Output Constraints Research Articles

Zero-sum game-based formation control for multi-USVs under hybrid irregular output constraints

Zero-sum game-based formation control for multi-USVs under hybrid irregular output constraints

A vector-type finite-time output-constrained control algorithm and its application to a mobile robot

Purpose The purpose of this paper is to investigate a vector-type finite-time control strategy with output constraints for first-order and second-order systems. Under the proposed controller, the system can be stabilized within a finite time and the output of the system is always maintained within the prescribed constraint bounds. Design/methodology/approach First, to prevent constraint violations, a piecewise barrier Lyapunov function (BLF) is formulated, which grows to infinity as its arguments approach prescribed limits. By embedding the BLF as a parameter into the design of finite-time controller, the constraints are ensured not to be violated. However, this resulted in a closed-loop system with coupled states, posing challenges for finite-time stability analysis. To tackle this issue, this paper introduces a vector-type fractional power function by skillfully leveraging the sign function, which constructs a continuous vector-type finite time controller with coupled state-dependent gains. Finally, model-based approaches are used to complete the theoretical analysis. It is demonstrated that the proposed controller effectively enables the system to achieve finite-time state convergence while adhering to output constraints. Findings By using a piecewise BLF and using the vector-type fractional power function, a continuous vector-type finite-time controller with coupled state-dependent gains can be constructed for both first-order and second-order systems. This allows the systems to achieve finite-time stabilization while satisfying output constraints. It is worth mentioning that the developed algorithm can be extended to the trajectory tracking of a mobile robot. Originality/value Compared with existing results, the main contributions presented in this paper are: (i) the proposed control scheme guarantees the closed-loop system converges within a finite time. (ii) The introduction of state-dependent gain penalty term effectively tackles the difficulty in ensuring the satisfaction of output constraints under coupled conditions. (iii) The vector-type fractional power function provides a more flexible and powerful tool for the design and analysis of the closed-loop system.

Disturbance Observer-Based Neural Network Control of a 2-DOF Helicopter System With Input Saturation and Output Constraints

Disturbance Observer-Based Neural Network Control of a 2-DOF Helicopter System With Input Saturation and Output Constraints

Prescribed-Time Fuzzy Optimal Containment Control for Multiagent Systems With Deferred Output Constraints: An Output Mask Method

Prescribed-Time Fuzzy Optimal Containment Control for Multiagent Systems With Deferred Output Constraints: An Output Mask Method

Discussion on Distribution Network Operating Envelope for Providing Allowable Active and Reactive Power Injections

Introduction: The high-proportion distributed energy resource (DER) connection in the distribution network deteriorates the condition of voltage violation when DERs control their power output arbitrarily. The operating envelope of distribution networks can decouple the safe operation of distribution networks from the regulation of distributed energy resources and map the voltage constraints of distribution networks into DER output constraints. Methods: This provides an effective means to solve the problem of different ownership entities and regulation objectives between distribution networks and DER. Existing researches mainly focus on the active power operating envelopes while ignoring the reactive power. This paper studies the calculating method for the active-reactive power operating envelope of distribution networks. First, the rectangle model of active-reactive power operating envelopes is constructed to calculate the operating envelopes that provide more flexibility for DER control, while satisfying the voltage constraints robustly and meeting the actual demands in the operation of DER control. Second, the area that the active-reactive operating envelope covers is enlarged by finding a new vertex on the right side of the p-q plane to generate a pentagon operating envelope which is called the expanded operating envelope. Results: This leads to a higher active power output limit for DER control. Conclusion: Finally, the effectiveness and safety of the proposed calculating method for distribution network operating envelopes is verified in distribution networks with different sizes

Memory‐Based Sampled‐Data Control Scheme for Vehicle Seat Suspension System With Actuator Faults via a Looped Lyapunov Approach

ABSTRACTThis paper addresses the problem of controlling the Vehicle Seat Suspension System (VSSS) under actuator faults using a memory‐based sampled‐data control (MSDC) strategy, incorporating looped Lyapunov functions. While previous works have focused on suspension control or actuator fault tolerance individually, few have combined memory‐based control with fault‐tolerant strategies specifically for Vehicle Seat Suspension Systems (VSSS). This study fills this gap by constructing a 3‐Degrees of Freedom (DOF) seat suspension model and designing a novel memory‐dependent sampled‐data controller. The proposed method improves stability and vibration control by incorporating memory parameters and handling disturbance attenuation and output constraints through control theory. Simulation results, validated under various road conditions, show significant improvements in ride comfort, road holding, and vehicle handling, outperforming traditional control strategies and also the Root Mean Square (RMS) values indicate significant reduction in vibration.

