Input Constraints Research Articles

Dynamical analysis and reachable set estimation of T-S fuzzy system with permanent magnet synchronous motor

In this study, we will present the dynamical analysis for permanent magnet synchronous motors (PMSM). Our goal is to identify an ellipsoid that contains the state trajectory of the system in as small a form as possible in the presence of control. First, we designed the non-fragile sampled-data fuzzy controller (NFSDFC) for the PMSM model. Second, the Lyapunov-Krasovskii functional (LKF) strategy, novel integral inequality mechanisms, and certain sufficient conditions are determined, which ensure an ellipsoidal bound of reachable sets for the closed-loop of a system with input constraints derived in terms of linear matrix inequalities (LMIs). Meanwhile, under the larger sampling interval, the corresponding sampled-data controller gains are designed. Finally, numerical examples are given to validate the derived theoretical results.

A flexible and efficient hybrid agricultural subsidy design for promoting food security and safety

The quantity and quality of edible agricultural products are critical for food security (quantity) and safety (quality). Supplying consumers with enough safe food is the key responsibility of food production firms. Still, this aim is not always guaranteed because of input capacity constraints and other limitations in the agricultural sector. A hybrid subsidy, a mix of quantity and quality subsidy, could help achieve food security and safety in a country its flexibility. However, the advantages of the hybrid have not been fully investigated. Thus, this paper designs a hybrid subsidy for edible agricultural products by considering cost uncertainties and input resource constraints. All conclusions are obtained by theoretical mathematical analysis. (1) equilibrium solutions under different conditions—cost uncertainties and input constraints—are obtained, and comparative analyses is offered. (2) the results show that the hybrid subsidy is convenient in the trade-off between food quantity and quality, which means a hybrid subsidy policy design is flexible and efficient for food security and safety. (3) cost uncertainties and input resource constraints have significant impacts on the efficiency of the hybrid subsidy. Findings show that the hybrid subsidy is ideal for supporting edible agricultural products. Additionally, we argue that cost uncertainties and input constraints should be considered when making policy efficiency evaluations. This study has a novel contribution to agricultural support policy design.

Safety-Certified Constrained Control of Maritime Autonomous Surface Ships for Automatic Berthing

The berthing of maritime autonomous surface ships (MASSs) is a challenging operation even for an experienced captain due to the required complicated maneuvering at low speeds in a constrained water space. This paper addresses safety-aware automatic berthing of MASSs moving in a constrained water region with stationary and moving obstacles. A safety-critical constrained anti-disturbance control method is proposed for the automatic berthing of MASSs subject to velocity constraints, input constraints, and collision-avoidance constraints, as well as ocean disturbances. At the kinematic level, desired linear velocities and yaw rate are prescribed by incorporating a line-of-sight guidance scheme to achieve position-heading stabilization. At the kinetic level, an anti-disturbance kinetic control law is designed based on an extended state observer. Through converting the input constraints to velocity constraints on guidance signals, the safety-guidance signals are obtained by solving a quadratic programming problem subject to the state, input, and collision-avoidance constraints. It is proven that an MASS controlled by using the proposed method is input-to-state safe in the presence of ocean disturbances. Simulation results are elaborated to substantiate the efficacy of the proposed safety-certified control law for the automatic berthing of an MASS subject to physical constraints and environmental disturbances.

Low-Complexity Fuzzy Neural Control of Constrained Waverider Vehicles via Fragility-Free Prescribed Performance Approach

In this article, we propose a concise fuzzy neural control framework for Waverider Vehicles with input constraints, while the spurred prescribed performance can be guaranteed, and the challenging fragility problem associated with the existing prescribed performance control (PPC) is avoided. Unlike the existing control protocols without considering computational costs, in this study, the low-complexity fuzzy neural approximation is combined with simple performance functions, which reduces the complexity burden and improves the practicability. Then, in order to handle the adverse effect of the actuator saturation on the control performance, bounded-input-bounded-state stable systems are developed to stabilize the closed-loop control system based on bounded compensations. Specially, flexible adjustment terms are exploited to modify the developed simple performance functions, while fragility-free prescribed performance is achieved for tracking errors, and moreover the fragility defect of the existing PPC is remedied. Finally, the efficiency and superiority of the design are verified via compared simulations.

