Trajectory Tracking Control Design for Dual-Arm Robots Using Dynamic Surface Controller

This paper presents a dynamic surface controller (DSC) for dual-arm robots (DAR) tracking desired trajectories. The DSC algorithm is based on backstepping technique and multiple sliding surface control principle, but with an important addition. In the design of DSC, low-pass filters are included which prevent the complexity in computing due to the “explosion of terms”, i.e. the number of terms in the control law rapidly gets out of hand. Therefore, a controller constructed from this algorithm is simulated on a four degrees of freedom (DOF) dual-arm robot with a complex kinetic dynamic model. Moreover, the stability of the control system is proved by using Lyapunov theory. The simulation results show the effectiveness of the controller which provide precise tracking performance of the manipulator

Development of Novel Compound Controllers to Reduce Chattering of Sliding Mode Control

The robotics and dynamic systems constantly encountered with disturbances such as micro electro mechanical systems (MEMS) gyroscope under disturbances result in mechanical coupling terms between two axes, friction forces in exoskeleton robot joints, and unmodelled dynamics of robot manipulator. Sliding mode control (SMC) is a robust controller. The main drawback of the sliding mode controller is that it produces high-frequency control signals, which leads to chattering. The research objective is to reduce chattering, improve robustness, and increase trajectory tracking of SMC. In this research, we developed controllers for three different dynamic systems: (i) MEMS, (ii) an Exoskeleton type robot, and (iii) a 2 DOF robot manipulator. We proposed three sliding mode control methods such as robust sliding mode control (RSMC), new sliding mode control (NSMC), and fractional sliding mode control (FSMC). These controllers were applied on MEMS gyroscope, Exoskeleton robot, and robot manipulator. The performance of the three proposed sliding mode controllers was compared with conventional sliding mode control (CSMC). The simulation results verified that FSMC exhibits better performance in chattering reduction, faster convergence, finite-time convergence, robustness, and trajectory tracking compared to RSMC, CSMC, and NSFC. Also, the tracking performance of NSMC was compared with CSMC experimentally, which demonstrated better performance of the NSMC controller

Robotic Manipulator Control in the Presence of Uncertainty

openThis research focuses on the problem of manipulator control in the presence of uncertainty and aims to compare different approaches for handling uncertainty while developing robust and adaptive methods that can control the robot without explicit knowledge of uncertainty bounds. Uncertainty is a pervasive challenge in robotics, arising from various sources such as sensor noise, modeling errors, and external disturbances. Effectively addressing uncertainty is crucial for achieving accurate and reliable manipulator control. The research will explore and compare existing methods for uncertainty handling such as robust feedback linearization , sliding mode control and robust adaptive control. These methods provide mechanisms to model and compensate for uncertainty in the control system. Additionally, modified robust and adaptive control methods will be developed that can dynamically adjust control laws based on the observed states, without requiring explicit knowledge of uncertainty bounds. To evaluate the performance of the different approaches, comprehensive experiments will be conducted on a manipulator platform. Various manipulation tasks will be performed under different levels of uncertainty, and the performance of each control approach will be assessed in terms of accuracy, stability, and adaptability. Comparative analysis will be conducted to highlight the strengths and weaknesses of each method and identify the most effective approach for handling uncertainty in manipulator control. The outcomes of this research will contribute to the advancement of manipulator control by providing insights into the effectiveness of different approaches for uncertainty handling. The development of new robust and adaptive control methods will enable manipulators to operate in uncertain environments without requiring explicit knowledge of uncertainty bounds. Ultimately, this research will facilitate the deployment of more reliable and adaptive robotic systems capable of handling uncertainty and improving their performance in various real-world applications.This research focuses on the problem of manipulator control in the presence of uncertainty and aims to compare different approaches for handling uncertainty while developing robust and adaptive methods that can control the robot without explicit knowledge of uncertainty bounds. Uncertainty is a pervasive challenge in robotics, arising from various sources such as sensor noise, modeling errors, and external disturbances. Effectively addressing uncertainty is crucial for achieving accurate and reliable manipulator control. The research will explore and compare existing methods for uncertainty handling such as robust feedback linearization , sliding mode control and robust adaptive control. These methods provide mechanisms to model and compensate for uncertainty in the control system. Additionally, modified robust and adaptive control methods will be developed that can dynamically adjust control laws based on the observed states, without requiring explicit knowledge of uncertainty bounds. To evaluate the performance of the different approaches, comprehensive experiments will be conducted on a manipulator platform. Various manipulation tasks will be performed under different levels of uncertainty, and the performance of each control approach will be assessed in terms of accuracy, stability, and adaptability. Comparative analysis will be conducted to highlight the strengths and weaknesses of each method and identify the most effective approach for handling uncertainty in manipulator control. The outcomes of this research will contribute to the advancement of manipulator control by providing insights into the effectiveness of different approaches for uncertainty handling. The development of new robust and adaptive control methods will enable manipulators to operate in uncertain environments without requiring explicit knowledge of uncertainty bounds. Ultimately, this research will facilitate the deployment of more reliable and adaptive robotic systems capable of handling uncertainty and improving their performance in various real-world applications

