Unmeasured Disturbances Research Articles

Composite-Observer-Based Adaptive Consensus Tracking Control for Nonlinear MASs With Unknown Control Directions Against Deception Attacks.

This article primarily studies the adaptive output-feedback consensus tracking control issue for nonlinear multiagent systems (MASs) with unknown control directions against deception attacks. First, a composite observer combining the state observer and the disturbance observer is developed to concurrently estimate the states of confronting deception attacks and unmeasurable disturbances. Moreover, to resolve the unknown gains resulting from deception attacks, the adaptive attack compensator is proposed. Furthermore, in view of the logarithm Lyapunov function in the final step of the design process and the intelligent approximation technique, a new composite-observer-based adaptive consensus tracking control strategy is constructed. The suggested control strategy ensures the boundedness of all the closed-loop signals while also achieving synchronous tracking of the leader's output by the followers. Last but not least, the effectiveness of the suggested control strategy is validated through two simulation examples.

Development of an Extended State Observer for Monitoring of a PWR Based on a Two-Point Kinetic Model

Accurate estimation of unmeasured system states and disturbances in a pressurized water reactor (PWR) is essential for effective control, operation optimization, and safety monitoring. To this end, this paper investigates the estimation of unmeasured system states and disturbances of the PWR system during load-following operation. First, a mathematical model for the PWR system is established based on the two-point kinetics equations with one equivalent delayed neutron precursor group. Subsequently, an extended state observer (ESO) integration scheme, incorporating two coupled ESOs, is constructed to estimate unmeasured system states, including relative density of delayed neutron precursor, average fuel temperature, total reactivity, xenon concentration, and iodine concentration, along with time-varying disturbances, with the use of measurements of the PWR system only. According to the Lyapunov stability theorem, it is proved that the estimation error dynamic of the proposed ESO integration scheme is uniformly ultimately bounded stable. Finally, simulation results confirm that the proposed ESO integration scheme provides higher estimation accuracy and stronger robustness against measurement noises, model uncertainties, and external disturbances compared to both a high-gain observer and a high-order sliding mode observer.

Observer-Based Consensus Control for MASs With Prescribed Constraints via Reinforcement Learning Algorithm.

In this article, an adaptive optimal consensus control problem is studied for multiagent systems (MASs) with external disturbances, unmeasurable states, and prescribed constraints. First, by using neural networks (NNs), a composite observer is constructed to estimate the unmeasurable states and disturbances simultaneously. Then, the consensus error is guaranteed within a prescribed boundary by presenting an improved prescribed performance control (PPC) technique, and the initial conditions for the error are eliminated. In addition, the updating laws of actor-critic NNs are established by using a simplified reinforcement learning (RL) algorithm based on the uniqueness of optimal solution, and the asymmetric input saturation is resolved by designing auxiliary system instead of using nonquadratic cost functions in other optimal control methods. Finally, the boundedness of all signals in the closed-loop system is proved by using Lyapunov stability theory. The effectiveness of the proposed control method is verified by a simulation example.

An Orbital-attitude Coupled Control Framework for a Full-scale Flexible Electric Solar Wind Sail Spacecraft in Orbital Transformation Missions

In this work, a novel orbital-attitude coupled control framework is developed for the electric solar wind sail (E-sail) spacecraft, aiming to handle the orbital transformation and attitude maneuver simultaneously. In the framework, the desired attitude parameters and the slow-varying voltage component are provided by the orbital control strategy, while the current attitude parameters and the fast-varying voltage component are manipulated by the attitude control strategy to approach their desired values. The orbital-attitude coupled characteristics of the E-sail spacecraft, including the flexibility-induced coupling effect, are fully described by the referenced nodal coordinate formulation. Considering the input saturation conditions, the governing equation for the orbital control strategy is then derived, in which the in-plane and out-of-plane displacement and velocity errors are prescribed as the state variables to be eliminated. An integral sliding mode control (ISMC) scheme is proposed to improve the robustness against the unmeasurable disturbance term. A model predictive control (MPC) scheme is introduced to enhance the convergence efficiency, where a quadratic optimization is performed to plan the desired attitude parameters and voltage components within the prediction horizon. To evaluate the control performance in the orbital transformation and attitude maneuver missions on the displaced non-Keplerian orbit, a series of scenarios with complex initial conditions are simulated under different control schemes, including the ISMC-MPC compound scheme. The results show that the control strategy designed under the rigid-body assumptions may not be feasible for the flexible E-sail spacecraft, while the investigated control strategy realizes the accurate and efficient convergence of the orbital and attitude variables on both the rigid and flexible E-sail spacecraft with the tether deformation stabilized.

