Performance Of Adaptive Controller Research Articles

Failure risk management: adaptive performance control and mission abort decisions.

The failure behavior of safety-critical systems typically depends on the system performance level, which offers opportunities to control system failure risk through dynamic performance adjustment. Moreover, mission abort serves as an intuitive way to mitigate safety hazards during mission execution. Our study focuses on systems that execute successive missions with random durations. To balance mission completion probability and system failure risk, we examine two decision problems: when to abort missions and how to select the performance level prior to mission abort. Our objective is to maximize the expected revenue through dynamic performance control and mission abort (PCMA) decisions. We consider condition-based PCMA decisions and formulate the joint optimization problem into a Markov decision process. We establish the monotonicity and concavity of the value function. Based on this insight, we show that optimizing the mission abort policy requires a series of control limits. In addition, we provide conditions under which the performance control policies are monotone. For comparative purposes, we analytically evaluate the performances of some heuristic policies. Finally, we present a case study involving unmanned aerial vehicles executing power line inspections. The results indicate the superiority of our proposed risk control policies in enhancing operational performance for safety-critical systems. Dynamic performance adjustment and mission abort decisions provide opportunities to reduce the failure risk and increase operational rewards of safety-criticalsystems.

Trajectory Tracking with Obstacle Avoidance for Nonholonomic Mobile Robots with Diamond-Shaped Velocity Constraints and Output Performance Specifications.

In this paper, we address the trajectory-/target-tracking and obstacle-avoidance problem for nonholonomic mobile robots subjected to diamond-shaped velocity constraints and predefined output performance specifications. The proposed scheme leverages the adaptive performance control to dynamically adjust the user-defined output performance specifications, ensuring compliance with input and safety constraints. A key feature of this approach is the integration of multiple constraints into a single adaptive performance function, governed by a simple adaptive law. Additionally, we introduce a robust velocity estimator with a priori-determined performance attributes to reconstruct the unmeasured trajectory/target velocity. Finally, we validate the effectiveness and robustness of the proposed control scheme, through extensive simulations and a real-world experiment.

Motion Coordination of Multiple Autonomous Mobile Robots under Hard and Soft Constraints

This paper presents a distributed approach to the motion control problem for a platoon of unicycle robots moving through an unknown environment filled with static obstacles under multiple hard and soft operational constraints. Each robot has an onboard camera to determine its relative position in relation to its predecessor and proximity sensors to detect and avoid nearby obstascles. Moreover, no robot apart from the leader can independently localize itself within the given workspace. To overcome this limitation, we propose a novel distributed control protocol for each robot of the fleet, utilizing the Adaptive Performance Control (APC) methodology. By utilizing the APC approach to address input constraints via the on-line modification of the error specifications, we ensure that each follower effectively tracks its predecessor without encountering collisions with obstacles, while simultaneously maintaining visual contact with its preceding robot, thus ensuring the inter-robot visual connectivity. Finally, extensive simulation results are presented to demonstrate the effectiveness of the presented control system along with a real-time experiment conducted on an actual robotic system to validate the feasibility of the proposed approach in real-world scenarios.

On the Performance of the Model-Free Adaptive Control For A Novel Moving-Mass Controlled Flying Robot

In this paper, the performance of Model-Free Adaptive Control (MFAC) has been investigated on a novel and specific moving-mass controlled (MMC) flying robot system. The novel one-degree-of-freedom (1 DOF) MMC flying robot test bed presented in this paper has highly nonlinear and slow dynamics with a variable center of gravity (CoG) and moment of inertia. This makes the control of this system a challenging problem. One of the solutions to this challenge is the use of data-driven control methods, in particular, MFAC. This controller uses a data-driven model to control the system using only input and output (I/O) data. This paper compares this data-driven controller with proportional-integral-derivative (PID) control, and Linear Quadratic Regulator (LQR) as two model-free and model-based controllers which are widely used controllers in industry. The results of the comparison show that in the various scenarios applied, MFAC has a clear superiority over the PID and LQR, and its adaptive structure gives more freedom of action in the implementation of different scenarios and the presented noise. The results are obtained using the Integral Time Absolute Error (ITAE) criteria and the mean maximum error has also been compared in a Monte Carlo analysis. For a more detailed study, the amount of control energy consumption was also compared, which showed a clear superiority of the MFAC. Also, the robustness of the controller was demonstrated by introducing uncertainty in the plant parameters and by running 100 Monte Carlo simulations with random initial conditions. Finally, despite the PID controller, the MFAC followed the desired scenarios well and compared to LQR consumed less energy. The results demonstrate that the MFAC outperformed the PID and LQR controllers in the presence of random initial conditions and noise in terms of mean maximum error (70.4%)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$(70.4\\%)$$\\end{document}, mean ITAE (91%)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$(91\\%)$$\\end{document}, and energy consumption (46%)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$(46\\%)$$\\end{document}.

