Output Constraints Research Articles

Event-triggered Trajectory Tracking Control of Marine Surface Vessels With Time-varying Output Constraints Using Barrier Functions

Event-triggered Trajectory Tracking Control of Marine Surface Vessels With Time-varying Output Constraints Using Barrier Functions

Tracking Control Method for Greenhouse Environment Prediction Model Based on Real-Time Optimization Error Constraints

Model predictive control, without strict constraints on the control model, effectively overcomes problems, such as poor system dynamic quality caused by time delay, can improve control accuracy to a certain extent, and can directly process input and output constraints of the system online. It is applied in greenhouse system control. The appropriate objective optimization function and its corresponding constraint conditions have a direct impact on the solution of the optimal control rate of the model predictive control. In response to this issue, this paper proposes a simple and fast optimal predictive tracking control method. Based on the current prediction model of the greenhouse system, which reflects the dynamic relationship between various control equipment actions and greenhouse environmental factors, a multi degree of freedom discrete time state space model with tracking errors is established. Based on this model, in establishing the corresponding objective optimization function, the gradient descent theory and the two-norm definition are applied, and combined with actual constraints, iterative constraint conditions for real-time error tracking updates are established. Compared with traditional constraint ranges, a constraint function with real-time update characteristics is formed, achieving more accurate constraint conditions. By using rolling optimization and iterative methods, the optimal control rate corresponding to the minimum value of the objective optimization function within a finite time is solved. Through simulation examples, it is demonstrated that the model predictive control with optimization constraints can achieve a more accurate prediction and tracking control of indoor environmental parameters. This method has the advantages of simple control, energy-saving optimization, stable control, and accurate tracking, providing a reference for online real-time prediction and tracking control of future greenhouse environmental parameters.

Neural network surrogate for flow prediction and robust optimization in fractured reservoir systems

Embedded discrete fracture models are widely used to model flow in naturally fractured systems. These models are, however, time-consuming to simulate, and this limits their use for computationally demanding applications such as optimization. In this work we present a deep-learning-based surrogate model for fast prediction of time-varying well flow rates over multiple fracture realizations, for given well bottom-hole pressure schedules. Our surrogate model applies convolutional and recurrent neural networks. The convolutional neural network is used to capture the spatial variability of fractures in different realizations, though the direct input of the fracture geometry is not feasible because structured data are required. We show, however, that the use of the single-phase steady-state pressure solution defined on the structured rock-matrix grid, for each realization, provides the requisite input to the convolutional neural network. The resulting proxy is shown to give accurate flow rate predictions for different discrete fracture realizations. The proxy is then applied for robust optimization (expected net present value computed over an ensemble of realizations is optimized) subject to nonlinear output constraints. Proxy-based optimization provides an 18% improvement in net present value, with an overall speedup of ∼500 relative to simulation-based optimization. The optimized solutions are validated through comparison to simulation results, and agreement is within 1%.

Robust guidance law for cooperative aerial target circumnavigation of UAVs based on composite system theory

Unmanned aerial vehicles (UAVs) are widely used in many fields and relevant research is becoming widespread. However, there are still many problems in the control design of multiple UAVs. This paper proposes a new robust guidance law for the cooperative circumnavigation of an aerial target by multiple UAVs. By incorporating an altitude channel, the controlled fixed-wing UAV was modeled as a simplified three-dimensional (3D) nonlinear dynamic system. Unlike existing studies, tracking and cooperation were considered as a composite system, and the coupling of the UAV linear velocity and angular velocity was considered in the design process. The proposed guidance law considers both the input and output constraints, which represent the system uncertainties and output limits, respectively, and are used to verify the robustness of the guidance law. The robust stability of the disturbed cooperative target circumnavigation system in 3D space is ensured through a strict mathematical proof. Finally, the effectiveness and superiority of the proposed robust guidance law are demonstrated by simulations using a UAV unicycle model and a six-degree-of-freedom (6-DOF) model.

Design, analysis and experiment of robust adaptive repetitive angle tracking control for a one-DOF manipulator driven by pneumatic artificial muscles

There exist nonlinearities, uncertainties and time-varying characteristics in the system dynamics of manipulators driven by pneumatic artificial muscles (PAMs), which makes the accurate dynamic modeling and controller design challenging. In this paper, a robust adaptive repetitive control scheme is proposed to solve the periodic-trajectory tracking problem for a one-degree of freedom (one-DOF) manipulator driven by two PAMs. First, the system model of the one-DOF PAM-driven manipulator is analyzed and derived. Next, during output constraint design by using a barrier Lyapunov function, a sliding mode surface is reasonably constructed to compensate for the differential term of time-varying constraint parameter. Then, with the help of Lipchitz condition, signal replacement technique and reparameterization are implemented to deal with nonparametric uncertainties in the manipulator system for the subsequent uncertainty compensation by using difference learning method and robust feedback strategy. Moreover, the stability of closed-loop PAM-driven manipulator is proven theoretically by using Lyapunov synthesis. In the end, comparative experiments are provided on a self-built PAM testbed to demonstrated the effectiveness of the proposed robust adaptive repetitive control scheme.

