Nonlinear Robotic Systems Research Articles

An innovative finite-time leader-follower consensus control for a nonlinear mechanical disturbed system with actuation saturation

ABSTRACT This paper presents a novel leader-follower consensus control method for a special class of nonlinear multi-agent mechanical systems in the presence of control input saturation and external disturbances, and this class includes robot system dynamics with a wide range of potential applications in industry. In the innovative technique, one of the agents is selected as the leader and the other agents are guided in such a way that the entire states of the system can reach a consensus within the transient limits of the determined performance. Due to the presence of disturbance in most practical systems, the effect of limited disturbance has been evaluated in the consensus control method and actuator saturation is included in the design process of the control technique. Finally, a terminal sliding mode control method is adapted to ensure the stability of the overall system and fast finite-time leader-follower consensus control. The simulation results of the multi-agent nonlinear robot system in the MATLAB environment, in different scenarios by simultaneously considering the actuator saturation and external disturbance, show the effectiveness of the proposed control method.

Enhanced tracking control for n-DOF robotic manipulators: A fixed-time terminal sliding mode approach with time delay estimation

This work proposes a fixed-time terminal sliding mode control scheme with time delay estimation (FxTSM-TDE) for unknown nonlinear robotic systems under uncertainties and unknown bounded disturbances. The idea of the fixed-time terminal sliding mode control method (FxTSM) is thoroughly explained and analyzed. The FxTSM approach has been used to achieve nonsingular fixed-time control, non-chatter control inputs, and superior trajectory tracking performance. Then, the proposed approach for estimating the unknown robot dynamics utilizes the TDE approach. The Lyapunov analysis is used to establish the fixed-time stability of the system. The effectiveness of the suggested approach is evaluated, demonstrated, and contrasted with the existing control schemes by illustrating the results of the proposed method's enhanced performance in tracking accuracy, robustness, and convergence speed when implemented in robotic manipulator dynamics. The results indicate that the existing scheme has corresponding root mean square (RMS) error values of 0.0235 and 0.0283, while the proposed scheme has values of 0.0178 and 0.0188, respectively.

Comparative Analysis: Fractional PID vs. PID Controllers for Robotic Arm Using Genetic Algorithm Optimization

This paper presents a comparative analysis of a fractional-order proportional–integral–derivative (FO-PID) controller against the standard proportional–integral–derivative (PID) controller, applied to a nonlinear robotic arm manipulator systems. The genetic algorithm (GA) optimization method was implemented to tune the gain parameters of the FO-PID and PID controllers. The performance of the FO-PID and PID controllers were evaluated though different cost functions, including integral of squared error (ISE), integral of absolute error (IAE), integral of time-weighted absolute error (ITAE), and integral of time-weighted squared error (ITSE). The performance of these controllers was examined via extensive simulations by using MATLAB/SIMULINK for different operating scenarios of the robotic arm manipulator system. Based on the obtained results, a comparative performance matrix is proposed, wherein cost functions ISE, IAE, ITAE, and ITSE are represented as columns while characteristic parameters (overshoot, rising time, and settling time) are represented as rows. The proposed performance matrix facilitates the selection between the PID and FO-PID controllers.

A novel finite-time leader-follower consensus control for a disturbed mechanical nonlinear system in presence of actuator saturation

This paper presents a novel leader-follower consensus control for a particular class of nonlinear multi-agent mechanical systems in the presence of control input constraints and external disturbances which includes robot system dynamics with a wide range of potential applications in industry. In this case, one of the agents is selected as the leader to direct the other agents in such a way that the whole system can reach consensus within certain prescribed performance transient bounds. Due to the presence of disturbances in most practical systems, the effect of limited disturbance in the consensus control method has been investigated, and actuator saturation is included in the design process. A terminal sliding mode control method has been adapted to ensure the stability of the overall system and fast finite-time leader-follower consensus control. The simulation results of the multi-agent nonlinear robot system in MATLAB environment, in different scenarios with simultaneous consideration of actuator saturation and external disturbance, will show the efficiency of the proposed control method.

