confirmed examples of digital simulation and has shown an efficient proposed approach to tasks synthesis of a nonlinear system of control with dynamical plants, procuring asymptotic sustainability of the control system and recouping unmeasured and internal disturbance.

A New Adaptive Suboptimal Controller for a Class of Linear Discrete-Time Systems with Unknown Bounded Disturbances

This paper deals with adaptive suboptimal control of linear discrete-time, time-invariant, minimum phase, scalar systems in the presence of arbitrary bounded unmeasurable disturbances whose upper and lower bounds are a priori unknown. The distinguishing feature of the systems to be controlled is that there is no information concerning a priori knowledge of a bounded set to which the unknown parameters of these systems belong. Novelty of this paper is that, instead of unknown a priori membership set of parameters, the peculiar a posteriori membership sets are designed via the use of the measured signals. The adaptive suboptimal control algorithm utilizing this approach is proposed. The properties of the adaptive controller including the convergence of the adaptation procedure and also the ultimate boundedness of system's signals are established. To demonstrate an efficiency of this controller and support the theoretical study, simulation results are presented.

Development of control-oriented models for a building under regular heating, ventilation and air-conditioning operation - a comparative simulation study and an experimental validation

The development of models is a major barrier to the fast and widespread adoption of model predictive control for building HVAC systems. This paper proposes the subspace identification technique, refined through the prediction error method, to quickly obtain a model for the accurate indoor temperature prediction, even with little identification data, even in the presence of large unmeasured disturbances and noisy identification data, and even using data which was collected during the regular HVAC operation of a building. The identification issues associated with grey-box models were thoroughly investigated. In particular, the development of a grey-box model was found to be a complex, lengthy and computationally intensive process, even for a single-zone building, and the models were not physically meaningful. The proposed method was found to be much easier and faster, with a potential for direct practical application. Analysis on experimental data from an existing building provided promising results.