Robust tracking control of a flexible manipulator with limited control input based on backstepping and the Nussbaum function

A flexible manipulator is a versatile automated device with a wide range of applications, capable of performing various tasks. However, these manipulators are often vulnerable to external disturbances and face limitations in their ability to control actuators. These factors significantly impact the precision of tracking control in such systems. This study delves into the problem of attitude tracking control for a flexible manipulator under the constraints of control input limitations and the influence of external disturbances. To address these challenges effectively, we first introduce the backstepping method, aiming to achieve precise state tracking and tackle the issue of external disturbances. Additionally, recognizing the constraints posed by control input limitations in the flexible manipulator's actuator control system, we employ a design approach based on the Nussbaum function. This method is designed to overcome these limitations, allowing for more robust control. To validate the effectiveness and disturbance rejection capabilities of the proposed control strategy, we conduct comparative numerical simulations using MATLAB/Simulink. These simulations provide further evidence of the robustness and reliability of the control strategy, even in the presence of external disturbances and control input limitations

Adaptive sliding mode control for uncertain wheel mobile robot

In this paper a simple adaptive sliding mode controller is proposed for tracking control of the wheel mobile robot (WMR) systems. The WMR are complicated systems with kinematic and dynamic model so the error dynamic model is built to simplify the mathematical model. The sliding mode control then is designed for this error model with the adaptive law to compensate for the mismatched. The proposed control scheme in this work contains only one control loop so it is simple in both implementation and mathematical calculation. Moreover, the requirement of upper bounds of disturbance that is popular in the sliding mode control is cancelled, so it is convenient for real world applications. Finally, the effectiveness of the presented algorithm is verified through mathematical proof and simulations. The comparison with the existing work is also executed to evaluate the correction of the introduced adaptive sliding mode controller. Thoroughly, the settling time, the peak value, the integral square error of the proposed control scheme reduced about 50% in comparison with the compared disturbance observer based sliding mode control