Positive temperature coefficient material based on silicone rubber/ paraffin/ graphite/ carbon nanotubes for wearable thermal management devices

PTC materials provide substantial advantages such as adaptive temperature control performance and excellent safety. However, their use in wearable devices has been limited due to the essential requirements for flexibility and stability. This study prepared a new type of flexible PTC material, with a PTC intensity of 6.3 and a resistivity of only 400 Ω·cm in the low-temperature region. The flexibility of the material is evidenced by its 2 mm bending diameter and its ability to maintain structural integrity even after 100 bends. Furthermore, experimental evidence proves that PTC material has excellent thermal stability and can withstand extreme temperatures ranging from −20 °C to 120 °C. In the thermal control experiment, the PTC material maintains a temperature of approximately 25 °C to accommodate the requirements of the human body in a cold environment. When compared to the conventional PI heating sheet, the newly prepared PTC material exhibits advantages such as rapid response, adaptive temperature control performance, and safety. Consequently, it presents a broad application prospect in the field of wearable heating devices.

Observer-based adaptive tracking control of robotic manipulators with predefined time-guaranteed performance: Theory and experiment

This paper investigates the challenging problem of fixed time trajectory tracking for robotic manipulators under the presence of unavailable model perturbation, external disturbance and from different initial states. Firstly, a novel fixed-time extended state observer (FESO) is designed to estimate and compensate the lumped disturbance, which is analyzed and proved to be stable in the sense of fixed time bounded stability. Secondly, a new type of fixed-time prescribed performance control (FPPC) is constructed to guarantee the system convergences to stable state within a predefined time and enhance transient performance. Furthermore, a novel continuous fixed time nonsingular fast terminal sliding mode variable is established, which addresses singularity obstacle in terminal sliding mode. Together with FESO and FPPC, a new fixed-time adaptive nonsingular fast terminal sliding mode controller (FANFTSMC) is developed. Meanwhile, an adaptive terminal sliding mode reaching law adopted in FANFTSMC promotes the robustness and decreases the chattering phenomenon. Then, the Lyapunov approach is given to clarify the advantages of FANFTSMC in terms of predefined time stabilization and which is demonstrated on a 2-DOF robotic manipulators. Thirdly, theoretical analysis and experiments are presented to illustrate the formulated control strategy owns fine performance and stronger robustness.

Adaptive neural network control of robotic manipulators with input constraints and without velocity measurements

AbstractThis paper addresses the trajectory tracking problem for a class of uncertain manipulator systems under the effect of external disturbances. The main challenges lie in the input constraints and the lack of measurements of joint velocities. An extend‐state‐observer is utilized to estimate the velocity signals; then, a neural‐network‐based adaptive controller is proposed to solve the problem, where a term based on the nominal model is included to enhance the tracking ability, and the effect of uncertainties and disturbances are compensated by a neural‐network term. Compared with the existing methods, the main distinctive features of the presented approach are: (i) The control law is guaranteed to be bounded by design, instead of directly bounded by a saturation function. (ii) The trade‐off between the performance and robustness of the presented controller can be easily tuned by a parameter that depends on the size of model uncertainties and external disturbances. By virtue of the Lyapunov theorem, the convergence properties of the proposed controller are rigorously proved. The performance of the controller is validated via both simulations and experiments conducted on a two‐degree‐of‐freedom robot manipulator.