Data‐driven consistent control with data compensation for a class of unknown nonlinear multiagent systems with constraints

AbstractTo solve the problem of longitudinal cooperative formation driving control of multiple vehicles, a novel model‐free adaptive control algorithm with data compensation under constraint conditions (COM‐cMFAC) is proposed in this manuscript. In the COM‐cMFAC algorithm, the pseudo partial derivative (PPD), which is a time‐varying parameter, is used to linearize the nonlinear dynamics of the multivehicle cooperative system by using dynamic linearization technology. Then, a COM‐cMFAC controller is designed. For the case of data packet loss, the proposed controller uses a data compensation mechanism that estimates and compensates for the lost data through the data collected at the previous moment to perform packet loss control. Additionally, the controller considers the constrained input and output problems that will occur in the actual control process and imposes input and output constraints. The main advantage of the COM‐cMFAC algorithm is that the entire control process only needs the input and output data of the multivehicle cooperative system, and it also has a good control effect in the case of packet loss. The stability of the proposed method is verified through strict mathematical analysis, and its effectiveness is verified by semiphysical experiments based on a MATLAB/Simulink and Carsim platform connection environment.

Identification Over Compound Multiple-Input Multiple-Output Broadcast Channels

The identification capacity region of the compound broadcast channel is determined under an average error criterion, where the sender has no channel state information. We give single-letter identification capacity formulas for discrete channels and multiple-input multiple-output Gaussian channels under an average input constraint. The capacity theorems apply to general discrete memoryless broadcast channels. This is in contrast to the transmission setting, where the capacity is only known for special cases, notably the degraded broadcast channel and the multiple-input multiple-output broadcast channel with private messages. Furthermore, the identification capacity region of the compound multiple-input multiple-output broadcast channel can be larger than the transmission capacity region. This is a departure from the single-user behavior of identification, since the identification capacity of a single-user channel equals the transmission capacity.

Consensus Congestion Control for Ad Hoc Networks: Time-Delay and Saturation

This work focuses on the congestion control design for ad hoc networks affected by time-delay and asymmetrical input saturation. First, an innovative general TCP/AQM model for mobile and wireless ad hoc networks (MANET/WANET) is proposed using graph theory and TCP/AQM system dynamics in a dumbbell topology. Next, a new characteristic condition for sector linearity is developed, which plays the role of a specification to be checked and allows us to assume that the dynamics of the nodes are not saturated. Finally, we propose a new consensus-based congestion control protocol while satisfying the input constraints based on the Lyapunov method, the new sector of linearity condition, the Bessel Legendre-based integral inequality, and the reciprocal convex combination. The proposed results have the advantage of being given as a set of linear matrix inequality (LMI) conditions, which can be solved using convex optimization toolboxes. The merits of the theory are evaluated with the help of various real and challenging scenarios with the use of MATLAB and NS3.

Adaptive path following control for wave gliders in ocean currents and waves

In this paper, a design method is presented for path following control of wave gliders (WGs) subject to velocity and rudder input constraints, as well as marine currents and wave disturbances. The control method integrates an adaptive integral line-of-sight (AILOS) guidance law, an adaptive target-path-switching control and a modified incremental PID heading control for WGs with the multi-straight-line path following, which can extremely overcome the influence of such marine currents interference and guarantee path following accuracy. For the control loop, adaptive adjustments on the parameters of the lookahead distance and the error-circle radius are built to provide a current-influenced position error compensation suitable for the time-varying velocity of the WG. Additionally, an online wave filter using an extended Kalman filter (EKF) is developed to address the issue of wave-induced disturbance influence worked on the position and heading of the WG. Further, simulations and sea trials demonstrate both tracking accuracy and high robustness under ocean disturbances with rudder constraints.

PECLOS path-following control of underactuated AUV with multiple disturbances and input constraints

This paper presents a new position error-constrained line-of-sight (PECLOS) path-following (PF) control strategy for an underactuated AUV under multiple disturbances and input constraints. Firstly, considering that many previous studies ignored the influence of stochastic noises, which may degrade the designed controller performance or even lead to instability in practical applications, the multiple disturbances are divided into two categories based on their features, i.e., the slowly time-varying deterministic disturbances and the stochastic noises. On this basis, the time-varying deterministic disturbances are further treated as a single equivalent disturbance and a linear stochastic differential model of underactuated AUV is established by augmenting the equivalent disturbance as an extended state. Secondly, a novel PF control strategy is proposed based on an extended state based Kalman-Bucy filter (ESKBF) and auxiliary dynamic compensators. The PECLOS guidance law is established to maintain the PF errors within the specified time-varying bounds, the ESKBF technology is utilized to estimate and compensate for the equivalent disturbances under the stochastic noises, and the auxiliary dynamic compensators are adopted to overcome the input constraints. Eventually, the stability of the overall closed-loop system is verified. Simulation and comparative analysis are presented to demonstrate the enhanced performance of the presented controller.

