Prescribed Performance Control Research Articles

Prescribed Performance Spacecraft Attitude Control with Multiple Convergence Rates

This paper proposes a prescribed performance control policy for spacecraft attitude tracking control. The designed controller employs a kind of performance envelope function that can configure the convergence rate arbitrarily. Moreover, the controller design is based on the barrier Lyapunov function and indirect adaptive methods, which can suppress the fluctuation of external disturbances to obtain lower steady-state errors. Compared to prior studies on spacecraft attitude tracking, the proposed policy can arbitrarily configure the system convergence performance to achieve performance trade-offs in different scenarios. Moreover, the barrier Lyapunov function design and adaptive methods can achieve low steady-state errors with low computational resource consumption. Simulations that verify its effectiveness and superiority are provided herein.

Nonlinear prescribed performance sliding mode control of hypersonic vehicles

SummaryThis paper addresses the adaptive saturation prescribed performance control problem for air‐breathing hypersonic vehicles with parameter uncertainties and unknown external disturbances. First of all, different from the traditional performance function, a novel class of performance functions is presented without the initial state information, which does not require to reset parameters even if the initial velocity and altitude change. To ensure the successful design of the velocity controller and altitude controller, the constrained errors are transformed into the unconstrained errors by the projection technique. Secondly, the adaptive neural network and the sliding mode control techniques are combined to design the adaptive neural network sliding mode controllers, which guarantee that the velocity and altitude tracking errors can respectively approach the predetermined regions within the identical preset finite time. Finally, the effectiveness of the designed controllers is verified by an example with comparisons.

Adaptive fuzzy neural network-based finite time prescribed performance control for uncertain robotic systems with actuator saturation

Adaptive fuzzy neural network-based finite time prescribed performance control for uncertain robotic systems with actuator saturation

Full prescribed performance trajectory tracking control strategy of autonomous underwater vehicle with disturbance observer

This paper investigates trajectory tracking control of the Autonomous Underwater Vehicle (AUV) with the general uncertainty consisting of model uncertainties and unknown ocean current disturbances. A full prescribed performance control strategy based on disturbance observer is developed, which ensures that the tracking error, the velocity error, and the observation error are all constrained. First, under the case of unmeasurable AUV acceleration, a fixed-time observer is constructed to estimate the general uncertainty, which constrains the observation error within the prescribed accuracy by a prescribed performance observer. Then, based on the performance function and corresponding error transformation, a prescribed performance protocol is designed to realize the trajectory tracking control, so that the observation error, the tracking error, and the velocity error are all constrained within the prescribed accuracy range. Simulation results demonstrate the efficiency of the full prescribed performance control strategy while the AUV tracking control with full state constraints is feasible. Moreover, compared with the other two relevant works, this study improves the observation performance by at least 10 %, both in case of deepwater disturbances and near-surface disturbances.

Switching periodic event‐triggered global prescribed performance control of uncertain strict‐feedback systems with sensor faults

AbstractThis study investigates the global adaptive prescribed performance control (PPC) of a class of uncertain strict‐feedback nonlinear systems with sensor faults based on the switching periodic event‐triggering mechanism (SPETM). Due to the existence of sensor faults, the controlled system is first remodeled by utilizing the available variables. To reduce the communication frequency, a novel SPETM is proposed by combining the advantages of the static and the dynamic event‐triggering mechanisms. This mechanism can not only avoid continuously monitoring the event‐triggering condition and avoid the Zeno phenomenon in mechanism, but also reduce the trigger frequency while ensuring the system performance by adjusting the event‐triggering threshold dynamically. Meanwhile, a time‐varying scaling function, whose reciprocal is considered as a prescribed performance function, is designed to achieve the global PPC by combining the nonlinear transformation technique. The adaptive control algorithm design is completed by employing the backstepping methodology, which can guarantee that all closed‐loop signals are bounded and the actual system output signal evolves within the prescribed performance boundary for arbitrary initial values. The effectiveness and the advantages of the proposed control algorithm are illustrated through an application example of the network‐based robotic manipulator system.