Traveling control method adapted to different paddy ground conditions with feedforward compensation for crawler combine harvester based on online tracking error prediction

Enhancing the adaptability of agricultural machinery to different ground conditions is paramount for advancing path-tracking accuracy and control stability. Precise online perception of the steering motion state of a crawler combine harvester is essential for adapting to different paddy ground conditions (PGCs) for path-tracking control. In this study, a novel pose vector method (PVM) was proposed for the online determination of the steering radius of a crawler combine harvester. A support vector regression (SVR) model was used to forecast the error-correction factor for the steering trajectory arc radius, and a feedforward compensation control method was designed based on the steering control model. Field steering experiments revealed that the PVM outperformed the conventional least squares method (LSM) in calculating the trajectory arc radius, particularly when dealing with sparse pose data. The relative error of the PVM was several to over 10 times lower than that of the LSM fitting. The field experiments of straight-path tracking showed that the standard deviations of the lateral deviation were 0.078 m, 0.059 m, and 0.062 m, and the standard deviations of the heading deviation were 2.622°, 1.580°, and 1.593°, respectively, for the three different PGCs of soil compaction from low to high. Compared to the conventional pure tracking algorithm, the proposed PGCs adaptive tracking control algorithm enhances the adaptability of the crawler combine harvester to PGCs. These results offer a novel method for enhancing the online perception of the motion state of a crawler combine harvester and improving the adaptive control performance under different PGCs.

π-Light: Programmatic Interpretable Reinforcement Learning for Resource-Limited Traffic Signal Control

The recent advancements in Deep Reinforcement Learning (DRL) have significantly enhanced the performance of adaptive Traffic Signal Control (TSC). However, DRL policies are typically represented by neural networks, which are over-parameterized black-box models. As a result, the learned policies often lack interpretability, and cannot be deployed directly in the real-world edge hardware due to resource constraints. In addition, the DRL methods often exhibit limited generalization performance, struggling to generalize the learned policy to other geographical regions. These factors limit the practical application of learning-based approaches. To address these issues, we suggest the use of an inherently interpretable program for representing the control policy. We present a new approach, Programmatic Interpretable reinforcement learning for traffic signal control (π-light), designed to autonomously discover non-differentiable programs. Specifically, we define a Domain Specific Language (DSL) and transformation rules for constructing programs, and utilize Monte Carlo Tree Search (MCTS) to find the optimal program in a discrete space. Extensive experiments demonstrate that our method consistently outperforms baseline approaches. Moreover, π-Light exhibits superior generalization capabilities compared to DRL, enabling training and evaluation across intersections from different cities. Finally, we analyze how the learned program policies can directly deploy on edge devices with extremely limited resources.

Adaptive control of robotic manipulator with input deadzone and disturbances/uncertainties

This work address the problem of trajectory tracking for robotic manipulator while encountering external disturbances and internal uncertainties. Robotic manipulators have been widely used in various industrial and research applications due to their versatility and efficiency. The precise control of these manipulators is essential for their proper functioning. However, due to the presence of uncertainties and disturbances in the system, it is often challenging to achieve accurate control. The proposed controller is capable of handling the uncertainties and disturbances present in the system, making it robust and efficient for practical applications. The controller is designed based on the concept of second-order nonsingular terminal sliding mode control (SNTSMC), which ensures that the system tracks the desired trajectory accurately. The substitution of the sign function with the saturation function effectively reduces the chattering that is caused by the high-frequency switching element in the conventional SMC approach. By utilizing an adaptive technique, uncertainties can be effectively mitigated without relying on any prior knowledge of an upper limit. Moreover, the Lyapunov stability criterion is used to prove the stability of the closed-loop system. The simulation results show the effectiveness of the proposed algorithm in terms of tracking accuracy and robustness.

Adaptive Cartesian space control of robotic manipulators: A concurrent learning based approach

This work introduces a concurrent learning-based adaptive control design for end-effector tracking and the corresponding stability analysis for robotic manipulators. The presented controller is developed directly in Cartesian space, thereby removing the necessity for inverse kinematics calculations at the position level. The designed adaptive controller ensures global exponential tracking of the end-effector in Cartesian space. Moreover, the developed controller assures globally exponential convergence of uncertain dynamical parameters to their actual values without demanding persistence excitation conditions via a combination of a standard gradient-based adaptation with a novel integral concurrent learning component. The developed integral concurrent learning part operates both real-time output data and the most informative historical data gathered by employing the singular-value maximization algorithm (SVMA) to reduce the size of memory allocation. Lyapunov-based arguments are applied to ensure the exponential stability of the closed-loop system. Numerical studies are performed to depict the feasibility and performance of the proposed design.

