Adaptive Prescribed Performance Control of A Flexible-Joint Robotic Manipulator With Dynamic Uncertainties

Convergence-Rate-Based Event-Triggered Mechanisms for Quasi-Synchronization of Delayed Nonlinear Systems on Time Scales

Most of the existing event-triggered mechanisms (ETMs) were designed according to the difference between the quadratic form of measurement errors and the quadratic form of sampling states (or real-time states). In order to reduce the amount of data transmission and develop ETMs for continuous-time and discrete-time delayed nonlinear systems (NSs) simultaneously, this article investigates quasi-synchronization (QS) of NSs on time scales based on a novel ETM, which is designed according to the convergence rate instead of measurement errors of the addressed systems. First, a novel ETM is designed under known nonlinear dynamics, and it is demonstrated that QS with given convergence rate and error level can be achieved under matrix inequality criteria. Second, if the nonlinear functions are unknown, we adapt our ETM to handle this special case. Not only QS but also complete synchronization with given convergence rate can be achieved under the ETMs. If the constructed Lyapunov functions passes through 0, the designed ETM will keep it at the origin. In this case, finite-time synchronization is achieved. Third, under the designed ETMs, it is proved that Zeno behavior can be excluded. At last, four numerical simulations are presented to demonstrate the feasibility and the advantage of the designed ETMs in this article.

A Novel Event-triggered Practical Prescribed-Time Control for four Complex coupled Duffing-type MEMS resonators with Prescribed Performance

This paper explores the dynamic characteristics and a novel event-triggered practical prescribed-time controller for four complex coupled Duffing-type MEMS resonators. Initially, the effects of mechanical coupling stiffness, electrostatic coupling stiffness, and internal system parameters on the system's dynamic behavior are examined. The analysis results provide guidance for selecting system parameters. Furthermore, the dynamic analysis reveals that chaotic oscillations may occur in the system without input, significantly affecting system performance. To mitigate these chaotic oscillations, a novel event-triggered practical prescribed-time controller with prescribed performance is proposed, utilizing interval type-3 fuzzy system (IT3FS) to estimate unknown nonlinear functions. By employing a more relaxed practical prescribed-time stability (PPTS) criterion and a novel time-varying scale transformation function (STF), the designed controller ensures that all signals converge within a prescribed time. Additionally, a prescribed performance function (PPF) is employed, allowing for more flexible convergence boundary settings. A dynamic event-triggered mechanism is implemented to reduce the frequency of control signal updates. The stability analysis demonstrates that all signals converge within a prescribed time, and the tracking errors remain within their prescribed boundary. Finally, simulations validate the effectiveness of the proposed scheme.

Mode switching control of independent metering fluid power systems

An independent metering system (IMS) realizes the decoupling of the meter-in and meter-out orifices. The energy efficiency of the hydraulic system can be effectively improved by switching between different operational modes. However, the tracking accuracy of the IMS mode-switching system is difficult to ensure, which can easily lead to instability in the hydraulic system. In view of this, this paper proposes a mode switching controller based on an IMS. First, the K-filters theory is innovatively applied to the mode switching hydraulic system to estimate unmeasurable state variables of a system accurately. In addition, fuzzy logic systems (FLSs) are applied to handle the unmodeled errors and disturbances in the mechanical system dynamics model and hydraulic system. Further, aiming at the stability and trajectory tracking problems in the mode switching control (MSC) process of an IMS, the average dwell time (ADT) stability analysis method is applied to the mode switching hydraulic system to construct a set of switching rules to make the closed-loop switching system stable. Moreover, based on the prescribed performance control (PPC) theory, all state errors of a hydraulic system are guaranteed to reach the performance function constraint boundary at the specified time. Also, a dynamic surface control (DSC) technique is used to avoid the explosion of computational complexity caused by iterative differentiation inherent in the traditional backstepping method. Finally, the feasibility and effectiveness of the proposed method are verified by simulation, and experiments are carried out on mini-excavators. The results show that the designed controller can not only ensure the tracking accuracy, but also effectively suppress the instability of the hydraulic system caused by MSC.