Correction to: Finite-time prescribed performance control for approaching non-cooperative target’s feature surface

Correction to: Finite-time prescribed performance control for approaching non-cooperative target’s feature surface

Nonlinear disturbance observer-based finite-time prescribed performance control for MSVs with input saturation

ABSTRACT A finite-time prescribed performance control scheme based on an improved nonlinear disturbance observer is proposed for tracking the trajectory of marine surface vessels (MSVs) under the disturbance of the marine environment. The objective of this paper is to design a nonlinear disturbance observer based on state estimation error feedback that accurately estimates and compensates for the unknown disturbance problem. An adaptive law is applied to estimate the upper bound of its error, removing the requirement of a priori knowledge of the unknown lumped disturbance upper bound. This paper also introduces a non-logarithmic performance function in order to efficiently solve the problem of considering both transient and steady state MSVs performance simultaneously. In addition, the adaptive dynamic system is introduced to overcome the adverse effects caused by nonlinear saturation factors for the input-constrained problem. With this control strategy, the system output error converges to a pre-defined region within a finite amount of time and meets the prescribed performance requirements. Finally, simulation and comparison verify the control scheme's effectiveness and superiority.

Adaptive neural fault-tolerant prescribed performance control of a rehabilitation exoskeleton for lower limb passive training

This article studies the passive tracking problem of a wearable exoskeleton for lower limb rehabilitation therapy in the face of unmodeled dynamics, interactive friction, disturbance, prescribed performance constraints, and actuator faults. Adaptive neural networks and a smooth performance function are incorporated to establish a novel fault-tolerant tracking scheme, which can not only compensate for the nonlinear uncertainties and disturbance, but also handle the actuator fault with guaranteed tracking performance. A state feedback controller is presented by using the full state information and an output feedback controller is developed when the angular velocity is unavailable. The differential explosion issue of the backstepping technique is resolved by constructing a first-order filter and the unmeasurable velocity is estimated by a nonlinear observer. Semiglobal uniform boundedness stabilities of the exoskeleton system are proved via the Lyapunov direct method. The tracking performances of the designed control approaches are tested by comparative simulations.

Contact Detumbling Toward a Nutating Target Through Deformable Effectors and Prescribed Performance Controller

Contact Detumbling Toward a Nutating Target Through Deformable Effectors and Prescribed Performance Controller

Neural Network Control of Underactuated Surface Vehicles With Prescribed Trajectory Tracking Performance.

This article is concerned with the fast and accurate trajectory tracking control problem for a sort of underactuated surface vehicle under model uncertainties and environmental disturbances. A novel neural networks (NNs)-based prescribed performance control strategy is proposed to solve the problem. In the control design, a new type of performance function is constructed which provides a way to predefine the settling time and accuracy, straightforward. Then, a pair of barrier functions are employed to combat not only the position error but also the virtual control input. This evades the possible singularity or discontinuity of the control solution. Next, an initialization technique is exploited, removing the requirement for the initial condition of the control system. Finally, two NNs are employed to deal with the unknown ship nonlinearities. The performance analysis not only demonstrates the effectiveness of the proposed approach but also reveals its robustness against disturbances and unknown reference trajectory derivatives. There is, thus, no need to acquire such knowledge or employ specialized tools to handle disturbances. The theoretical findings are illustrated by a simulation study.

Prescribed Performance Control for the Lower Limb Exoskeleton With Time-varying State Constraints and Input Saturation

Prescribed Performance Control for the Lower Limb Exoskeleton With Time-varying State Constraints and Input Saturation

Zero‐sum game‐based H∞ optimal finite‐time prescribed performance control for nonlinear multiagent systems with actuator faults

AbstractThis article proposes an optimal leader‐follower consensus control protocol with finite‐time prescribed performance for nonlinear multiagent systems suffering from the actuator fault based on the zero‐sum game framework. First, to guarantee the specified performance of the system within a finite time frame, a novel error transformation function (ETF) is constructed. The ETF proposed in this paper can adjust the mapping range of error transformation to achieve better error convergence performance, thereby enhancing the flexibility of the controller design. Then, to compensate for the negative impact of actuator fault, an error‐related item is separated from the optimal performance index function to design an online fault estimator, which can obtain a better error estimation accuracy. Furthermore, to lessen the need for difficult‐to‐verify persistent excitation signal in adaptive dynamic programming algorithm, both historical and real‐time data are applied to adjust the updating law of neural network weight. Moreover, it demonstrates that the signals within the closed‐loop system are bounded and the expected finite‐time prescribed performance can be ensured in the presence of faults. Finally, a numerical example of multiple single‐link robots manipulator system is executed to validate the efficacy of the developed scheme.