Nonlinear Control Strategies for Outdoor Aerial Manipulators

In this thesis, the design, validation and implementation of nonlinear control strategies for aerial manipulators {i.e. aerial robots equipped with manipulators{ is studied, with special emphasis on the internal coupling of the system and its resilience against external disturbances. For the rst, di erent decentralised control strategies {i.e. using di erent control typologies for each one of the subsystems{ that indirectly take into account this coupling have been analysed. As a result, a nonlinear strategy composed of two controllers is proposed. A higher priority is given to the manipulation accuracy, relaxing the platform tracking, and hence obtaining a solution improving the manipulation capabilities with the surrounding environment. To validate these results, thorough stability and robustness analyses are provided, both theoretically and in simulation. On the other hand, a signi cant e ort has been devoted to improving the response and applicability of robot manipulators used in ight via control. In particular, the design of controllers for lightweight exible manipulators {that reduce the consequences of incidents involving unforeseen contacts{ is analysed. Although their inherent nature perfectly ts for aerial manipulation applications, the added exibility produces unwanted behaviours, such as second-order modes and uncertainties. To cope with them, an adaptable position nonlinear control strategy is proposed. To validate this contribution, the stability of the approach is studied in theory and its capabilities are proven in several experimental scenarios. In these, the robustness of the solution against unforeseen impacts and contact with uncharacterised interfaces is demonstrated. Subsequently, this strategy has been enriched with {multiaxis{ force control capabilities thanks to the inclusion of an outer control loop modifying the manipulator reference. Accordingly, this additional applicationfocused capability is added to the controlled system without loosing the modulated response of the inner-loop position strategy. It is also worth noting that, thanks to the cascade-like nature of the modi cation, the transition between position and force control modes is inherently smooth and automatic. The stability of this expanded strategy has been theoretically analysed and the results validated in a set of experimental scenarios. To validate the rst nonlinear approach with realistic outdoor simulations before its implementation, a computational uid dynamics analysis has been performed to obtain an explicit model of the aerodynamic forces and torques applied to the blunt-body of the aerial platform in ight. The results of this study have been compared to the most common alternative nowadays, being highlighted that the proposed model signi cantly surpasses this option in terms of accuracy. Moreover, it is worth underscoring that this characterisation could be also employed in the future to develop control solutions with enhanced rejection capabilities against wind conditions. Finally, as the focus of this thesis is on the use of novel control strategies on real aerial manipulation outdoors to improve their accuracy while performing complex tasks, a modular autopilot solution to be able to implement them has been also developed. This general-purpose autopilot allows the implementation of new algorithms, and facilitates their theory-to-experimentation transition. Taking into account this perspective, the proposed tool employs the simple and widely-known MAS interface and the highly reliable PX4 autopilot as backup, thus providing a redundant approach to handle unexpected incidents in ight.En esta tesis se ha estudiado el diseño, validación e implementación de estrategias de control no lineales para robots manipuladores aéreos –esto es, robots aéreos equipados con un sistema de manipulación robótica–, dándose especial énfasis a las interacciones internas del sistema y a su resiliencia frente a efectos externos. Para lo primero, se han analizado diferentes estrategias de control descentralizado –es decir, que usan tipologías de control diferentes para cada uno de los subsistemas–, pero que tienen indirectamente en consideración la interacción entre manipulación y vuelo. Como resultado de esta línea, se propone una estretegia de control conformada por dos controladores. Estos se coordinan de tal forma que se le da prioridad a la manipulación sobre el seguimiento de posiciones del vehículo, produciéndose un sistema de control que mejora la precisión de las interacciones entre el sistema manipulador y el entorno. Para validar estos resultados, se ha analizado su estabilidad y robustez tanto teóricamente como mediante simulaciones numéricas. Por otro lado, se ha buscado mejorar la respuesta y aplicabilidad de los manipuladores que se usan en vuelo mediante su control. Dentro de esta tendencia, la tesis se ha centrado en el diseño de controladores para manipuladores ligeros flexibles, ya que estos permiten reducir el peso del sistema completo y reducen el riesgo de incidentes debidos a contactos inesperados. Sin embargo, la flexibilidad de estos produce comportamientos indeseados durante la operación, como la aparición de modos de segundo orden y cierta incentidumbre en su comportamiento. Para reducir su impacto en la precisión de las tareas de manipulación, se ha desarrollado un controlador no lineal adaptable. Para validar estos resultados, se ha analizado la estabilidad del sistema teóricamente y se han desarrollado una serie de experimentos. En ellos, se ha comprobado su robustez ante impactos inesperados y contactos con elementos no caracterizados. Posteriormente, esta estrategia para manipuladores flexibles ha sido ampliada al añadir un bucle externo que posibilita el control en fuerzas en varias direcciones. Esto permite, mediante un único controlador, mantener la suave respuesta de la estrategia. Además cabe destacar que, al contar esta estrategia con un diseño en cascade, la transición entre los segmentos de desplazamiento del brazo y de aplicación de fuerzas es fluida y automática. La estabilidad de esta estrategia ampliada ha sido analizada teóricamente y los resultados han sido validados experimentalmente. Para validar la primera estrategia mediante simulaciones que representen fielmente las condiciones en exteriores antes de su implementación, ha sido necesario realizar un estudio mediante mecánica de fluidos computacional para obtener un modelo explícito de las fuerzas y momentos aerodinámicos a los que se efrenta la plataforma en vuelo. Los resultados de este estudio han sido comparados con la alternativa más empleada actualmente, mostrándose que los avances del método propuesto son sustanciales. Asimismo, es importante destacar que esta caracterización podría también usarse en el futuro para desarrollar controladores con una respuesta mejorada ante perturbaciones aerodinámicas, como en el caso de volar con viento. Finalmente, al ser esta una tesis centrada en las estrategias de control novedosas en sistemas reales para la mejora de su rendimiento en misiones complejas, se ha desarrollado un autopiloto modular fácilmente modificable para implementarlas. Este permite validar experimentalmente nuevos algoritmos y facilita la transición entre teoría y práctica. Para ello, esta herramienta se basa en una interfaz sencilla ampliamente conocida por los investigadores de robótica, Simulink®, y cuenta con un autopiloto de respaldo, PX4, para enfrentarse a los incidentes inesperados que pudieran surgir en vuelo