Data-driven replay attack detection for unknown cyber-physical systems

This paper considers a data-driven method to detect stealthy replay attacks on cyber-physical systems (CPSs) whose models are unknown. Most existing attack detection methods assume the system dynamic is known. However, due to the large scale and complexity of the system, it is difficult to get an accurate model of a CPS. This paper uses a moving window subspace identification method to construct a linear discrete time-varying model of CPS. The controllability and observability of the obtained model are proved. On this basis, the replay attack is transformed into a detectable additive attack using the output coding strategy. Then, considering modeling errors, a time-varying H∞ filter is designed, and a detection function based on the output residual is constructed. When the system works without any attacks, the detection function approaches a limited threshold. On the contrary, that function will increase extremely larger than the threshold when the replay attack occurs. The detectability of our scheme for attacks is also mathematically proven. Finally, to verify the effectiveness of the proposed scheme, we provide simulation results on a linear motor system and a nonlinear robotic system.

Modeling Nonlinear Physical Systems in a Real-Time Operating System Environment for Control and Cyber Threat Mitigation

Nonlinearity control and interconnection between physical systems are essential for many contemporary systems. Cyber Physical systems came about because of the growing number of security concerns with network control systems. Nonlinear physical systems are the primary subject of this study's investigation into control and cyber threat mitigation. Nonlinear physical systems are the focus of this study, which also delves into their control, modelling, and real-time implementation. Here, physical systems are defined as two kinds of benchmark nonlinear robotic systems: the Segway transporter and the cruise control system. Many see the Segway as an unstable and nonlinear physical system. By factoring in kinetic and potential energy, Lagrangian dynamics has allowed for the derivation of equations of motion. Next, a state-space model of the system is obtained by linearizing the nonlinear differential equations using Taylor series expansion of functions. This model is then transformed into two transfer functions, one for the tilt angle and one for the yaw angle. The Model Predictive Controller (MPC), GA Tuned PID, and PID are the building blocks of MATLAB's effective simulation study of nonlinear control.

Adaptive Robust Tracking Control of Robotic Manipulator based on SMC and Fuzzy Control Strategy

In recent years, robotic systems have been widely used in different applications, and this has motivated researchers to develop different control methods. A model-free, intelligent, robust control method for a nonlinear robotic manipulator system is proposed in this work. This paper presents a novel solution for the major drawbacks of the sliding mode control scheme, which are chattering. Prior knowledge is needed about the dynamic model of the controlled system and the upper bound of uncertainty. In this paper, a fuzzy-like PD controller with SMC (FLPDSM) is proposed. The fuzzy-like PD controller was designed according to fuzzy rules and membership functions based on the nominal model of the robot manipulator. A robust control term was added to the control signal to compensate for the system uncertainty, and external disturbances are compensated by adding an auxiliary robust term to the SMC control law. Two methods for designing robust control terms are proposed. The first proposed method assumes that the upper bound of system uncertainty is known although it cannot be exactly determined due to external disturbances and uncertainty. Hence, a second method was proposed that assumes this bound to be unknown, and an adaptive gain based on Lyapunov theory was used to derive the adaptation law. The Lyapunov second method was used to ensure the stability of the closed loop system. Performance tests on the proposed methods were implemented through simulation studies for the two-link robotic manipulator, and the test results were compared with the standard SMC to verify the effectiveness of the proposed method. A good trajectory tracking with a high robustness against parameter variations and external disturbances was observed under the presented control scheme.