Discrete-time sliding mode prescribed performance controller via Kalman filter and disturbance observer for load following of a pressurized water reactor

In practical implementations, most nuclear reactors employ digital controllers to accomplish the load following operation. However, most of the pre-existing works only focus on continuous time controller design, which may yield degraded control performance when the control strategy is employed by a digital controller owing to the finite sampling frequency and the inevitable discrete-time approximations. To this end, this paper proposes a discrete-time sliding mode prescribed performance controller (DTSMPPC) via Kalman filter (KF) and disturbance observer (DO) for a pressurized water reactor (PWR), aiming to achieve an easier implementation on the practical nuclear reactor systems. Specifically, the continuous mathematical model of the PWR system with consideration of model errors and disturbances is first transformed to a discrete-time form by virtue of Euler’s discretization technique. Then, based on this transformed discrete-time model, the KF and DO, which provide the estimates of unmeasured system states (relative density of delayed neutron precursor, average fuel temperature, xenon concentration, and iodine concentration) and uncertainties, respectively, are constructed using the input/output measurement information acquired from the nuclear reactor system merely. By incorporating the observed information provided by the KF and DO into the control framework, the proposed DTSMPPC enables the load following error to fulfill the accurate and robust performance requirement even in the presence of unmeasured system states and uncertainties, while at the same time ensuring the transient and steady-state load following error within a prescribed zone described by performance functions. The system stability and the aforementioned theoretical findings are proved through rigorous analysis and simulations. Simulation results also reveal that the proposed DTSMPPC via the KF and DO yields a superior load following performance compared with some previous control strategies.

Event-based appointed-time cooperative formation for linear multiagent systems with actuator saturation

This paper considers the time-varying formation (TVF) tracking issue of a class of multiagent systems (MASs) with input saturation. Meanwhile, the prescribed transient and steady-state performance of MASs is guaranteed. Compared to the existing prescribed performance control (PPC) approaches, the maximum convergence time of the designed algorithm is preassigned. To save network resources, an event-based protocol is introduced to achieve lower information communication frequency, where Zeno behavior is excluded. Furthermore, an improved control strategy is employed where an auxiliary system is designed to improve the performance under actuator saturation. According to Lyapunov stability theory, the expected practical TVF tracking control problem is solved for the MASs. Finally, the effectiveness of the designed schemes is demonstrated by the simulation examples.

Pneumatic servo position control optimization using adaptive-domain prescribed performance control with evolutionary mating algorithm

Pneumatic servo systems face challenges such as friction, compressibility, and nonlinear dynamics, necessitating advanced control techniques. Research suggests model-based, model-free, hybrid, and optimization-based methods have their strengths. Therefore, this study presents an optimal control strategy using Adaptive Domain Prescribed Performance Control (AD-PPC) cascaded with PID and optimized using the Evolutionary Mating Algorithm (EMA) for a pneumatic servo system (PSS). The goal is to achieve faster transient control and stable rod-piston positioning with minimal friction through the hysteresis phenomenon of the targeted proportional valve-controlled double-acting pneumatic cylinder (PPVDC) representing the PSS. The novel EMA optimizes the cascaded controller based on the tracking error as its objective function. Simulation studies verify the proposed AD-PPC-PID controller with the PPVDC model plant, iteratively optimized by the EMA. The analytical study compares this setup's control system and optimization model with the same control system model using alternative optimization methods. The testing employs step and multi-step signals for PPVDC's rod-piston position input. Results show that the EMA-tuned AD-PPC-PID outperforms AD-PPC-PID controller with other optimizers. For both input trajectory tests, EMA-tuned AD-PPC-PID shows faster response times, with average improvements of 30 % in settling times and 70 % in tracking performance metrics compared to other optimizers, making it robust for nonlinear system applications like PPVDC rod-piston positioning.

Prescribed Performance Fault-Tolerant Attitude Tracking Control for UAV with Actuator Faults

This paper proposes a prescribed performance fault-tolerant control based on a fixed-time extended state observer (FXTESO) for a carrier-based unmanned aerial vehicle (UAV). First, the attitude motion model of the UAV is introduced. Secondly, the proposed FXTESO is designed to estimate the total disturbances including coupling, actuator faults and external disturbances. By using the barrier Lyapunov function (BLF), it is proved that under prescribed performance control (PPC), the attitude tracking error is stable within the prescribed range. The simulation results for tracking the desired attitude angle show that the average overshoot and stabilization time of PPC-FXTESO is 0.00455rad and 6.2s. Comparatively, the average overshoots of BSC-ESO and BSC-FTESO are 0.035rad and 0.027rad, with stabilization times of 14.97s and 12.56s, respectively. Therefore, the control scheme proposed in this paper outperforms other control schemes.