Practical Tracking Control via Switching for Uncertain Nonlinear Systems With Both Dead-Zone Input and Output Constraint

Practical Tracking Control via Switching for Uncertain Nonlinear Systems With Both Dead-Zone Input and Output Constraint

Clinical characteristics of heart failure with reduced ejection fraction patients with rare pathogenic variants in dilated cardiomyopathy-associated genes: A subgroup analysis of the PARADIGM-HF trial.

To evaluate the prevalence of pathogenic variants in genes associated with dilated cardiomyopathy (DCM) in a clinical trial population with heart failure and reduced ejection fraction (HFrEF) and describe the baseline characteristics by variant carrier status. This was a post hoc analysis of the Phase 3 PARADIGM-HF trial. Forty-four genes, divided into three tiers, based on definitive, moderate or limited evidence of association with DCM, were assessed for rare predicted loss-of-function (pLoF) variants, which were prioritized using ClinVar annotations, measures of gene transcriptional output and evolutionary constraint, and pLoF confidence predictions. Prevalence was reported for pLoF variant carriers based on DCM-associated gene tiers. Clinical features were compared between carriers and non-carriers. Of the 1412 HFrEF participants with whole-exome sequence data, 68 (4.8%) had at least one pLoF variant in the 8 tier-1 genes (definitive/strong association with DCM), with Titin being most commonly affected. The prevalence increased to 7.5% when considering all 44 genes. Among patients with idiopathic aetiology, 10.0% (23/229) had tier-1 variants only and 12.6% (29/229) had tier-1, -2 or -3 variants. Compared to non-carriers, tier-1 carriers were younger (4 years; adjusted p-value [padj ] = 4 × 10-3 ), leaner (27.8 kg/m2 vs. 29.4 kg/m2 ; padj = 3.2 × 10-3 ), had lower ejection fraction (27.3% vs. 29.8%; padj = 5.8 × 10-3 ), and less likely to have ischaemic aetiology (37.3% vs. 67.4%; padj = 4 × 10-4 ). Deleterious pLoF variants in genes with definitive/strong association with DCM were identified in ∼5% of HFrEF patients from a PARADIGM-HF trial subset, who were younger, had lower ejection fraction and were less likely to have had an ischaemic aetiology.

Adaptive Tracking Control With Global Performance for Output-Constrained MIMO Nonlinear Systems

In this paper, a novel adaptive tracking control technique is developed for multiple-input-multiple-output (MIMO) nonlinear systems with model uncertainty and under output constraints occurring in a limited time interval (OCOLT). The OCOLT, which is a type of constraints occurring some time after (rather than from the beginning of) system operation and with limited duration, can be found in many practical systems and has not been effectively addressed in the literature up to now. By designing a new shift function and with the aid of barrier functions, the constrained system is transformed into an unconstrained one. A new disturbance observer then is designed to estimate unknown disturbances. The resultant control method is able to not only address the OCOLT, but also deal with the constraint-free output case and the infinite-time constrained one (i.e., constraints existing for all <inline-formula><tex-math notation="LaTeX">$t\geq 0$</tex-math></inline-formula>) without the need to revise the control structure. Finally, The effectiveness of the proposed technique is assessed in simulation.

Robust Adaptive Fault-Tolerant Control for a Riser-Vessel System With Input Hysteresis and Time-Varying Output Constraints.

Recently, with the development of the marine economy, marine risers have garnered increasing attention as they present facile and reliable methods for oil and gas transportation. However, these risers are susceptible to vibrations, which can lead to system performance degradation and fatigue damage. Therefore, effective vibration control strategies are required to address this issue. In this study, a novel adaptive fault-tolerant control (FTC) strategy is adopted to suppress the vibrations of a 3-D riser-vessel system against the effects of actuator failures, backlash-like hysteresis, and external disturbances. A barrier-based Lyapunov function is merged to eliminate the time-varying output constraints of the system. Adaptive FTC laws with projection mapping operators are designed to compensate for parameter uncertainties and consider input nonlinearities to improve system robustness. Finally, a rigorous Lyapunov analysis and numerical simulations are performed to verify the validity of the proposed controller and guarantee uniformly bounded stability of the system.