A Novel Adaptive Iterative Learning Control Approach for Nonlinear Output‐Constrained Systems With Input Quantization

ABSTRACTIn practice, the communication bandwidth and physical limitations are the two main categories of threats that the networked control systems may encounter. Therefore, this paper focuses on adaptive iterative learning control (ILC) of nonlinear strict‐feedback systems with input quantization and asymmetric output constraint. Through constructing an error transformation mechanism, the original output‐constrained control system is converted into the unconstrained form. Subsequently, a novel adaptive ILC algorithm is established by virtue of the command filtered backstepping technique, in which the uncertain terms of Lyapunov function (LF) are decomposed into the parameter compensation components of the controller and the iteration‐convergent lumps via the hyperbolic tangent function. Specially, to accommodate the input‐related uncertainties brought by quantizer, the devised ILC law adapts a nested structure, thus achieving the estimation of unknown parameters associated with the quantized bias. The convergence of error along the iteration axis is rigorously proven by the composite energy function (CEF). Finally, the proposed approach is applied to two examples, the results of which illustrate the effectiveness of the scheme.

Fixed‐Time Adaptive Tracking Control for High‐Order Nonlinear Systems With Asymmetric Output Constraints

ABSTRACTThis article proposes a fixed‐time adaptive tracking control strategy for a class of high‐order non‐strict feedback nonlinear systems with asymmetric output constraints. The key technique of this strategy is to guarantee that the output of the system does not break the pre‐set constraints through the barrier Lyapunov function (BLF) while approximating any unknown nonlinear term through the fuzzy logic systems (FLSs). We design a fixed‐time fuzzy adaptive controller to ensure that the closed‐loop error signal converges to a tunable neighborhood near zero in a fixed‐time instant. Compared with the existing results, the fixed‐time adaptive control is realized and the semi‐global fixed‐time stability of the closed‐loop system is proved theoretically. Finally, the effectiveness of the proposed scheme is verified by a numerical simulation.

Finite-time adaptive output feedback stabilization of uncertain switched nonlinear systems with time-varying output constraints

Finite-time adaptive output feedback stabilization of uncertain switched nonlinear systems with time-varying output constraints

Neural-Network-Based Adaptive Fixed-Time Control for a 2-DOF Helicopter System With Input Quantization and Output Constraints.

This study proposes a neural-network (NN)-based adaptive fixed-time control method for a two-degree-of-freedom (2-DOF) nonlinear helicopter system with input quantization and output constraints. First, a hysteresis quantizer is employed to mitigate chattering during signal quantization, and adaptive variables are utilized to eliminate errors in the quantization process. Subsequently, the system uncertainties are approximated using a radial basis function NN. Simultaneously, a logarithmic barrier Lyapunov function (BLF) is constructed to prevent the system outputs from violating the constraint boundaries. Based on a rigorous Lyapunov stability analysis and the fixed-time stability criterion, the signals of the closed-loop system are proven to be bounded within a fixed time. Finally, numerical simulations and experiments verified the feasibility of the proposed method.

Adaptive path following control for autonomous surface vehicles towed by a single tugboat with output constraints.

Adaptive path following control for autonomous surface vehicles towed by a single tugboat with output constraints.

Fixed‐Time Adaptive Tracking Event‐Based Control for Uncertain Stochastic Nonlinear Systems With Output Constraints

ABSTRACTThis paper focuses on an adaptive fixed‐time tracking control problem for nonstrict‐feedback stochastic nonlinear systems with unknown control coefficients and output constraints. To overcome this problem, this article introduces a unified system transformation function to facilitate the transition of the system from constrained to unconstrained states. Subsequently, a fuzzy approximation principle is utilized for handling unknown terms in the transformed system, while Nussbaum functions are employed to manage unknown control directions. Then, combined with the backstepping technique, an adaptive fixed‐time tracking controller is designed with an event‐triggered mechanism, which not only achieves the convergence of tracking errors within a limited time independent of initial states, but also conserves resources. Under the proposed tracking controller, practical fixed‐time stability can be ensured for the closed‐loop system. Finally, two simulation examples demonstrate the efficacy of the proposed scheme.

Barrier Functions‐Based Adaptive Integral Terminal Sliding Mode Control for Output‐Constrained Uncertain Nonlinear Systems

ABSTRACTHerein, a new barrier function (BF)‐based adaptive integral terminal sliding mode control (ABF‐ITSMC) with output constraints methodology is proposed for high‐order nonlinear systems (HONSs) exposed to unknown lumped disturbance. The proposed algorithms utilize the backstepping control (BSC) technique for management of high‐order mismatched uncertainties by incorporating the dynamic surface control (DSC) via first order low pass filter (FOLPF) to eliminate “complexity explosion.” To deal with the output constraints requirement, a barrier Lyapunov function is introduced for the design of the virtual control law. Besides, an integrate sliding manifold surface is designed to obtain the finite time convergent and singularity free features to enhance the robustness. Additionally, an adaption control gain law is devised by BF to precisely estimate the real‐time lumped disturbance without the requirement of any boundary information. Lyapunov stability theory is employed for proving that the uniformly and eventually bounded tracking deviation of the overall system. Lastly, simulations with two cases are conducted to validate the devised method with respect to the strength and effectiveness by comparing the derived outcomes of the method with that of the existing method.

