Update Law Research Articles

Novel adaptive boundary control for synchronization of fractional fuzzy Cohen-Grossberg BAM neural networks with reaction-diffusion and time-varying delay

ABSTRACT The boundary synchronization problem for a class of fractional fuzzy Cohen-Grossberg BAM neural networks (FFCGBAMNNs) with reaction-diffusion and time-varying delay is studied. Novel adaptive boundary controllers, Neumann boundary conditions, and fuzzy feedback MIN and MAX templates of nonlinear dynamic fuzzy modeling are employed. Based on the adaptive boundary controllers with fractional adaptive updated laws and the appropriate Lyapunov-Krasovskii functional (LKF), new sufficient criteria are derived to ensure that the drive-response FFCGBAMNNs achieve asymptotic and finite-time boundary synchronization in terms of the linear matrix inequality (LMI). Based on these sufficient criteria, the effects of adaptive boundary controller on asymptotic and finite-time stability are analyzed. Finally, numerical simulations are presented to illustrate the feasibility of the theoretical results.

Adaptive Position/Force Controller Design Using Fuzzy Neural Network and Stiffness Estimation for Robot Manipulator

AbstractThis paper proposes an adaptive hybrid position/force control approach using fuzzy neural networks (FNNs) for a robot manipulator with joint friction compensation. The dynamics model and system uncertainties are estimated by FNNs. For force tracking control, an adaptive impedance controller is employed with an online stiffness estimator, wherein the stiffness of the contacted environment is estimated using a gradient descent algorithm. The adaptive update laws of the FNNs and the stability of the controller are obtained using the Lyapunov stability theorem. Finally, the proposed adaptive hybrid controller is implemented on the AR605, a 6-axis articulated robot manipulator manufactured by the Industrial Technology Research Institute (ITRI). The effectiveness and robustness of the proposed control strategies are verified by the simulation and experimental results.

Momentum-Based Adaptive Laws for Identification and Control

In this paper, we develop momentum-based adaptive update laws for parameter identification and control to improve parameter estimation error convergence and control system performance for uncertain dynamical systems. Specifically, we introduce three novel continuous-time, momentum-based adaptive estimation and control algorithms and evaluate their effectiveness via several numerical examples. Our proposed adaptive architectures show faster parameter convergence rates as compared to the classical gradient descent and model reference adaptive control methods.

Optimal Adaptive Tracking Control of Partially Uncertain Nonlinear Discrete-Time Systems Using Lifelong Hybrid Learning.

This article addresses a multilayer neural network (MNN)-based optimal adaptive tracking of partially uncertain nonlinear discrete-time (DT) systems in affine form. By employing an actor-critic neural network (NN) to approximate the value function and optimal control policy, the critic NN is updated via a novel hybrid learning scheme, where its weights are adjusted once at a sampling instant and also in a finite iterative manner within the instants to enhance the convergence rate. Moreover, to deal with the persistency of excitation (PE) condition, a replay buffer is incorporated into the critic update law through concurrent learning. To address the vanishing gradient issue, the actor and critic MNN weights are tuned using control input and temporal difference errors (TDEs), respectively. In addition, a weight consolidation scheme is incorporated into the critic MNN update law to attain lifelong learning and overcome catastrophic forgetting, thus lowering the cumulative cost. The tracking error, and the actor and critic weight estimation errors are shown to be bounded using the Lyapunov analysis. Simulation results using the proposed approach on a two-link robot manipulator show a significant reduction in tracking error by 44% and cumulative cost by 31% in a multitask environment.

Velocity-free containment control with binary relative measurements only

This paper introduces a containment control law for a multi-agent system (MAS) of double integrator agents using only relative position information. Unlike existing methods, this approach eliminates the need for velocity or exact position measurements as feedback, reducing communication and sensor requirements. Using the non-smooth Lyapunov analysis, we prove the convergence of the proposed control law under any detailed-balanced network with any positive gains. Moreover, the controller inherently guarantees bounded control effort and finite-time or fixed-time convergence without needing any additional update law. Finally, the numerical simulations demonstrate the effectiveness of the proposed method.