Research on boundary control of vehicle-mounted flexible manipulator based on partial differential equations

Vehicle-mounted flexible robotic arms (VFRAs) are crucial in enhancing operational capabilities in sectors where human intervention is limited due to accessibility or safety concerns, such as hazardous environments or precision surgery. This paper introduces the latest generation of VFRAs that utilize advanced soft materials and are designed with elongated structures to provide greater flexibility and control. We present a novel mathematical model, derived using Hamilton's principle, which simplifies the analysis of the arm's dynamic behaviors by employing partial differential equations (PDEs). This model allows us to understand how these arms behave over time and space, classifying them as distributed parameter systems. Furthermore, we enhance the practical utility of these robotic arms by implementing a proportional-derivative (PD) based boundary control law to achieve precise control of movement and suppression of vibrations, which are critical for operations requiring high accuracy. Our approach's effectiveness and practical utility are evidenced by numerical simulations, which verify that our advanced control strategy greatly enhances the performance and dependability of VFRAs in actual applications. These advancements not only pave the way for more sophisticated robotic implementations but also have broad implications for the future of automated systems in various industries.

Event-Triggered Adaptive Preassigned Finite-Time Consensus Control for Multiagent Systems With Nonlinear Faults

This article investigates a novel neuro-adaptive barrier Lyapunov function (BLF)-based event-triggered preassigned finite-time consensus control with asymptotic tracking for the nonlinear multiagent systems. The proposed approach is designed to broaden the scope of application by considering the high-order nonstrict-feedback dynamics of each agent with dynamic uncertainties subject to external disturbances and nonaffine nonlinear faults. A neural network (NN) is employed to approximate the unknown nonlinear terms. By fusing the NNs and Butterworth low-pass filter technique, the issues arising from the nonaffine nonlinear fault are addressed. To save the communication resources, a novel dynamic event-triggered mechanism based on an enhanced switching threshold is suggested. Additionally, a novel concept called the preassigned finite-time performance function (PFTPF) is defined to improve the transient and steady-state performances as well as providing faster response. The key feature of the proposed adaptive BLF-based control based on the bound estimation method is the introduction of a smooth function with decreasing variable which not only ensures that all the signals remain bounded and the synchronization errors are restricted within the PFTPF but also guarantees that the tracking errors asymptotically converge to zero. Finally, an illustrative example is provided to verify the feasibility of the proposed control approach.

Whole Body Control of Mobile Manipulators With Series Elastic Actuators for Cart Pushing Tasks

Human-centered environments provide affordance for the use of two-handed mobile manipulators. Yet robots designed to function in and physically interact with such environments are not yet capable of meeting human users' requirements. This work proposes a whole body control framework of a two-handed mobile manipulator driven by series elastic actuators (SEAs) for cart pushing tasks. A whole body dynamic model for an integrated mobile platform and on-board arms is revealed, which takes into account the interaction forces with the cart. Then, the explicit force/position control of the mobile manipulator is performed. It enables the robot to interact dynamically with the environment while providing motion, i.e., the manipulators provide both output force control and motion control for pushing a cart. To cope with the highly nonlinear system dynamics and parameter variation of a SEA-driven mobile manipulator, this work proposes an adaptive robust controller based on a novel integral barrier Lyapunov function for cart pushing tasks by considering model uncertainty. The proposed controller enables the mobile manipulator to complete cart pushing tasks by regulating the position and output force of the mobile base and arms. The experimental results show the effectiveness of this approach in cart pushing tasks.

Pitch angle and altitude control for unmanned helicopter based on new approximation-free control

This article introduces an enhanced non-approximated control technique for the pitch and altitude control systems of unmanned helicopters. It takes into account unpredictable external disturbances and system dynamics. The integration of prescribed performance control into unmanned helicopter systems significantly improves the transient and steady-state response capabilities. This approach avoids the computational complexities often associated with neural networks and fuzzy control methods. By avoiding the need for function approximation, which can introduce inaccuracies and computational overhead, the controller design process is streamlined. This method's simplicity and ability to handle unknown disturbances make it highly suitable for real-world implementation, where robustness and efficiency are paramount. Finally, simulations are conducted to showcase the improved transient and steady-state response capabilities achieved by the proposed approach.

Fixed-Time Composite Neural Learning Control of Flexible Telerobotic Systems

This article is devoted to the fixed-time synchronous control for a class of uncertain flexible telerobotic systems. The presence of unknown joint flexible coupling, time-varying system uncertainties, and external disturbances makes the system different from those in the related works. First, the lumped system dynamics uncertainties and external disturbances are estimated successfully by designing a new composite adaptive neural networks (CANNs) learning law skillfully. Moreover, the fast-transient, satisfactory robustness, and high-precision position/force synchronization are also realized by design of fixed-time impedance control strategies. Furthermore, the "complexity explosion" issue triggered by traditional backstepping technology is averted efficiently via a novel fixed-time command filter and filter compensation signals. And then, sufficient conditions of system controller parameters and fixed-time stability are theoretically given by establishing the Lyapunov stability theorem. Besides, the absolute stability of the two-port networked system under complex transmission time delays is rigorously proved. Finally, simulations are performed with 2-link flexible telerobotic systems under two cases, results are presented to realistically verify the proposed control algorithm available.