Output-feedback stabilisation of output-constrained high-order nonlinear systems with high-order and low-order nonlinearities

This paper investigates output-feedback stabilisation of n-dimensional output-constrained high-order nonlinear systems with high-order and low-order nonlinearities. By constructing new nonlinear mappings, a crucial coordinate transformation, integral Lyapunov functions and an observer, skillfully combining adding a power integrator technique and introducing a novel analysis method, a continuous output-feedback controller is designed to guarantee that all the closed-loop signals are uniformly bounded, output constraint is not transgressed, and the equilibrium point of the closed-loop system is uniformly asymptotically stable. A simulation example shows the effectiveness of this control scheme.

Adaptive output-constrained system with zone barrier Lyapunov function

It is essential to implement effective compliance control measures to ensure the safe and reliable operation of robots and automated systems. In this paper, zone barrier Lyapunov function is presented to the practical nonlinear system with adaptive fuzzy neural network control design. Output-constraint is considered by zone barrier Lyapunov function (zBLF), which is designed to enforce safety constraints and offer flexibility when the system states remain within a safe region of operation, even in the presence of nonlinear uncertainties. Adaptive fuzzy neural control is adopted to approximate the nonlinear uncertainties for practical stability analysis and design, which aims to adjust the control parameters online according to the system's behaviour. The fuzzy control based on zBLF symmetric output constraint is further extended to the asymmetric case. The output state converges to the trajectory and moves freely in a region around the origin, and then it is demonstrated the developed design method guarantees the boundedness of the states of a closed-loop system. Moreover, the system's residual set for each condition can be identified respectively. The simulation results on euler-lagrange system are to validate the effectiveness of the proposed scheme.

Neuro‐Observer Based Adaptive Fixed‐Time Fault‐Tolerant Control for Uncertain Teleoperation System With Input Saturation and Asymmetric Time‐Varying Output Constraints

Neuro‐Observer Based Adaptive Fixed‐Time Fault‐Tolerant Control for Uncertain Teleoperation System With Input Saturation and Asymmetric Time‐Varying Output Constraints

Zero-Error Prescribed-Time Tracking Control for Switched Non-Square MIMO Nonlinear Systems Subject to Asymmetric Time-Varying Output Constraints

Previous studies typically assume that output constraints are symmetric or time-invariant. However, effectively addressing asymmetric and time-varying output constraints remains an unsolved issue, especially in the case of switched non-square multi-input multi-output (MIMO) nonlinear systems. To tackle this challenge, this paper first establishes a prescribed-time (PT) Lyapunov criterion for switched nonlinear systems. Second, an asymmetric nonlinear mapping (ANM) method is proposed to handle asymmetric time-varying output constraints. Compared to the barrier Lyapunov function (BLF) approach, the ANM method relaxes the initial output conditions and the constraint functions are not necessarily required to be opposite in sign. Finally, a PT tracking controller is designed for a class of switched MIMO nonlinear systems with a non-square control coefficient matrix by using the time-varying gain technique. This controller achieves zero-error tracking within the prescribed time while ensuring that output constraints are satisfied. The effectiveness of the proposed control strategy is validated through numerical and simulation examples.

A Comprehensive Survey on Advanced Control Techniques for T-S Fuzzy Systems Subject to Control Input and System Output Requirements

This paper provides a comprehensive survey on advanced control techniques for Takagi-Sugeno (T-S) fuzzy systems that are subject to input and output performance constraints. The focus is on addressing practical applications, such as actuator saturation and output limits, which are often encountered in industries like aerospace, automotive, and robotics. The paper discusses key control methods such as model predictive control, anti-windup compensators, and Linear Matrix Inequality (LMI)-based control, emphasizing their effectiveness in handling input and output constraints. These techniques ensure system stability, robustness, and performance even under strict physical limitations. The survey also highlights the importance of T-S fuzzy systems, which provide a flexible framework for modeling and controlling nonlinear systems by breaking them down into simpler linear models. Additionally, recent developments in robust and adaptive control strategies are explored, particularly in handling time delays, disturbances, and uncertainties. These methods are crucial for real-time applications where the system must remain stable and safe despite unmeasured states or external disturbances. By reviewing these advanced techniques, the paper aims to identify research gaps and future directions, particularly in scalable solutions and integrating data-driven approaches with T-S fuzzy control frameworks.

Finite-time Control Design of Flexible Risers with Input Saturation Constraints

To control the vibration of flexible riser systems affected by uncertain perturbation, this paper utilizes the transverse vibration control equations of marine flexible risers established based on Hamiton's principle and conducts a controlled study by combining the problems of input saturation and output limitation that exists in the practical engineering applications. A finite time control algorithm for flexible risers with input saturation constraints and output constraints is proposed. The barrier Lyapunov function is used to limit the end of the flexible riser to a given range, and the hyperbolic tangent function is introduced to deal with the saturation nonlinearity. The stability of the system is proved using the Lyapunov stability theory. Finally, the superiority of the control algorithm proposed in this paper is illustrated with simulation examples.

Adaptive global predefined-time control of robotic systems with output constraints via multiple multidimensional Taylor network.

Adaptive global predefined-time control of robotic systems with output constraints via multiple multidimensional Taylor network.