Type-2 fuzzy adaptive robust fault-tolerant control for manipulators with disturbance observer

A configuration of interval type-2 (IT2) fuzzy adaptive fault-tolerant control strategy is designed for the position trajectory tracking of cooperated robot manipulators carrying a common object. First, a non-zero time-varying parameter is installed in IT2 FLS, then a property of universal approximation of IT2 fuzzy logic system (FLS) is proposed, where the non-zero time-varying parameter has the relation with the approximation precision of IT2 FLS. Second, for the unknown compounded disturbance, a disturbance observer is designed to estimate it and is integrated into the IT2 adaptive control. To improve the approximation accuracies, the novel IT2 FLSs with non-zero parameter are utilized to compensate for the uncertain nonlinear terms with including the uncertainties and fault components in the system, the fault-tolerant control with some adaptive laws are established using the stability theory of barrier Lyapunov function (BLF), where the approximation accuracies can be modified online using the provided adaptive laws with neglecting the number of fuzzy rules, the computation burden of the proposed control is greatly reduced because of a small number of updated laws. Finally, the simulation results are compared with those of several existing control methods, which confirms the validity of the designed control approach.

Smooth adaptive finite-time tracking for uncertain time-varying systems

This paper addresses the adaptive finite-time tracking control problem of strict-feedback nonlinear systems, where the control coefficient and the model parameters are time-varying and unknown. Based on the so-called congelation of variables method, a novel fractional-power adaptive update law is designed to achieve finite-time practical tracking in the presence of unknown time-varying coefficient/parameters. The virtual and actual control inputs of the proposed finite-time controller are designed in a smooth sign-function-like form complemented by a smooth sign-function-like filter, which allows for circumventing the singularity issues and the chattering phenomenon caused by non-smooth terms in classical finite-time controllers and filters. The tracking error proves to be bounded and converges to a desired compact set in finite time. Simulations of two practical examples are presented and show the effectiveness of the proposed algorithm.

Integral predictive sliding mode control for high-speed trains: A dynamic linearization and input constraint-based data-driven scheme

A control scheme with high reliability and excellent tracking performance is essential for the automatic operation of high-speed trains (HSTs). In this study, a novel discrete-time data-driven predictive sliding mode control (DDPSMC) scheme is proposed for multi-power unit HSTs. Initially, a nonlinear integral terminal sliding mode surface was designed to replace the traditional linear sliding mode function, thereby achieving a rapid system error convergence and alleviating chattering. Then, receding horizon optimization was integrated into predictive control, which allowed the predicted sliding mode state to follow the expected trajectory of a predefined continuous convergence law. This scheme enabled the system to obtain higher output error accuracy and explicitly handle input constraints. Moreover, to enhance robustness, a parameter update law and disturbance delay estimation algorithm were introduced to calculate the control gain and total uncertainty, respectively. Finally, a comparative test of the proposed control scheme was conducted using a CRH380A HST simulation experimental platform in a laboratory setting. Simulation results demonstrate that the velocity error range of each power unit of the HST under the proposed control scheme is within [−0.176 km/h, 0.152 km/h], while the control force and acceleration are within [−55.7 kN, 44.8 kN] and [−0.564 m/s2, 0.496 m/s2], respectively, with stable variation, and other performance indicators are also better than other comparison methods. These results satisfy the safety, stability, and punctuality requirements of the train.