Decentralized adaptive fault‐tolerant control for interconnected nonlinear time‐delay systems with uniform prescribed performance

AbstractIn this article, the prescribed performance tracking control problem is studied for interconnected nonlinear time‐delay systems with sensor and actuator faults. Based on the dynamic gain technique and the backstepping control method, a delay‐independent adaptive state feedback controller is designed. Besides, by embedding a smooth function into the controller, the unknown external disturbances, sensor and actuator faults are successfully compensated. Furthermore, we can deal with the asymmetric prescribed performance control with semi‐global or global requirements uniformly by selecting the initial value of the the scaling function. Based on Lyapunov theory, it is proved that the system output of each subsystem can closely track the desired signal and the boundedness of all signals in the closed‐loop systems can be ensured under the proposed control method. Finally, the effectiveness of the proposed control strategy is proved by two simulation examples.

Fixed-time observation and predefined performance regulation for oxygen excess ratio of vehicular fuel cells under input saturation

The efficiency, health and lifetime of vehicle-mounted fuel cells highly rely on the oxygen excess ratio (OER). Faced with urgent research limitations, this article tackles the estimation and regulation for immeasurable OER, while suppressing the influences of dramatic stack current, uncertain system information, actuator saturation and performance constraint. Restricted by high cost and measurement difficulty, two fixed-time adaptive observers are proposed to rebuild unavailable OER and lumped disturbance respectively, which eliminate the dependence of estimation time on initial states and alleviate the peaking issue. Further, a fixed-time prescribed performance controller is developed to elevate the tracking performance for the estimated OER, in which the overshoot, regulation time and convergence region can be quantitatively constrained, regardless of initial conditions. Specially, to exclude undesired control singularity, an improved performance function is established to automatically adjust the predefined constraint boundaries depending on the captured variations of stack current and anti-saturation auxiliary signal. The hardware-in-loop validation results under custom working conditions and a typical vehicle driving cycle exhibit that the estimation and regulation of OER reach stability within 0.2s and 1s respectively, besides, the maximum absolute errors in observing and controlling the OER are limited to around 0.6 and 0.8 respectively, with great adaptability to different scenarios.

Robust Fixed-Time Fault-Tolerant Control for USV with Prescribed Tracking Performance

The unmanned surface vessel (USV) is an emerging marine tool with its advantages of automation and intelligence in recent years; the good trajectory tracking performance is an important capability. This paper proposes a novel prescribed performance fixed-time fault-tolerant control scheme for an USV with model parameter uncertainties, unknown external disturbances, and actuator faults, based on an improved fixed-time disturbances observer. Firstly, the proposed observer can not only accurately and quickly estimate and compensate the lumped nonlinearity, including actuator faults, but also reduce the chattering phenomenon by introducing the hyperbolic tangent function. Then, under the framework of prescribed performance control, a prescribed performance fault-tolerant controller is designed based on a nonsingular fixed-time sliding mode surface, which guarantees the transient and steady-state performance of an USV under actuator faults and meets the prescribed tracking performance requirements. In addition, it is proved that the closed-loop control system has fixed-time stability according to Lyapunov’s theory. Finally, upon conducting numerical simulations and comparing the proposed control scheme with the SMC and the finite-time NFTSMC scheme, it is evident that the absolute error tracking performance index of the proposed control scheme is significantly lower, thus indicating its superior accuracy.

Identification and Control of Flexible Joint Robots Based on a Composite-Learning Optimal Bounded Ellipsoid Algorithm and Prescribe Performance Control Technique

This paper presents an indirect adaptive neural network (NN) control algorithm tailored for flexible joint robots (FJRs), aimed at achieving desired transient and steady-state performance. To simplify the controller design process, the original higher-order system is decomposed into two lower-order subsystems using the singular perturbation technique (SPT). NNs are then employed to reconstruct the aggregated uncertainties. An adaptive prescribed performance control (PPC) strategy and a continuous terminal sliding mode control strategy are introduced for the reduced slow subsystem and fast subsystem, respectively, to guarantee a specified convergence speed and steady-state accuracy for the closed-loop system. Additionally, a composite-learning optimal bounded ellipsoid algorithm (OBE)-based identification scheme is proposed to update the NN weights, where the tracking errors of the reduced slow and fast subsystems are integrated into the learning algorithm to enhance the identification and tracking performance. The stability of the closed-loop system is rigorously established using the Lyapunov approach. Simulations demonstrate the effectiveness of the proposed identification and control schemes.