A robust batch-to-batch optimization framework for pharmaceutical applications

The study proposes a robust algorithm for batch-to-batch optimization in the presence of model-mismatch. Robustness is achieved by the implementation of the following features: i — the gradient correction step is modified to consider the gradients of the cost function and constraints at both final and intermediate points, ii — Economic Model Predictive Control is applied to mitigate the impact of unmeasured disturbances on the optimum, and iii — an optimal design of experiments is performed to expedite convergence. Significant improvements of the proposed algorithm in convergence to the process optimum and robustness to noise, unmeasured disturbances, and model error are demonstrated using a fed-batch fermentation for penicillin production.

Event-triggered anti-disturbance fault tolerant control for switched networked control systems

This work centres on the event-triggered anti-disturbance fault-tolerant (ETADFT) control problem for unknown switched networked control systems (USNCSs) with dual-source disturbances and unknown faults by the multiple Lyapunov functions method. A dynamic memory event-triggered (DMET) mechanism is developed to optimise the utilisation of transmission resources. A composite switching observer is devised to approximate the unmeasurable state, fault and disturbance. By utilising the DMET mechanism, composite switching observer and time-relevant switching regulator, an ETADFT controller is formulated to counteract the impact of the unknown disturbance and tolerant unknown faults. Finally, the suggested ETADFT control strategy is implemented on the switched RLC circuit model to verify the rationality of the control mechanism.

Coordinating variable refrigerant flow system for effective demand response in commercial buildings

Model Predictive Control (MPC) is extensively utilized for demand response (DR) in commercial buildings. When deploying MPC in real buildings, the model uncertainty is unavoidable, which could come from multiple sources: incorrectly identified values of model parameters, unmeasured disturbances, sensor errors, etc. However, there is a lack of research on the effects of model uncertainty on the performance of MPC for DR events. This study addresses this gap by analyzing the impact of model uncertainty on MPC in coordinating multiple VRF systems in commercial buildings. A virtual testbed was developed to benchmark and evaluate different DR control algorithms. We compared rule-based control (RBC), priority stack-based control (PSBC), and MPC with model uncertainties. This involved assuming normal distributions with various means for biased and unbiased uncertainties and different standard deviations. Our results show that MPC is robust to model uncertainty. For costs saving tasks, when the model uncertainty is unbiased, MPC outperforms the RBC controller with fewer discomfort hours and lower energy costs when the uncertainty of cooling load is below 40 % and 90 %, respectively; when the model uncertainty is biased and increases gradually, MPC can only outperform RBC under small uncertainties of cooling load. For load tracking tasks, when the model uncertainty is unbiased, MPC outperforms PSBC in terms of fewer discomfort hours and lower power tracking error, even with a 100 % uncertainty of cooling load; when the model uncertainty is biased, it exhibits a similar pattern to the unbiased uncertainty. Last, this study compared the computation time and control performance of formulating MPC as integer versus linear programming, demonstrating that linear programming is more suitable for coordinating thermally controllable loads (TCLs) at large-scales due to its faster computation time and good tracking performance. This study provides valuable insights into the effects of model uncertainty on MPC performance in demand response and supports its practical application in commercial buildings.

Adaptive RISE-based tracking control of uncertain nonlinear systems: A FAS approach

A fully actuated system (FAS) approach integrated with adaptive robust integral of the sign of the error (ARISE) feedback control strategy is proposed for multi-input multi-output nonlinear systems in the presence of both external disturbances and parametric uncertainties. Owing to an inability to eliminate unmeasured disturbances and model inaccuracies simultaneously, the existing results based on the FAS approaches are typically limited to the uniformly ultimate boundedness of the tracking errors. To achieve the asymptotic tracking performance confronted with parametric uncertainties and time-varying disturbances, an ARISE feedback controller with desired compensation is synthesized to suppress the adverse effects arising from nonlinearity and uncertainty of the system. The improvements compared to the traditional RISE feedback control are attributed to two aspects: (i) the feedback gains in the RISE term are adjustable-online without having to know the prior bounds of disturbances and their time derivatives; (ii) a desired compensation-based adaptive feedforward term, primarily employing the desired trajectories in place of the measured states, could weaken the underlying interaction between the adaptive compensation and robustness part. A rigorous stability analysis is provided to demonstrate that the system state can asymptotically track a bounded desired trajectory in spite of bounded disturbances and parametric uncertainties. Comparative simulations on an under-actuated planar manipulator, possessing an equivalent multi-order FAS model, have been conducted to verify the effectiveness and merits of the developed controller. Experimental validation on a two-wheeled self-balancing robot is also provided to show the feasibility of the proposed approach.