Event-triggered fuzzy adaptive control for a class of MIMO discrete-time nonlinear systems with uncertain control gains

ABSTRACT An event-triggered output control technique is devised for the output anticipated trajectory tracking controller design problem for a class of unknown multiple-input-multiple-output (MIMO) discrete-time nonlinear systems. A parameterized state observer is established to estimate the non-measurable states accurately by utilizing the filter states and the gradient-based fuzzy parameter updated laws. A series of fuzzy filters are proposed to evaluate the immeasurable states, and a filter state observer is constructed to overcome the state estimation that is triggered by the unknown control gains and the coupling of control input. One non-zero parameter is added to a fuzzy logic system (FLS) to reduce the computed burden of the controller with several updating laws. A FLS with one non-zero parameter is introduced to reduce the calculated burden of the controller with fewer updating laws. The proposed method reduces the computed burden and communication consumption while realizing the immeasurable state estimations, guaranteeing the ultimately uniformly bounded (UUB) of the closed-loop system, and achieving a good tracking impact. Finally, simulation analyses are used as a numerical example to confirm that the proposed control strategy is valid.

Lyapunov-derived fuzzy temperature control for thermoelectric cooling system using dynamic updating law

In this paper, an intelligent temperature control strategy is proposed for thermoelectric cooling systems. The Lyapunov-based control method allows a dynamic updating law to be employed in the fuzzy logic system to improve the system performance in the presence of unmodeled dynamics. A new smooth function is also integrated into the control loop to limit input voltage, effectively addressing potential energy waste issue. Furthermore, the controller design challenges posed by even-power input are resolved using the adaptive fuzzy control method and Lagrange’s mean value theorem. Performance analysis indicates that the control function and state signals of the refrigeration system are bounded under our proposed method. Compared with the traditional intelligent control strategy, we only need to design a dynamic update law, which effectively reduces the complexity of the algorithm and is favorable for implementation. Finally, simulation and experimental results demonstrate the effectiveness of the method proposed in this paper. Compared to the traditional PID method, our control method reduces the maximum error by 29.4% and the setting time by 35.7% in the experiment.

Adaptive fault estimation and accommodation for distributed parameter systems with coupled parabolic partial differential equations

This paper presents a novel model-based approach for fault detection, estimation and accommodation in linear distributed parameter systems (DPS) governed by parabolic partial differential equations (PDEs), prone to sensor and actuator faults. Unlike traditional methods, our approach utilises a filter-based observer directly employing the PDE representation and relies solely on boundary measurements, eliminating the need for extensive sensor arrays. By generating fault detection residuals from a comparison of measured and observer outputs, our method offers efficient fault detection. Innovative parameter update laws facilitate accurate estimation of fault parameters, enabling precise adjustment of the nominal controller to mitigate fault effects. Rigorous Lyapunov analysis ensures the robustness of our approach. Additionally, we derive a formula for estimating time-to-accommodation (TTA), providing valuable insights into fault resolution timelines. Simulations on a linearised diffusion process validate the effectiveness of our scheme, highlighting its superiority over existing approaches.

Finite-time robust control of morphing aircraft based on time-varying observer

Morphable vehicles have strong nonlinear, strong coupling and strong time-varying characteristics in the process of variation, which brings great challenges to the design of their flight control systems. To address this problem, a time-varying gain extended state observer is proposed to accurately approximate the system coupling terms. Combining adaptive backstepping control technology and finite-time control theory, a finite-time bounded robust adaptive controller is designed. By designing the barrier Lyapunov function, the actual control law and the adaptive network update law are obtained step by step, ensuring that the system command tracking error can converge to a predetermined range within a finite time. Finally, the effectiveness of the designed control system is verified by attitude control simulation. The simulation results show that the morphable vehicle can track the command signal well in the process of variation and is basically not affected by the variation rate.

Adaptive synchronization and anti-synchronization of Julia sets generated by the competitive model

In this paper, we study the fractal behaviour of a competitive model that describes the interaction of plankton allelopathy. This paper aims to establish synchronization and anti-synchronization of Julia sets of two competitive systems with some different parameters by using an adaptive control strategy. Firstly, a discrete version of the competitive model is obtained, and then the Julia set of the discrete version is generated by using the escape-time algorithm. Adaptive controllers and parameter update laws for unknown parameters are designed to achieve synchronization and anti-synchronization of Julia sets. Furthermore, we can determine unknown parameters of the competitive system by using this adaptive control technique. Here, the adaptive synchronization and anti-synchronization of Julia sets are accomplished by its trajectories synchronization and anti-synchronization due to the close relation of trajectories of the system with the Julia set of the system. Numerical simulations are carried out to validate several key theoretical results as well as the efficacy and accuracy of the applied methodologies. Moreover, with the help of this analysis, we can study other models of a similar type.

