Control Strategy For Robotic Manipulator Research Articles

Guaranteed real-time cooperative collision avoidance for n-DOF manipulators

Abstract This paper presents a decentralized, cooperative, real-time avoidance control strategy for robotic manipulators. The proposed avoidance control law builds on the concepts of artificial potential field functions and provides tighter bounds on the minimum safe distance when compared to traditional potential-based controllers. Moreover, the proposed avoidance control law is given in analytical, continuous closed form, avoiding the use of optimization techniques and discrete algorithms, and is rigorously proven to guarantee collision avoidance at all times. Examples of planar and 3D manipulators with cylindrical links under the proposed avoidance control are given and compared with the traditional approach of modeling links and obstacles with multiple spheres. The results show that the proposed avoidance control law can achieve, in general, faster convergence, smaller tracking errors, and lower control torques than the traditional approach. Furthermore, we provide extensions of the avoidance control to robotic manipulators with bounded control torques.

Enhancing Underwater Robot Manipulators with a Hybrid Sliding Mode Controller and Neural-Fuzzy Algorithm

The sliding mode controller stands out for its exceptional stability, even when the system experiences noise or undergoes time-varying parameter changes. However, designing a sliding mode controller necessitates precise knowledge of the object’s exact model, which is often unattainable in practical scenarios. Furthermore, if the sliding control law’s amplitude becomes excessive, it can lead to undesirable chattering phenomena near the sliding surface. This article presents a new method that uses a special kind of computer program (Radial Basis Function Neural Network) to quickly calculate complex relationships in a robot’s control system. This calculation is combined with a technique called Sliding Mode Control, and Fuzzy Logic is used to measure the size of the control action, all while making sure the system stays stable using Lyapunov stability theory. We tested this new method on a robot arm that can move in three different ways at the same time, showing that it can handle complex, multiple-input, multiple-output systems. In addition, applying LPV combined with Kalman helps reduce noise and the system operates more stably. The manipulator’s response under this controller exhibits controlled overshoot (Rad), with a rise time of approximately 5 ± 3% seconds and a settling error of around 1%. These control results are rigorously validated through simulations conducted using MATLAB/Simulink software version 2022b. This research contributes to the advancement of control strategies for robotic manipulators, offering improved stability and adaptability in scenarios where precise system modeling is challenging.

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.

Discrete Robust Control of Robot Manipulators Using an Uncertainty and Disturbance Estimator

AbstractThis article presents the design of a robust observer based on the discrete-time (DT) formulation of uncertainty and disturbance estimator (UDE), a well-known robust control technique, for the purpose of controlling robot manipulators. The design results in a complete closed-loop, robust, and controller–observer structure. The observer incorporates the estimate of the overall uncertainty associated with the plant, in order to mimic its dynamics, and the control law is generated using an auxiliary error instead of state tracking error. A detailed qualitative and quantitative stability analysis is provided, and simulations are performed on the two-link robot manipulator system. Further, a comparative study with well-known control strategies for robot manipulators is presented. The results demonstrate the efficacy of the proposed technique, with better tracking performance and lower control energy compared to other strategies.

A Review on Fractional-Order Modelling and Control of Robotic Manipulators

Robot manipulators are widely used in many fields and play a vital role in the assembly, maintenance, and servicing of future complex in-orbit infrastructures. They are also helpful in areas where it is undesirable for humans to go, for instance, during undersea exploration, in radioactive surroundings, and other hazardous places. Robotic manipulators are highly coupled and non-linear multivariable mechanical systems designed to perform one of these specific tasks. Further, the time-varying constraints and uncertainties of robotic manipulators will adversely affect the characteristics and response of these systems. Therefore, these systems require effective modelling and robust controllers to handle such complexities, which is challenging for control engineers. To solve this problem, many researchers have used the fractional-order concept in the modelling and control of robotic manipulators; yet it remains a challenge. This review paper presents comprehensive and significant research on state-of-the-art fractional-order modelling and control strategies for robotic manipulators. It also aims to provide a control engineering community for better understanding and up-to-date knowledge of fractional-order modelling, control trends, and future directions. The main table summarises around 95 works closely related to the mentioned issue. Key areas focused on include modelling, fractional-order modelling type, model order, fractional-order control, controller parameters, comparison controllers, tuning techniques, objective function, fractional-order definitions and approximation techniques, simulation tools and validation type. Trends for existing research have been broadly studied and depicted graphically. Further, future perspective and research gaps have also been discussed comprehensively.