Model-free scheme using time delay estimation with fixed-time FSMC for the nonlinear robot dynamics

<abstract><p>This paper presents a scheme of time-delay estimation (TDE) for unknown nonlinear robotic systems with uncertainty and external disturbances that utilizes fractional-order fixed-time sliding mode control (TDEFxFSMC). First, a detailed explanation and design concept of fractional-order fixed-time sliding mode control (FxFSMC) are provided. High performance tracking positions, non-chatter control inputs, and nonsingular fixed-time control are all realized with the FxSMC method. The proposed approach performs better and obtains superior performance when FxSMC is paired with fractional-order control. Furthermore, a TDE scheme is included in the suggested strategy to estimate the unknown nonlinear dynamics. Afterward, the suggested system's capacity to reach stability in fixed time is determined by using Lyapunov analyses. By showing the outcomes of the proposed technique applied to nonlinear robot dynamics, the efficacy of the recommended method is assessed, illustrated, and compared with the existing control scheme.</p></abstract>

Finite-Time Sliding Mode Control Based on Unknown System Dynamics Estimator for Nonlinear Robotic Systems

This article proposes an unknown system dynamics estimator (USDE) based finite-time sliding mode control for nonlinear robotic systems with unknown dynamics. First, an USDE with only one tuned parameter is designed by using filter operations and invariant manifold to estimate the unknown system dynamics. Second, a novel sliding mode surface with faster response time is designed. By incorporating the USDE into the sliding mode surface, a composite sliding mode control (CSMC) is proposed to achieve better dynamic performance. With simple parameter turning, the CSMC can realize the high precision trajectory tracking control of robotic systems. Finally, simulation results illustrate the superiority of the proposed control strategy.

Controller design of robot nonlinear system based on the improved fuzzy sliding mode control

In this paper, the input-output finite time stability of fuzzy sliding mode control in nonlinear robot systems is discussed. Specifically, the system considers more realistic factors, such as uncertainty, nonlinearity, interference, and state delay. An improved fuzzy sliding mode control (FSMC) scheme is developed, which is considered in most results of FSMC schemes. Based on the input-output finite-time stability characteristics and the proposed control scheme, the objective of this work is to reduce the effects of uncertainty, nonlinearity, disturbance, and state delay. Finally, the effectiveness and superiority of the proposed method are verified by simulation and comparison with other reference methods.

A novel fixed‐time sliding mode control for nonlinear manipulator systems based on adaptive disturbance observer

AbstractConsidering that in the trajectory tracking control of a nonlinear robotic manipulator system, the control effect is easily limited by the initial state of the system, and the system has modeling error, unknown disturbance, and friction in the actual control, to overcome the above problems, a fixed‐time sliding mode control (SMC) strategy based on adaptive disturbance observer (ADO) is developed in this paper. Firstly, feedforward compensation of the system is achieved by developing an ADO to accurately estimate the compound disturbances in the system. Second, a new fixed‐time sliding mode (SM) surface is presented to overcome the singularity issue and accelerate the error convergence. In addition, to enhance the performance of the reaching phase, a variable exponential power reaching law (VEPRL) is developed, which can effectively adjust the convergence rate. Through rigorous theoretical analysis, it is shown that the system state can be stabilized at a fixed time, and an upper bound on the convergence time is also given. Finally, the effectiveness of the control method is verified by comparing it with different control schemes in simulation.

Secure Adaptive Trajectory Tracking Control for Nonlinear Robot Systems Under Multiple Dynamic Obstacles: Safety Barrier Certificates

This article studies the problem of obstacle-avoidance tracking control for a class of uncertain nonlinear robot systems in multiple-dynamic-obstacles environment. The main challenge focuses on how to simultaneously ensure tracking performance and obstacle avoidance. The existing collision-avoidance tracking control schemes cannot guarantee the tracking performance inside the obstacle detection region, since the use of additive Lyapunov-barrier function (LBF) generates the dynamic mismatching. To overcome this difficulty, a novel integral-multiplicative LBF is constructed. The adaptive mechanism is designed to compensate for the mismatching uncertainties. By incorporating the barrier function into backstepping procedure, a secure adaptive tracking control scheme is proposed. Compared with the existing results, the proposed control scheme can simultaneously achieve the obstacle avoidance and tracking performance regardless of being inside the obstacle sensing region and unknown nonlinear uncertainties.