Finite-time optimal control for a class of nonlinear systems with performance constraints via critic-only ADP: Theory and experiments

This paper addresses the optimal control problem within the framework of adaptive dynamic programming (ADP) for a class of nonlinear systems subjected to performance constraints. A new finite-time optimal control scheme is developed to stabilize the system by using the critic-only neural network ADP method. Compared with the existing ADP-based optimal control methods with uniformly ultimately bounded stability, the provided control scheme ensures that the controlled system's state and neural network weight estimation error are finite-time stable. It can ensure optimality, prescribed performance, and finite-time stability of the closed-loop control system simultaneously through an integration of ADP, the prescribed performance control technique, and Lyapunov theory. The designed adaptive neural network weight update law can relax the persisting excitation condition. The proposed control scheme is implemented on a robotic experiment platform to achieve trajectory tracking and verify its effectiveness.

Event-Triggered Adaptive Neural Impedance Control of Robotic Systems.

This article presents an event-triggered adaptive neural impedance control (ETANIC) scheme for robotic systems, where the combination of impedance control (IC) and event-triggered mechanism can significantly reduce the computational burden and the communication cost under the premise of ensuring the stability and tracking performances of the robotic systems. The IC is used to achieve the compliant behavior of the robotic systems in response to the environment. The uncertainties of the robotic systems are estimated by the radial basis function neural network (RBFNN), and the update laws for RBFNN are derived from the designed Lyapunov function. The stability of the whole closed-loop control system is analyzed by the Lyapunov theory, and the event-triggered conditions are designed to avoid the Zeno behavior. The numerical simulation and experimental tests demonstrate that the proposed ETANIC scheme can achieve better efficiency for controlling the robotic systems to perform the interaction tasks with the environment in comparison to the adaptive neural IC (ANIC).

Adaptive model-free disturbance rejection for continuum robots

This paper presents two model-free control strategies for the rejection of unknown disturbances in continuum robots. The strategies utilize a neural network-based approximation technique to estimate the uncertain Jacobian matrix using position measurements. The first strategy is designed for periodic disturbances and employs an adaptive model-free controller in conjunction with an adaptive disturbance observer. The second strategy is designed for robustness against arbitrary disturbances and employs time-varying input and update law gains that grow monotonically, resulting in the achievement of asymptotic, exponential, and prescribed-time reference trajectory tracking. The notion of fixed-time stabilization in prescribed time is particularly noteworthy, as it allows for the predefinition of a terminal time, independent of initial conditions and system parameters. A formal stability analysis is presented for each strategy, and the strategies are both tested experimentally with a concentric tube robot subject to unknown disturbances.

Adaptive Inverse Nonlinear Optimal Control Based on Finite-Time Concurrent Learning and Semidefinite Programming.

This article investigates the problem of inverse optimal control (IOC) for a class of nonlinear affine systems. An adaptive IOC approach is proposed to recover the cost functional using only the system state data, which integrates the finite-time concurrent learning (FTCL) technique and the semidefinite programming (SDP) technique. First, an identifier neural network (NN) is employed to approximate the unknown nonlinear control policy, and an FTCL-based update law is proposed to estimate the weights of the identifier NN online, which removes the traditional persistent excitation (PE) condition. Moreover, the finite-time convergence as well as the uniformly ultimately boundness (UUB) of estimation error of the identifier NN weights are analysed according to whether or not there exists the identifier NN approximation error. Then, with the help of a value NN for approximating the value function, an SDP problem with a quadratic objective function can be set up for determining the weighting matrices of the cost functional. Finally, simulation results are presented to validate the proposed method.