Observer-Based Neural Control of N-Link Flexible-Joint Robots

This article concentrates on the adaptive neural control approach of n -link flexible-joint electrically driven robots. The presented control method only needs to know the position and armature current information of the flexible-joint manipulator. An adaptive observer is designed to estimate the velocities of links and motors, and radial basis function neural networks are applied to approximate the unknown nonlinearities. Based on the backstepping technique and the Lyapunov stability theory, the observer-based neural control issue is addressed by relying on uplink-event-triggered states only. It is demonstrated that all signals are semi-globally ultimately uniformly bounded and the tracking errors can converge to a small neighborhood of zero. Finally, simulation results are shown to validate the designed event-triggered control strategy.

Multi-stage trajectory tracking of robot manipulators under stochastic environments

For robot manipulators composed of Lagrange subsystems driven by direct current (DC) motors under stochastic environments, multi-stage trajectory tracking is investigated in this paper. The main challenge is how to achieve the end-effector drive of manipulators from a given initial state to a final state. First, the inverse kinematics method and the partition of the task space are adopted to tackle multi-stage trajectory planning. Second, the adaptive backstepping technique is used to design tracking controller for stochastic Lagrangian subsystems. Then, based on the state-dependent switching signal, a multi-stage switched controller is designed for trajectory tracking of robot manipulators. All signals in the close-loop error switched system are bounded in probability, and the tracking error in mean square can be made arbitrarily small enough by parameters-tuning The effectiveness of the proposed control method is illustrated by simulation results.

A model-free terminal sliding mode control for robots: Achieving fixed-time prescribed performance and convergence

This paper introduces a new control strategy for robot manipulators, specifically designed to tackle the challenges associated with traditional model-based sliding mode (SM) controller design. These challenges include the need for accurately computed system models, knowledge of disturbance upper bounds, fixed-time convergence, prescribed performance, and the generation of chattering. To overcome these obstacles, we propose the incorporation of a neural network (NN) that effectively addresses these issues by removing the constraint of a precise system model. Additionally, we introduce a novel fixed-time prescribed performance control (PPC) to enhance response performance and position-tracking accuracy, while effectively limiting overshoot and maintaining steady-state error within the predefined range. To expedite the convergence of the SM surface to its equilibrium point, we introduce a faster terminal sliding mode (TSM) surface and a novel fixed-time reaching control algorithm (RCA) with adaptable factors. By integrating these approaches, we develop a novel control strategy that successfully achieves the desired goals for robot manipulators. The effectiveness and stability of the proposed approach are validated through extensive simulations on a 3-DOF SAMSUNG FARA-AT2 robot manipulator, utilizing both Lyapunov criteria and performance evaluations. The results demonstrate improved convergence rate and tracking accuracy, reduced chattering, and enhanced controller robustness.

Adaptive Neural Network Event-Triggered Output-Feedback Containment Control for Nonlinear MASs With Input Quantization

This article investigates the adaptive neural network (NN) event-triggered containment control problem for a class of nonlinear multiagent systems (MASs). Since the considered nonlinear MASs contain unknown nonlinear dynamics, immeasurable states, and quantized input signals, the NNs are adopted to model unknown agents, and an NN state observer is established by using the intermittent output signal. Subsequently, a novel event-triggered mechanism consisting of both the sensor-to-controller and controller-to-actuator channels are established. By decomposing quantized input signals into the sum of two bounded nonlinear functions and based on the adaptive backstepping control and first-order filter design theories, an adaptive NN event-triggered output-feedback containment control scheme is formulated. It is proved that the controlled system is semi-globally uniformly ultimately bounded (SGUUB) and the followers are within a convex hull formed by the leaders. Finally, a simulation example is given to validate the effectiveness of the presented NN containment control scheme.

Adaptive neural network prescribed performance control for dual switching nonlinear time-delay system

This paper investigates the adaptive neural network prescribed performance control problem for a class of dual switching nonlinear systems with time-delay. By using the approximation of neural networks (NNs), an adaptive controller is designed to achieve tracking performance. Another research point of this paper is tracking performance constraints which can solve the performance degradation in practical systems. Therefore, an adaptive NNs output feedback tracking scheme is studied by combining the prescribed performance control (PPC) and backstepping method. With the designed controller and the switching rule, all signals of the closed-loop system are bounded, and the tracking performance satisfies the prescribed performance.