Adaptive Event-Triggered Control for Nonlinear Systems With Asymmetric State Constraints: A Prescribed-Time Approach

Finite/Fixed-time control yields a promising tool to optimize a system&#x0027;s settling time, but lacks the ability to separately define the settling time and the convergence domain (known as <i>practically prescribed-time stability</i>, PPTS). We provide a sufficient condition for PPTS based on a new piecewise exponential function, which decouples the settling time and convergence domain into separately user-defined parameters. We propose an adaptive event-triggered prescribed-time control scheme for nonlinear systems with asymmetric output constraints, using an exponential-type barrier Lyapunov function. We show that this PPTS control scheme can guarantee tracking error convergence performance, while restricting the output state according to the prescribed asymmetric constraints. Compared with traditional finite/fixed-time control, the proposed methodology yields separately user-defined settling time and convergence domain without the prior information on disturbance. Moreover, asymmetric state constraints can be handled in the control structure through bias state transformation, which offers an intuitive analysis technique for general constraint issues. Simulation and experiment results on a heterogeneous teleoperation system demonstrate the merits of the proposed control scheme.

The Fixed-Time Observer-Based Adaptive Tracking Control for Aerial Flexible-Joint Robot with Input Saturation and Output Constraint

The aerial flexible-joint robot (AFJR) manipulation system has been widely used in recent years. To handle uncertainty, the input saturation and the output constraint existing in the system, a fixed-time observer-based adaptive control scheme (FTOAC) is proposed. First, to estimate the input saturation and disturbances from the internal force between the robot and the flight platform, a fixed-time observer is designed. Second, a tangent-barrier Lyapunov function is introduced to implement the output constraint. Third, adaptive neural networks are introduced for the online identification of nonlinear unknown dynamics in the system. In addition, a fixed-time compensator is designed in this paper to eliminate the adverse effects caused by filtering errors. The stability analysis shows that all the signals of the closed-loop system are bounded, and the system satisfies the condition of fixed-time convergence. Finally, the simulation results prove the superiority of the proposed control strategy by comparing it with the previous schemes.

Distributed dynamic matrix control with constrained optimization for collision and obstacle avoidance of simulated multiple quadcopters

AbstractA system of fast moving quadcopters has a high risk of collisions with neighboring quadcopters or obstacles. The objective of this work is to develop a control strategy for collision and obstacle avoidance of multiple quadcopters. In this paper, the problem of distributed dynamic matrix control (DMC) for collision avoidance among a team of multiple quadcopters attempting to reach consensus in the horizontal plane and yaw direction ( , and ) is investigated. Violations of a predetermined safety radius generates output constraints on the DMC optimization function, which has not been dealt with in the literature. Different from past works, the proposed strategy can perform collision avoidance in the , , and ‐directions. In addition, logarithmic barrier functions are implemented as input rate constraints on the control actions. Extensive simulation studies for a team of quadcopters illustrate promising results of the proposed control strategy and case variations. In addition, DMC parameter effects on the system performance are studied, and a successful study for obstacle avoidance is presented.

Event‐triggered model‐free adaptive control for nonlinear systems with output saturation

AbstractIn this article, an original event‐triggered model‐free adaptive control is presented for nonlinear systems with output constraints. Firstly, a compact form dynamic linear model for this nonlinear system is established. Based on the output saturated data, the pseudo partial derivative (PPD) parameter is designed to identify the linear model. Then, a novel event‐triggered mechanism is inserted into the controller. The mechanism will be activated only if the event‐triggered error satisfies the predefined condition; Otherwise, it keeps the last triggering state. The purpose is to alleviate the transmission burden and save the communication resources. Varying from the existing studies, the main innovation of this article is that the controller is reconstructed in virtue of saturated data and event‐triggered condition. The present algorithm convergence is proved. Finally, the simulation study illustrates the feasibility of the plan.

Nonlinear feedback-based event-triggered output-feedback control for marine surface vehicles under deferred output constraints

This paper investigates the trajectory tracking control for marine surface vehicles (MSVs) in the presence of deferred output constraints, unmeasured velocities, model uncertainties, and unknown external disturbances. A dynamic event-triggered extended state observer (DET-ESO) is proposed to achieve real-time estimation for unmeasurable velocities and lumped disturbances with lower communication cost of measurement channels, and an explicit relationship between the upper bounds of estimation errors and design parameters is provided. Based on the observation results, the control design is conducted using the backstepping approach. Initially, a nonlinear feedback method is employed to empower the control scheme to be self-tuning for tracking errors, thereby improving the dynamic performance of the control system. Subsequently, an error-shifting function and a novel barrier Lyapunov function (BLF) suitable for compound features of nonlinear feedback are used to handle deferred output constraints with unknown initial tracking conditions. On this basis, a dynamic event-triggered mechanism, including dynamic variables and decreasing functions of tracking errors, is presented to evaluate control input signals to dramatically reduce the transmission load and actuator execution rate, and the development of the control scheme is accomplished. Theoretical analysis shows that with the proposed control scheme, all signals in the closed-loop system are uniformly ultimately bounded, and the output constraints violated initially are fulfilled within a preset finite time. Additionally, Zeno behavior is excluded strictly. Finally, the comparative simulation results verify the effectiveness of the scheme.