Recurrent neural network-based technique for synchronization of fractional-order systems subject to control input limitations and faults

Despite the existence of promising methods for controlling complex systems, there is still a need for further advancement in controlling and synchronizing fractional systems. This is even more monumental when it comes to practical applications with faults and physical constraints present in their control actuators. To address this issue, the current study proposes a new control technique that utilizes a recurrent neural network-based finite-time super-twisting algorithm for fractional-order systems. The proposed controller is enhanced with an intelligent observer to account for faults and limitations that may be present in the control actuator of the fractional-order systems. The proposed method allows the system to be regulated even in the presence of control input constraints and faults. Moreover, the proposed technique guarantees that the closed-loop system will converge in a finite amount of time. The control design is explained in detail, and its finite-time stability is proven. To evaluate the performance of the controller, we applied it to two different fractional-order systems that were subject to control input limitations and faults. The outcomes of the proposed approach were then compared with those obtained using a state-of-the-art technique for fractional-order systems to further validate the effectiveness of our proposed approach.

Assessment of global sorghum production, tolerance, and climate risk

Some cereal crops can withstand harsh growing conditions, and, hence, can be considered an important line of defense against food shortage under climate change. Sorghum (Sorghum bicolor (L.) Monech) is the main food crop for millions of people, especially in sub-Saharan Africa and South Asia. This crop performs well under input constraints (e.g., limited water) and exhibits high resilience to adverse climate conditions (e.g., high temperature, drought, and waterlogging), making it a star crop for combating hunger under climate change. However, sorghum and other similar crops are not tolerant in absolute terms. Climate change could push the growing conditions of these crops beyond the limits they can tolerate, jeopardizing the food security of millions of people around the world. In this research, we analyzed the current status of sorghum production, trends, and factors controlling sorghum yield using empirical approaches. Especially in sub-Saharan Africa, this crop is cultivated widely under unfavorable climate conditions and sub-optimal input levels. Our findings raise multiple concerns as well as highlight opportunities for the sustainability of sorghum cultivation in a future with climate change. Sorghum yield seems to increase persistently despite global warming due to an improved inputs approach, offering hope that similar adaptation approaches can be fruitful, especially in sub-Saharan Africa. A combination of interventions including adequate use of fertilizers and technology adoption (e.g., tolerant cultivars), efficient management (e.g. improved irrigation), and better agronomic practices, is the key to boosting sorghum yield and ensuring the sustainability of this important crop under a warmer climate.

Autonomous Spacecraft Attitude Reorientation Using Robust Sampled-Data Control Barrier Functions

This paper presents a provably safe method for constrained reorientation of a spacecraft in the presence of input constraints, bounded disturbances, and fixed frequency zero-order-hold (ZOH) control inputs. The set of states satisfying all pointing and rate constraints, herein called the safe set, is expressed as the intersection of the sublevel sets of several constraint functions, which are subsequently converted into control barrier functions (CBFs). The method then extends prior results on utilizing CBFs with ZOH controllers to the case of relative-degree-2 constraint functions, as occurs in the constrained attitude reorientation problem. The developed sampled-data controller is also shown to remain provably safe in the presence of input constraints and bounded disturbances. Finally, the method is validated and compared to three prior approaches via both low-fidelity and mid-fidelity simulations.

Observer-based event-triggered optimal control for unknown nonlinear stochastic multi-agent systems with input constraints

This paper addresses the observer-based event-triggered optimal control (ETOC) for unknown nonlinear Ito^-type stochastic multi-agent systems (SMASs) with input constraints. To begin with, the event-triggered stochastic Hamilton-Jacobi-Bellman (HJB) equation with input constraints is presented, and a sufficient criterion on optimal mean-square leader-following consensus of constrained-input SMASs is derived. Next, a novel event-triggered policy iteration algorithm of constrained-input SMASs is designed to obtain the ETOC strategy. Then, an identifier-critic framework is designed where the observer-based identifier network is utilized to recover the knowledge of unknown stochastic dynamics and the constrained-input approximate event-triggered optimal controller is designed via event-triggered adaptive critic designs (ET-ACDs). Moreover, it is proved that the Zeno behavior can be excluded in the sense of expectation. Finally, we present two examples to further verify the validity of the ETOC scheme.