Characterizations for eWLB (Embedded Wafer Level Ball Grid Array) Antenna in Molded Package Integrations in 77GHz Automotive Applications

The worldwide IC market has had a significant growth rate in recent years, driving diversified package solutions in the segments of communication, computing, consumer, automotive, industrial, etc. Among these market segments, the automotive market is forecast to have the strongest CAGR (Compound Annual Growth Rate) of any of the end-use segments in the next 5 years, which results in more IC packages and components being designed into automotive vehicle systems such as infotainment, ADAS (Advance Driving Assistance System), instrumentation, body systems, battery control systems and so on. In these applications, the 77GHz short-range radar sensor application that can enable higher EIRP (Effective Isotropic Radiated Power) and adaptive cruise control performance has become more attractive to be commercialized. Due to the benefits of shorter interconnection length, lower conductive loss with smooth Cu surface as well as a lower dielectric constant and dielectric loss of materials in a fan-out wafer level package (FOWLP) technology, the FOWLP is proven as a suitable packaging solution to achieve the high speed and frequency requirements in 5G and mmWave applications. The eWLB Embedded Wafer Level Ball Grid Array) is a versatile FOWLP technology, providing a robust package platform in a space-efficient design with the very dense interconnection and routing of multiple dies as well as a smaller footprint and lower package profile providing a reliable package solution. Meanwhile, since the utilization of an antenna that is integrated into the packages has been studied as a lowcost solution because the packages can be tested in normal FR4 printed circuit board (PCB) instead of the RF function integrated PCB, the eWLB integrated with molded antenna chip packages (called eWLB antenna in molded package) technology is introduced in this paper. The characterizations for assembly process challenges are evaluated and illustrate the process optimizations of equipment handling, antenna molded chip package fabrication, reconstitution process control and warpage characterization of the molded wafer. In addition, the radio frequency (RF) test validation is performed to verify the performance of scattering parameter (S-parameter), radiation pattern, impedance and detection range of the antenna. In order to prove the reliable quality and yield of the evaluated eWLB antenna in molded packages, package-level reliability tests are studied. Through these results, it will show that this eWLB antenna in molded package integration is a robust and costeffective solution for 77GHz automotive applications.

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.

Adaptive Neural Network Tracking Control of Robotic Manipulators Based on Disturbance Observer

This article presents an adaptive neural network (ANN) control scheme based on a disturbance observer that can achieve trajectory tracking control of robotic manipulators under external disturbances and dynamic model uncertainties. Firstly, an ANN controller based on full-state feedback is derived using the backstepping technique to achieve an online approximation of uncertainty. The integral sliding mode surface with a position error is introduced into the controller, which reduces the steady-state error of the system and enhances robustness. Then, a novel disturbance observer is designed to estimate both the approximation errors of the ANN and external disturbances, and to provide compensation for the controller, effectively suppressing the trajectory tracking errors caused by approximation errors and disturbances. Subsequently, the Lyapunov stability theory is utilized to demonstrate the stability of the developed control strategy and the boundedness of all closed-loop signals. Finally, numerical simulations are used to confirm the efficacy of the proposed control method.

Adaptive Tracking Control of Robotic Manipulator Subjected to Actuator Saturation and Partial Loss of Effectiveness

Abstract This paper introduces an adaptive control design tailored for robotic systems described by Euler–Lagrange equations under actuator saturation and partial loss of effectiveness. The adaptive law put forth not only retains conventional control properties but also extends its scope to effectively address challenges posed by actuator saturation and partial loss of effectiveness. The framework’s primary focus is on bolstering system robustness, thereby ensuring the achievement of uniformly ultimate bounded tracking errors. The stability and convergence of the system’s behavior are rigorously established through the application of the Lyapunov analysis technique. Moreover, the effectiveness and superiority of the introduced framework are compellingly demonstrated through a series of practical simulations and experimental instances.

Adaptive control of robot manipulators with closed architecture

This paper focuses on the adaptive control of robot manipulators with closed architecture and parametric uncertainty. We propose a new class of adaptive controllers via exploiting the potential as applying the forwardstepping approach and the input–output properties of an inverted form of the dynamics of robots with closed architecture. The condition that ensures the stability and convergence of the closed-loop system becomes rather moderate due to the new design and analysis, and the result presented further enhances the connection between nonlinear control theory and most industrial/commercial robotic applications.