State and parameter estimation of a dynamic froth flotation model using industrial data

This paper investigates an observable dynamic model of froth flotation circuits aimed at online state and parameter estimation and model-based control. The aim is to estimate the model states and parameters online from industrial data. However, in light of limitations in the plant data, additional model analysis is conducted. It is shown that without online compositional measurements, only the states and parameters of a reduced model can be estimated online. The reduced model lumps all recovery mechanisms into a single empirical equation. The reduced model is used to develop a moving horizon estimator (MHE) which is implemented on the industrial data. The state and parameter estimates from the MHE are used to evaluate the model prediction accuracy over a receding control horizon as would be done in model predictive control (MPC). Given the uncertainty of the available data, unmeasured disturbances and missing online measurements, the estimation and prediction results are reasonably accurate, at least in a qualitative sense. If accurate and reliable online measurements are available for estimation, the reduced model shows potential to be used for long-term model-based supervisory control of a flotation circuit.

Stabilization of Two Axis Gimbal System with Self Tuning PID Control

Gimbal is a system that provides locking and tracking of the seeker on the target in missiles and increases the angle of view with its mobility in two axes. In this study, stabilization of a two-axis gimbal system used in the missile was carried out. In gimbal stabilization, adaptive controllers are preferred instead of classical controllers due to unbalance, cross-coupling, and unmeasurable disturbances. A Self Tuning PID controller based on Fuzzy Logic was developed for axis controls in the stabilization algorithm. Thanks to this controller, which works with the principle of choosing the most appropriate coefficient at every step, it was possible to control with less than 3% errors in the tests performed with the flight simulator. In addition, a PID controller whose coefficients are optimized with Particle Swarm Optimization is designed for comparison purposes. In experimental studies, it was seen that PID with adjustable coefficients gave better results than the fixed PID.

Analysis of a Simplified Predictive Function Control Formulation Using First Order Transfer Function for Adaptive Cruise Control

This paper presents a formulation and analysis of a low computation Predictive Functional Control (PFC), which is a simplified version of the more advanced Model Predictive Control (MPC) for an Adaptive Cruise Control (ACC) system by using a representation of first order closed-loop transfer function. In this work, a non-linear mathematical model of vehicle longitudinal dynamics is considered as a control plant. Then, a simple Proportional Integral (PI) controller is employed as an inner loop to identify the first-order relationship between its actual and desired trajectory speed according to the reasonable time constant based on the logical response of pedals pressing. To directly control the whole plant, the PFC is formulated as an outer loop to track the desired speed together with the convergence rate based on a user preference while satisfying constraints related to acceleration and safe distancing. Since PFC is formulated based on the first-order transfer function, the prediction and tuning processes are straightforward and specific to this system. The simulation results confirm that the proposed controller managed to track the desired speed while maintaining a comfortable driving response. Besides, the controller also can retain safe distancing during the car following application, even in the presence of unmeasured disturbance. In summary, this framework can avoid the need to formulate an inverse non-linear model that is typically used when deploying a hierarchical control structure to compute the throttle and brake pedals pressing as it has been replaced with an inner loop PI controller. The performance also is comparable yet more conservative due to the simplification. These findings can become a good reference for designing and improving the ACC controller, as the framework can be easily generalized for any type of vehicle for future work.

Nonsingular fixed-time K-filter output-feedback control for high-order nonlinear multi-agent systems

This paper investigates the problem of nonsingular fixed-time control for high-order nonlinear multi-agent systems with unmeasurable states and disturbances. To address the unmeasurable states, a K-filter is introduced to reconstruct the system with the input and output only and to facilitate the construction of the observer. To provide an approximation for the unknown nonlinear functions, the fuzzy logic system technique is applied. A novel tracking controller is proposed to ensure that all signals remain bounded for the closed-loop system and that the tracking error converges into a small and adjustable set within a fixed time by utilising the adaptive backstepping recursive technology and the adding power integrator technique. Meanwhile, the singular problems arising from the fixed-time stability criterion can be also avoided. Finally, a simulation experiment verifies the effectiveness of the designed control scheme.