Study on welding buckling evaluation method based on inherent strain theory and eigenvalue buckling analysis

Welding buckling deformation is one of the foremost failure modes of thin plate welded structures. It is important to estimate welding buckling for structural design and process optimization. Inherent strain-based welding buckling evaluation methods have been proved to be valid, but the existing methods have low efficiency due to requiring multiple inherent forces. In this study, a novel welding buckling evaluation method based on longitudinal shrinkage force was established. The effectiveness of the proposed method was validated using a butt welded joint. In addition, this method was applied in a large-scale thin plate structure for evaluating the structural stability, the effects of heat input, welding manner, and external constraints on welding deformation and buckling were also discussed. The results reveal that the proposed inherent strain-based welding buckling evaluation method can be used to judge whether welded structures are buckled. The reduction of welding heat input and the use of single-sided welding can mitigate welding deformation. Increasing external constraints can not only reduce the welding deformation, but also improve the welding critical buckling load for preventing welding buckling.

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.

Finite‐time tracking control of disturbed non‐holonomic systems with input saturation and state constraints: Theory and experiment

AbstractThis article investigates the finite‐time tracking control problem for disturbed non‐holonomic systems with input saturation and state constraints. Input saturation is ensured by utilizing saturated state feedback and designing auxiliary variables. A rigorous design procedure, which combines barrier Lyapunov function‐based backstepping and neural networks, is introduced to satisfy state constraints and overcome the influence of lumped disturbances. A finite‐time filter is developed to address the explosion of complexity problem. Together with relay switching, the designed saturated controller guarantees that the tracking errors converge to arbitrarily small neighbourhoods around zero within a finite time. Stability analysis indicates that all closed‐loop system signals maintain bounded, and the desired input and state constraints are not violated throughout the control process. To demonstrate the effectiveness of the proposed approach, simulation and experimental results on a wheeled mobile robot are presented.

Robust Control Barrier Functions under high relative degree and input constraints for satellite trajectories

This paper presents methodologies for constructing Control Barrier Functions (CBFs) for nonlinear, control-affine systems, in the presence of input constraints and bounded disturbances. More specifically, given a constraint function with high-relative-degree with respect to the system dynamics, the paper considers three methodologies, two for relative-degree 2 and one for higher relative-degrees, for creating CBFs whose zero sublevel sets are subsets of the constraint function’s zero sublevel set Three special forms of Robust CBFs (RCBFs) are developed as functions of the input constraints, system dynamics, and disturbance bounds, such that the resultant RCBF condition on the control input is always feasible for states in the RCBF zero sublevel set. The RCBF condition is then enforced in a switched fashion, which allows the system to operate safely without enforcing the RCBF condition when far from the safe set boundary and allows tuning of how closely trajectories approach the safe set boundary The proposed methods are verified in simulations demonstrating the developed RCBFs in an asteroid flyby scenario for a satellite with low-thrust actuators.

Robust adaptive control for dynamic positioning vehicles in presence of adjustable threshold rule and input constraints

In this work, we present a new class of event-triggered mechanism for dynamic positioning vehicles under input saturation and system uncertainties. The class is characterized by the introduction of an internal dynamic variable, which motivated the proposed name of the dynamic event-triggered mechanism. Different from the traditional event-triggered mechanism with invariable thresholds, the proposed mechanism with the adjustable threshold parameters could reduce communication burden. The internal adjustable threshold parameters are updated online in an adaptive manner. Furthermore, an auxiliary system is employed to resolve the input saturation of actuators. System uncertainty and explosion of complexity are resolved by combining dynamic surface control technique and robust neural damping technique. Due to the virtues of the above design, the proposed algorithm is concise in form and easy to be implemented in the marine practice. Based on the Lyapunov theory, it is proved that the closed-loop system is semi-globally uniform ultimate bounded (SGUUB). Finally, the effectiveness and feasibility of the proposed algorithm are verified by simulation experiments.

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.

Robust Adaptive Composite Learning Integrated Guidance and Control for Skid-to-Turn Interceptors Subjected to Multiple Uncertainties and Constraints

This paper investigates a novel robust adaptive dynamic surface control scheme based on the barrier Lyapunov function (BLF), online composite learning, disturbance observer, and improved saturation function. It is mainly designed for a class of skid-to-turn (STT) interceptor integrated guidance and control (IGC) design problems under multi-source uncertainties, state constraints, and input saturation. The serial-parallel estimation model used in this study estimates the system states and provides “critic” information for the neural network and disturbance observer; then, these three are combined to realize online composite learning of the multiple uncertainties of the system and improve the interception accuracy. In addition, the state and input constraints are resolved by adopting the BLF and the improved saturation function, while the design of the auxiliary system ensures stability. Finally, a series of simulation results show that the proposed IGC scheme with a direct-hit intercept strategy achieves a satisfactory effect, demonstrating the validity and robustness of the scheme.