An improved indirect adaptive neural control performance based on MOPSO approach: An experimental validation via a transesterification reactor

This paper proposes an indirect adaptive control method based on recurrent neural networks. To achieve satisfactory closed-loop performances, a neural emulator (NE) and a neural controller (NC) adapting rates are established using the multiobjective particle swarm optimization (MOPSO) algorithm. The proposed MOPSO algorithm has been designed to minimize, simultaneously, two separated objective functions: the emulation and the tracking errors. The proposed approach guarantees that the NE tracks the system dynamics within a short time window. Consequently, it provides for the suggested control structure useful information to synthesize optimal adaptive rates of the NE and NC. To validate the effectiveness of the proposed MOPSO algorithm, a numerical example and an experimental validation on a chemical reactor are proposed.

All-weather anti-icing and de-icing properties of the carbon-based electro@photo-thermal material with adaptive temperature control performance

Photo-thermal anti/de-icing and electro-thermal anti/de-icing have been widely used in the field of anti/de-icing in recent years. The combination of electro@photo-thermal can achieve all-weather anti/de-icing. However, there is no need for anti/de-icing in hot environments, but the photo-thermal conversion does not automatically stop. And the photo-thermal conversion will cause the surface to overheat, seriously affecting the safety and normal operation of the machine, which is a great challenge in practical applications. Based on this, an electro@photo-thermal material with adaptive temperature control performance was proposed in this work. Specifically, the carbon cloth was used as an electro-thermal substrate, and the thermochromic powder mixed with polydimethylsiloxane (PDMS) was used as a photo-thermal component. The photo-thermal conversion experiment shows that when the temperature reaches the threshold temperature, the color of the material can be changed from black to white to reduce the absorption of sunlight, thus avoiding the overheating phenomenon. The anti/de-icing experiment also shows that the material meets the all-weather anti/de-icing requirements. In addition, continuous tape stripping experiments and acid-base soaking experiments also verified the excellent hydrophobicity of the material. The research provides new ideas for all-weather anti/de-icing systems with adaptive temperature control performance.

A Two-Layer Trajectory Tracking Control Scheme of Manipulator Based on ELM-SMC for Autonomous Robotic Vehicle

With the development of autonomous driving technology, some special vehicles should also be developed into autonomous driving vehicles. The robotic vehicles are widely used in engineering operations, and the robotic manipulator is a common tool mounted on robotic vehicles. The autonomous driving of robotic vehicles requires not only automatic movement, but also the automation of the robotic manipulator drive system. Therefore, this paper proposes an adaptive trajectory tracking control of manipulator based on sliding mode control (SMC), which can more accurately describe the joint space movement of the manipulator system under multi task coupling and external interference. Considering the multi-tasking situation, the influence of obstacle avoidance and base tilt on the trajectory of manipulator is investigated by using extreme learning machine (ELM), and the framework of machine learning is established. The output based on ELM-SMC is used as the bottom-layer control. Then an effective trajectory compensation method is proposed as the top-level control to avoid the error accumulation in the periodic repeated operation of the manipulator. Finally, the application effect of trajectory error tracking and compensation of manipulator in complex tasks of dynamic environment is verified by simulation and experiments, which lays a foundation for the stability control of manipulator in practical complex engineering applications. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —The motivation of this paper is to solve the problem of poor trajectory tracking control accuracy of the manipulator caused by the change of the direction of the manipulator base. It is suitable for the high-precision position control of the manipulator assembled on the mobile robotic vehicle. Nowadays, the autonomous driving technology is developing rapidly, and the automation of traffic will be widely popularized in the future. Therefore, we should not only pay attention to the autonomous driving function of passenger vehicles, but also give consideration to the autonomous driving technology of robotic vehicles applied in engineering. It not only needs to complete the driving function, but also needs to ensure the reliable operation of the manipulator. In this paper, we mathematically deduce the control model of the manipulator. Then, we use SMC as the trajectory tracking control method of the manipulator, and use ELM to estimate the trajectory error caused by the end trajectory of the manipulator in the non-horizontal state of the base. Furthermore, we propose an optimization algorithm to optimize the compensation coefficient of cubic spline and give a reasonable compensation scheme. Through simulation and experiments, it is preliminarily verified that the schemes proposed in this paper are feasible, but they lack the application verification in the actual robotic vehicles, and the model fitting part of machine learning is completed with the help of the host computer, and the calculation ability of the controller of the actual manipulator may also be the limitation of completing this scheme.

Adaptive Tracking Control of Robotic Manipulators With Unknown Kinematics and Uncertain Dynamics

Adaptive Tracking Control of Robotic Manipulators With Unknown Kinematics and Uncertain Dynamics