Robust reference governor for input-constrained model predictive control to enforce state constraints at low computational cost

In the setting of discrete-time linear systems with unmeasured set-bounded disturbances, this paper proposes a control scheme that combines a disturbance free input-constrained Model Predictive Control (uMPC) policy and a reference governor (RG) which modifies the reference command to enforce constraints that are not enforced by the uMPC policy. Such a scheme, referred to as the robust RGMPC, can handle (possibly nonlinear) state and input constraints and only requires solving an optimisation problem for MPC with polytopic input constraints, for which fast algorithms exist. Conditions are given which ensure recursive feasibility of the robust RGMPC scheme, finite-time convergence of the modified reference command to the desired reference command and asymptotic convergence of the state to an invariant neighbourhood of the associated set-point. Simulation results for a constrained spacecraft rendezvous manoeuvre demonstrate that the rRGMPC scheme has lower average computational time than several robust state and input-constrained MPC controllers.

Less conservative robust reference governors and their applications

The applications of reference governors to systems with unmeasured set-bounded disturbances can lead to conservative solutions. This conservatism can be reduced by estimating the disturbance from output measurements and canceling it in the nominal control law. In this paper, a reference governor based on such an approach is considered and time-varying, disturbance and state estimation errors bounding sets are derived. Consequently, the traditional implementation of a reference governor, which exploits a constraint admissible positively-invariant set of constant commands and initial states, is replaced by one which utilizes a time-dependent sequence of similar sets (which are not necessary nested). Examples are reported which include two applications to longitudinal control of aircraft that illustrate handling of elevator uncertainty and wing icing.

Offset-Free Koopman Model Predictive Control of Thermal Comfort Regulation for a Variable Refrigerant Flow-Dedicated Outdoor Air System-Combined System

Abstract Variable refrigerant flow (VRF) system has been an appealing solution of air conditioning for residential and commercial buildings, due to its flexibility and cost effectiveness, while lack of ventilation capability is a major drawback. Incorporation of dedicated outdoor air system (DOAS) is a typical practice. However, good coordination between DOAS and VRF is critical for achieving desired thermal comfort is challenging due to the possible complexity of mixed sensible and latent heat exchanges. In this paper, to handle the nonlinear dynamic characteristics of VRF-DOAS system, we propose an offset-free Koopman model predictive control (MPC) strategy for thermal comfort regulation, in which the MPC design is computationally more efficient due to the convex problem formulation and the use of reduced-order Koopman models, and the offset-free MPC structure enhances the robustness to model uncertainties and unmeasured disturbances. A control-oriented model is obtained by hybridizing the first-principle and data-driven modeling approach. The proposed controls strategy is evaluated with a Modelica simulation model of a VRF-DOAS system. A Dymola-Python cosimulation platform is developed via the functional mockup interface (FMI), for which the MPC algorithms are implemented in Python. Simulation results show significantly better performance of the offset-free Koopman MPC in thermal comfort regulation.

Coordinated discrete-time super-twisting sliding mode controller coupled with time-delay estimator for PWR-based nuclear steam supply system

The coordination control problem for the core thermal power of the nuclear reactor (NR) and the water level of the nuclear steam generator (SG) is a crucial challenge for real-time model-based control of a pressurized-water reactor (PWR)-based nuclear steam supply system (NSSS). In this paper, a new coordinated discrete-time super-twisting sliding mode controller (CDTSTSMC) coupled with time-delay estimators (TDEs) is proposed, tackling the coordination control problem for the core thermal power of the NR and the water level of the nuclear SG in the presence of unknown dynamics, such as unmeasured system states, model uncertainties, parameter perturbations, and disturbances. First of all, the continuous-time dynamical models of both the NR and the SG in the PWR-based NSSS are described with consideration of unknown dynamics and then are transformed into discrete-time forms using the Euler approximation method. Two TDEs capable of estimating the unknown dynamics existing in the NR and the SG, respectively, are developed, which rely merely on the measurements of the PWR-based NSSS without the upper bounds of the uncertain dynamics being known. After that, a CDTSTSMC strategy, based on the discrete-time dynamical models describing both the NR and the SG, is proposed, allowing the core thermal power to be driven to a desired value while at the same time maintaining the desired water level in the SG with the help of TDEs. It is proved from stability analysis using a Lyapunov approach that under the proposed CDTSTSMC strategy coupled with the TDEs, the stability of the controlled PWR-based NSSS can be ensured in a wide range of load, where the control errors of both the NR and the SG are driven to a neighbor of zero. Finally, the proposed CDTSTSMC strategy coupled with the TDEs is compared with the previous discrete-time optimized proportional–integral–derivative controller (DTOPIDC) and fuzzy weighting controller (FWC). The simulation results reveal that the proposed CDTSTSMC strategy coupled with the TDEs outperforms the DTOPIDC and the FWC, particularly in rejecting unknown dynamics while maintaining both the core thermal power in the NR and the water level in the SG in their desired ranges.