Fixed-time adaptive fuzzy event-triggered control for uncertain nonlinear systems with output constraint and actuator failures

For a category of uncertain nonlinear systems with output constraint and actuator failures, the fixed-time event-triggered control method is investigated. Output constraint and actuator failures are common in practical systems, which may affect the performance of the system. Most of the existing schemes to deal with these problems only guarantee the system's asymptotic stability. It is a challenge to design event-triggered based controllers to achieve fixed-time stability of a system with the above constraints. To handle the above problems, barrier Lyapunov functions (BLFs) and extended fuzzy logic systems (EFLSs) are introduced to the backstepping procedure. Besides, to reduce redundant data transmissions, an event-triggered mechanism (ETM) is designed. On this foundation, a fixed-time event-triggered control method is proposed. Technically, by using the proposed method, the controlled system is practically fixed-time stable, and the system output abides by the corresponding constrained requirements. Meanwhile, the communication resources are saved. Finally, some simulation examples are used to demonstrate the effectiveness of the control method.

Cooperative Learning Control of Unknown Nonlinear Multi-agent Systems with Time-varying Output Constraints Using Neural Networks and Barrier Lyapunov Functions via Backstepping Design

Cooperative Learning Control of Unknown Nonlinear Multi-agent Systems with Time-varying Output Constraints Using Neural Networks and Barrier Lyapunov Functions via Backstepping Design

Disturbance‐observer‐based adaptive finite‐time dynamic surface control for PMSM with time‐varying asymmetric output constraint

AbstractThis article presents a disturbance‐observer‐based adaptive finite‐time dynamic surface control scheme, capable of guaranteeing transient behavior for the PMSM with arbitrary asymmetric time‐varying output constraint and unmatched external disturbance. The major challenge of this paper is devising efficient strategies to tackle the nonsymmetric output restraints with arbitrary characteristics and unmatched external perturbation for the system under the finite‐time backstepping framework. Given this, a nonlinear transformation function is adopted to coordinate from the output‐constrained dynamic model to an uncontained one, and a finite‐time disturbance observer is introduced to evaluate the unmatched external perturbation. Then, a dynamic surface control approach having adaptive properties for PMSM is conceived by combing a neural network to evaluate the nonlinear functions and a first‐order filter to handle the “explosion of complexity.” Additionally, it is proved that the signals in the closed‐loop system can narrow down to a bounded region and the tracking error can merge in a limited time to tiny vicinity of zero by employing a fast finite‐time stability principle. Eventually, simulation cases and contrast results reveal the tracking performance and immunity to the disturbance of the devised controller.

Backstepping Control for Vibration Suppression of 2-D Euler–Bernoulli Beam Based on Nonlinear Saturation Compensator

In this article, a boundary control scheme is proposed to suppress 2-D vibration of Euler–Bernoulli beam with output constraints and input saturation. The original partial differential equations (PDEs) model is transformed to a new form containing virtual control. Then a boundary controller is designed via the backstepping method to suppress the coupled vibration. The hyperbolic tangent function and Nussbaum function are employed to deal with the input saturation. A barrier Lyapunov function with time adjusting function is introduced to suppress the structural vibration of Euler–Bernoulli beam with arbitrary initial conditions. The disturbance observer is designed to deal with the unknown boundary disturbance. Finally, the simulation results show the effectiveness of the proposed vibration controller.

Optimal dispatch of isolated island micro-grid based on W-K-PSO algorithm

Aiming at the micro-grid system with wind turbines, photovoltaics, micro gas engines, fuel cells, energy storage batteries, and loads, taking the optimal economic and environmental benefits as the objective function and considering the output constraints and climbing power constraints of each distributed power supply, battery charging and discharging protection and power balance constraints in micro-grid, the output models of each distributed power supply are established. On this basis, particle swarm optimization with improved inertia weight and contraction divisor is used to solve the objective function. Combined with the time-division scheduling strategy, the simulation analysis is carried out on the MATLAB2021a platform. The results show that the time-division scheduling strategy can reduce the total cost more effectively, which verifies the rationality and feasibility of this method.

Prescribed-time Stabilization with Time-varying Output Constraints for Switched Nonlinear Systems

Prescribed-time Stabilization with Time-varying Output Constraints for Switched Nonlinear Systems