Adaptive prescribed performance control for nonlinear robotic systems

This paper investigates the problem of asymptotic tracking control of nonlinear robotic systems with prescribed performance. The control strategy is developed based on a modified prescribed performance function (PPF) to guarantee the transient behavior, while the requirements on the accurate initial tracking error in the classical PPF can be remedied. The fuzzy logic system (FLS) is used to approximate the unknown dynamics. In the existing PPF based adaptive control schemes with FLSs, the tracking error does not achieve asymptotic convergence. To address this issue, a robust integral of the sign of the error (RISE) term is incorporated into the control design to reject the FLS approximation errors and external disturbances, such that the asymptotic convergence is achieved. Finally, numerical simulation and experimental results validate the effectiveness of the proposed control scheme.

Neural Network-Based Tracking Control of Uncertain Robotic Systems: Predefined-Time Nonsingular Terminal Sliding-Mode Approach

This article investigates the predefined time trajectory tracking control of uncertain nonlinear robotic systems. A radial basis function neural network (RBFNN) is used to estimate uncertainties in the robotic system dynamics. To avoid the singularity of terminal sliding-mode control (TSMC), a modified sliding variable is adopted. In order to realize that the tracking errors can converge to a small neighborhood of the origin in <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">predefined time</i> , within which the maximum convergence time can be adjusted by explicit parameters in advance, a nonsingular TSMC based on the RBFNN is proposed. Experiments on a ROKAE platform demonstrate the effectiveness and advantage of the proposed control method.

Neural Adaptive Robust Motion-Tracking Control for Robotic Manipulator Systems

This paper deals with the motion trajectory tracking control problem based on output feedback and artificial neural networks for anthropomorphic manipulator robots under disturbed operating scenarios. This class of manipulator robots constitutes nonlinear dynamic systems subjected to disturbance torques induced mainly by work payload. Parametric uncertainty and possible dynamic modeling errors stand for other kind of disturbances that can deteriorate the efficiency and robustness of the tracking of controlled nonlinear robotic system trajectories. In fact, the presence of unknown dynamic disturbances is unavoidable in industrial robotic engineering systems. Therefore, for high-precision applications, such as laser cutting, marking, or welding, effective control schemes should be designed to guarantee adequate motion profile tracking planned on this class of disturbed nonlinear robotic system. In this context, a new adaptive robust motion trajectory tracking control scheme based on output feedback and artificial neural networks of anthropomorphic manipulator robots is presented. Three-layer B-spline artificial neural networks and time-series modeling are properly exploited in the design of novel adaptive robust motion tracking controllers for robotic applications of laser manufacturing. In this way, dependency on detailed nonlinear mathematical modeling of robotic systems is considerably reduced, and real-time estimation of uncertain dynamic disturbances is not required. Furthermore, several cases studies to demonstrate the motion planning tracking control robustness for a class of MIMO nonlinear robotic systems are described. blue Insights for the extension of the introduced output-feedback adaptive neural control design approach for other architecture of nonlinear robotic systems are depicted.

TDE Based Model-free Control for Rigid Robotic Manipulators under Nonlinear Friction

This paper establishes a model-free finite-time tracking control of nonlinear robotic manipulator systems. The proposed controller incorporates both time delay estimation (TDE) and an enhanced terminal sliding mode control (TSMC). The improved TSMC scheme is devised using fractional-order TSMC (FOTSMC) and proportional-integral-derivative (PID) control to obtain robust tracking and high control performance. The TDE is designed to estimate the unknown nonlinear dynamics of robotic manipulators including the Stribeck friction and the external disturbances. Due to Stribeck friction, the effect of TDE error may fail to obtain the desired error performance; thus, another TDE loop is devised to compensate for TDE error generated by non-smooth frictions. The Lyapunov criterion is used to investigate the finite-time stability to analyze the behavior of the designed approach. Finally, computer simulations of the proposed method on PUMA 560 robotic manipulators are performed in contrast with FOTSMC and adaptive fractional-order nonsingular terminal sliding mode control (AFONTSM).