Multi-objective design of parametric equivalent-input-disturbance approaches with application to vehicle suspension systems

A multi-objective parametric design method is developed for an equivalent-input-disturbance-based control system with mismatched disturbances. Different from the existing design methods that calculated control parameters by stability conditions, multiple performance objectives including unmeasurable output estimation, disturbance rejection, noise suppression, input refinement, and robustness, are simultaneously optimized in the developed design. First, for the sake of low computation complexity, the closed-loop system is represented. Then, the parameterization of the design conditions for multiple performance objectives is given. Further, a nonlinear optimization method is used to perform the performance-oriented system design. Lastly, the efficacy and benefits of the developed methods are demonstrated through simulations of a quarter-vehicle suspension system and comparisons with other alternative methods.

Composite anti-disturbance dynamic positioning for mass-switched unmanned marine vehicles with multisource disturbances and actuator saturation: A switched model method

This article focuses on the composite anti-disturbance dynamic positioning (CADDP) control problem for the unmanned marine vehicle (UMV) system subject to mass switching, multisource disturbances and actuator saturation. The multisource disturbances include the unmeasurable modeled disturbances and measurable unmodeled disturbances. First, a switched model is introduced to describe the mass-switched UMV (MSUMV) system. Then, a series of switching observers are built to capture the unmeasurable disturbances. Meanwhile, the convex hull technique is exploited to address the actuator saturation. Further, based on the disturbance observer, the CADDP control scheme is developed to alleviate the influences of the multisource disturbances and actuator saturation, and to achieve the dynamic positioning (DP) of the MSUMV model. In the end, the established CADDP control strategy is carried on a supply vessel to illustrate the feasibility of the developed theory.

Control-oriented thermal model and load identification for circulating cooling water system in precision process applications

With the increasing precision in machining and measurement, there is a growing demand for improved temperature stability in cooling water systems used in industrial processes. Accurate thermal models and load states are essential for developing efficient controllers to enhance temperature performance. This paper focuses on the development of thermal models for various components of circulating water systems in precision water-cooling processes. It presents a control-oriented thermal model framework that segregates the water circulation cycles and defines system inputs and processes. A novel identification algorithm is proposed to address thermal inertia and cyclic nonlinearity by extracting the cyclic period and cycle change rate. The algorithm can simultaneously estimate the state–space model and unmeasurable load disturbances. A validation experimental setup was constructed, and the results demonstrate that, for different flow scenarios, the estimated load and thermal model parameters align well, with correlations exceeding 0.85 and error ratios close to 10%. Additionally, the circulating water output temperature can be accurately predicted. The fitting degrees surpass 84% within five cycles, with correlations exceeding 0.97 and error standard deviations below 0.22 °C.

Model-driven full system dynamics estimation of PMSM-driven chain shell magazine

Based on the system dynamic model, a full system dynamics estimation method is proposed for a chain shell magazine driven by a permanent magnet synchronous motor (PMSM). An adaptive extended state observer (AESO) is proposed to estimate the unmeasured states and disturbance, in which the model parameters are adjusted in real time. Theoretical analysis shows that the estimation errors of the disturbances and unmeasured states converge exponentially to zero, and the parameter estimation error can be obtained from the extended state. Then, based on the extended state of the AESO, a novel parameter estimation law is designed. Due to the convergence of AESO, the novel parameter estimation law is insensitive to controllers and excitation signal. Under persistent excitation (PE) condition, the estimated parameters will converge to a compact set around the actual parameter value. Without PE signal, the estimated parameters will converge to zero for the extended state. Simulation and experimental results show that the proposed method can accurately estimate the unmeasured states and disturbance of the chain shell magazine, and the estimated parameters will converge to the actual value without strictly continuous PE signals.