Prescribed‐time resilient current‐sensorless DC power converter control

AbstractWhile conventional control techniques for stabilizing power electronic converters with constant‐power load (CPL) often rely on complete system state information, the associated challenges, including the cost and increased size due to the installation of multiple sensors, necessitate exploration of alternative methodologies. To address these concerns, a novel observer‐based sliding‐mode control approach is proposed. This approach aims to enhance cost‐effectiveness and reliability in the context of a DC‐DC buck converter supplying power to an unknown CPL. The core concept involves designing a state observer to estimate the system's current based on voltage measurements. A non‐certainty equivalent adaptive update law is then introduced to estimate the unknown CPL, incorporating feedback from voltage measurements and current estimation. To guarantee stability in the face of uncertainties in system parameters, a thorough analysis utilizing the Lyapunov theorem is conducted on the closed‐loop system. The efficacy and feasibility of the proposed control algorithm are substantiated through experimental results on the OPAL‐RT platform, showcasing its promising performance.

Robust neuro-adaptive command-filtered back-stepping fault-tolerant control of satellite using composite learning

This study proposes an adaptive neural command-filtered backstepping control system for precise and fast attitude control of a satellite considering different uncertain dynamics. Mission success crucially depends on robust attitude control resilient to model uncertainties, unmodeled dynamics, external disturbances, and actuator faults. The proposed approach synergistically combines a neural network for handling model uncertainties, unmodeled dynamics, and actuator faults, with a disturbance observer for compensating external disturbances and neural network estimation errors. The command-filtered backstepping technique avoids the explosion of complexity inherent to traditional backstepping, while integrating integral action into the design results in the elimination of the steady-state tracking error. Besides, a composite learning method optimizes the update laws for the neural network and disturbance observer weights, enhancing control performance. Despite the presence of uncertainties, the closed-loop system stability is guaranteed by the Lyapunov stability theorem. Simulation results demonstrate the proposed controller’s ability to handle severe actuator faults, unmodeled dynamics, and measurement noise without requiring explicit fault detection and isolation schemes.

Sexual Violence and Girl Child Completion of Primary Education; A Case Study of Mitooma Sub County, Mitooma District.

The study focused on determining the relationship between sexual violence and girl child completion of primary education. Specifically, to find out how girl child sexual assault affects the completion of primary Education; to find out how girl child sexual harassment affects the completion of primary education ; to find out how effectiveness of statutory instruments in place towards sexual violence against girl child affects completion of primary Education and to find possible measures of controlling sexual harassment against girl child in primary schools in Mitooma Sub County. The study adopted cross-sectional research design with the sample size of 160 respondents, participants were selected using simple random and purposive sampling. Data was collected using questionnaire survey, interview method and focused group discussion. It was analysed using descriptive and pearson correlation coefficient. The study found out that sexual assault had a positive significant contribution to girl child completion of primary education. The study also revealed that that effect of sexual violence had a negative significant contribution to girl child completion of primary education. The study showed that effectiveness of statutory instruments had a positive significant contribution to girl child completion of primary education. The study found out that school has provided learners with basic information on how to avoid situations leading to sexual violence; sign posts have been hung in the school compound bearing messages against sexual violence; the school provides a protective environment and free from risks of sexual violence. The study recommends that government should promote gender equality and respect through incorporating gender equality and respectful relationships into school curricula. Teach children about consent, boundaries, and mutual respect from a young age. Government though police and court should ensure Immediate and Comprehensive Support for Victims by providing immediate and ongoing psychological support to help victims cope with trauma and regain confidence in their ability to learn and participate in school; Ensuring that victims have access to medical care to address any physical health issues resulting from the violence and to offer preventive and supportive care; Maintaining strict confidentiality to protect the privacy of victims and reduce the risk of stigma or further trauma. Government should periodically review and update laws and policies to address emerging trends and challenges related to sexual violence; ensure that statutory instruments cover all aspects of sexual violence, including prevention, protection, and prosecution, and address the specific needs of children. The study also recommends that there should be parent and caregiver workshops to offer workshops for parents and caregivers on recognizing signs of abuse, discussing safety with children, and understanding their role in prevention.