ACD-EDMD: Analytical Construction for Dictionaries of Lifting Functions in Koopman Operator-Based Nonlinear Robotic Systems

Koopman operator theory has been gaining momentum for model extraction, planning, and control of data-driven robotic systems. The Koopman operator’s ability to extract dynamics from data depends heavily on the selection of an appropriate dictionary of lifting functions. In this letter, we propose ACD-EDMD, a new method for Analytical Construction of Dictionaries of appropriate lifting functions for a range of data-driven Koopman operator based nonlinear robotic systems. The key insight of this work is that information about fundamental topological spaces of the nonlinear system (such as its configuration space and workspace) can be exploited to steer the construction of Hermite polynomial-based lifting functions. We show that the proposed method leads to dictionaries that are simple to implement while enjoying provable completeness and convergence guarantees when observables are weighted bounded. We evaluate ACD-EDMD using a range of diverse nonlinear robotic systems in both simulated and physical hardware experimentation (a wheeled mobile robot, a two-revolute-joint robotic arm, and a soft robotic leg). Results reveal that our method leads to dictionaries that enable high-accuracy prediction and that can generalize to diverse validation sets. The associated GitHub repository of our algorithm can be accessed at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/UCR-Robotics/ACD-EDMD</uri> .

Position Tracking Control of Robotic System with Time-varying Delay and Dead-zone

This paper studies the position tracking control problem of nonlinear robotic system subject to time-varying state delay and input dead-zone. The input dead-zone and unknown external disturbance are regarded as the lumped disturbance and a sliding mode disturbance observer is designed. In order to ensure the stability of the robotic system with time-varying state delay, a position tracking controller based on auxiliary position tracking error functions is designed. Considering the estimated error of the sliding mode disturbance observer, a gain adaptive law is designed to further improve the position tracking accuracy of the system. The stability of the proposed controller is theoretically proved by Lyapunov-Krasovskii function method, and the effectiveness of the controller is verified by comparative simulations. The proposed control method can achieve stable and accurate position tracking of the robotic system with simultaneous time-varying state delay and input dead-zone.

Adaptive sliding tracking control for nonlinear uncertain robotic systems with unknown actuator nonlinearities

AbstractThis study is concerned with the tracking control problem for nonlinear uncertain robotic systems in the presence of unknown actuator nonlinearities. A novel adaptive sliding controller is designed based on a robust disturbance observer without any prior knowledge of actuator nonlinearities and system dynamics. The proposed control strategy can guarantee that the tracking error eventually converges to an arbitrarily small neighborhood of zero. Simulation results are included to demonstrate the effectiveness and superiority of the proposed strategy.

Envisioning the future of colorectal surgery: preclinical assessment and detailed description of an endoluminal robotic system (ColubrisMX ELS)

The EndoLuminal Surgical System (ELS) is an emerging non-linear robotic system specifically designed for transanal surgery that allows for excision of colorectal neoplasia and luminal defect closure. An evaluation of ELS was conducted by a single surgeon in a preclinical setting at the EndoSurgical Center of Florida in Orlando, between October 1st, 2020 and December 31st, 2020, using porcine colon as a model. Mock lesions measured 2.5 to 3.5cm were excised partial-thickness. Specimen quality and excision time was assessed and evaluated. Twenty consecutive robotic transanal minimally invasive surgery (TAMIS) operations utilizing the ELS system were successfully performed without fragmentation. The mean and standard deviation procedure time for all 20 cases was 18.41 ± 14.15min. The latter 10 cases were completed in substantially less time, suggesting that ELS requires at least 10 preclinical cases for a surgeon to become familiar with the technology. A second task, namely suture closure of the partial-thickness defect, was performed in 9 of the 20 cases. Mean time and standard deviation for this task measured 27.89 ± 10.07min. There were no adverse events. ELS was successful in performing the tasks of partial-thickness disc excision and closure in a preclinical evaluation. Further study is necessary to determine its clinical applicability.