A Biologically Inspired Framework for the Intelligent Control of Mechatronic Systems and Its Application to a Micro Diving Agent

Mechatronic systems are becoming an intrinsic part of our daily life, and the adopted control approach in turn plays an essential role in the emulation of the intelligent behavior. In this paper, a framework for the development of intelligent controllers is proposed. We highlight that robustness, prediction, adaptation, and learning, which may be considered the most fundamental traits of all intelligent biological systems, should be taken into account within the project of the control scheme. Hence, the proposed framework is based on the fusion of a nonlinear control scheme with computational intelligence and also allows mechatronic systems to be able to make reasonable predictions about its dynamic behavior, adapt itself to changes in the plant, learn by interacting with the environment, and be robust to both structured and unstructured uncertainties. In order to illustrate the implementation of the control law within the proposed framework, a new intelligent depth controller is designed for a microdiving agent. On this basis, sliding mode control is combined with an adaptive neural network to provide the basic intelligent features. Online learning by minimizing a composite error signal, instead of supervised off-line training, is adopted to update the weight vector of the neural network. The boundedness and convergence properties of all closed-loop signals are proved using a Lyapunov-like stability analysis. Numerical simulations and experimental results obtained with the microdiving agent demonstrate the efficacy of the proposed approach and its suitableness for both stabilization and trajectory tracking problems.</p

Fractional multi-loop active disturbance rejection control for a lower knee exoskeleton system

Rehabilitation Exoskeleton is becoming more and more important in physiotherapists’ routine work. To improve the treatment performance, such as reducing the recovery period and/or monitoring and reacting to unpredictable situations, the rehabilitation manipulators need to help the patients in various physical trainings. A special case of the active disturbance rejection control (ADRC) is applied to govern a proper realisation of basic limb rehabilitation trainings. The experimental study is performed on a model of a flexible joint manipulator, whose behaviour resembles a real exoskeleton rehabilitation device (a one-degree-of-freedom, rigid-link, flexible-joint manipulator). The fractional (FADRC) is an unconventional model-independent approach, acknowledged as an effective controller in the existence of total plant uncertainties, and these uncertainties are inclusive of the total disturbances and unknown dynamics of the plant. In this work, three FADRC schemes are used, the first one using a fractional state observer (FSO), or FADRC1, second one using a fractional proportional-derivative controller (FPD), or FADRC2, and the third one a Multi-loop fractional in PD-loop controller and the observer-loop (Feedforward and Feedback), or FADRC3. The simulated Exoskeleton system is subjected to a noise disturbance and the FADRC3 shows the effectiveness to compensate all these effects and satisfies the desired position when compared with FADRC1 and FADRC2. The design and simulation were carried out in MATLAB/Simulink

Control of Flexible Manipulator Robots Based on Dynamic Confined Space of Velocities: Dynamic Programming Approach

Linear Parameter Varying models-based Model Predictive Control (LPV-MPC) has stood out in manipulator robots because it presents well-rejection to dynamic uncertainties in flexible joints. However, it has become too weak when the MPC's optimization problem does not include kinematic constraints-based conditions. This paper uses dynamic confined space of velocities (DCSV) to include these conditions as a recursive polytopic constraint, guaranteeing optimal dependency on a simplex scheduling parameter. To this end, the local frame's velocities and torque/force preload of joints (related to violation of kinematic constraints) are associated with different time scale dynamics such that DCSV correlates them as a polytope. So, a classical LPV-MPC will be updated using a dynamic programming approach according to the DCSV-based polytope. As a result, one lemma about DCSV-based recursive polytope and a five-step procedure for two decoupled close-loop schemes with different time scales compose the LPV-MPC proposed method. Numerical validation shows that even for relevant flexibility situations, trajectory tracking performance is improved by tuning finite horizons and optimization problem constraints regarding DCSV's behavior