Fixed-time compliant motion/force control of robotic manipulators with environmental constraints

PurposeThe purpose of this paper is to design a new compliant motion/force control strategy for robotic manipulators with environmental constraints in the sense of fixed-time stability.Design/methodology/approachThis paper investigates a novel compliant motion/force control strategy for robotic manipulators with environmental constraints. By using the Lyapunov theory and fixed-time stability theory, a non-singular terminal sliding mode manifold is first established. Then, the compliant motion/force controller is designed, and relevant conditions are given for guaranteeing that the robotic manipulator can track the prescribed constrained trajectory while exerting a desired force to the environment in fixed-time. An illustrative example is presented to show the effectiveness of our proposed control strategy.FindingsBased on fixed-time stability theory, the desired compliant motion/force controller for robotic manipulators with environmental constraints is developed.Originality/valueCompared with most existing literature, the proposed fixed-time compliant motion/force control strategy can provide the upper bound of the settling time independent of the initial conditions in designing procedure and is more practical for the real-world applications.

Linguistic Lyapunov reinforcement learning control for robotic manipulators

We propose a Lyapunov theory based linguistic reinforcement learning (RL) framework for stable tracking control of robotic manipulators. In particular, we employ Lyapunov theory to constrain fuzzy rule consequents for ensuring stability of the designed controller. Proposed fuzzy RL controller employs Lyapunov theory dictated rules for discovering an optimal yet stable control strategy for robotic manipulators. Furthermore, our proposed linguistic RL controller handles payload variations and external disturbances quite effectively. We validate linguistic Lyapunov RL controller on two benchmark control problems: (i) a standard two-link robotic arm manipulator, and (ii) a two link selective compliance assembly robotic arm (SCARA). Simulation results and comparison against (a) baseline fuzzy Q learning (FQL) controller, and (b) a recently proposed Lyapunov theory based Markov game controller showcases our controller's superior tracking performance and lower computational complexity. Furthermore, our controller exhibits high stability with disturbances and payload variations.

Adaptive control algorithm of flexible robotic gripper by extreme learning machine

Adaptive grippers should be able to detect and recognize grasping objects. To be able to do it control algorithm need to be established to control gripper tasks. Since the gripper movements are highly nonlinear systems it is desirable to avoid using of conventional control strategies for robotic manipulators. Instead of the conventional control strategies more advances algorithms can be used. In this study several soft computing methods are analyzed for robotic gripper applications. The gripper structure is fully compliant with embedded sensors. The sensors could be used for grasping shape detection. As soft computing methods, extreme learning machine (ELM) and support vector regression (SVR) were established. Also other soft computing methods are analyzed like fuzzy, neuro-fuzzy and artificial neural network approach. The results show the highest accuracy with ELM approach than other soft computing methods. Controlling input displacement of a new adaptive compliant gripper.This design of the gripper with embedded sensors as part of its structure.To build an effective prediction model of input displacement of gripper.The impact of the variation in the input parameters.

Design Robust Fuzzy Sliding Mode Control Technique for Robot Manipulator Systems with Modeling Uncertainties

This paper describes the design and implementation of robust nonlinear sliding mode control strategies for robot manipulators whose dynamic or kinematic models are uncertain. Therefore a fuzzy sliding mode tracking controller for robot manipulators with uncertainty in the kinematic and dynamic models is design and analyzes. The controller is developed based on the unit quaternion representation so that singularities associated with the otherwise commonly used three parameter representations are avoided. Simulation results for a planar application of the continuum or hyper-redundant robot manipulator (CRM) are provided to illustrate the performance of the developed adaptive controller. These manipulators do not have rigid joints, hence, they are difficult to model and this leads to significant challenges in developing high-performance control algorithms. In this research, a joint level controller for continuum robots is described which utilizes a fuzzy methodology component to compensate for dynamic uncertainties.

Quasi sliding mode‐based single input fuzzy self‐tuning decoupled fuzzy PI control for robot manipulators with uncertainty

SUMMARYThis paper presents a robust adaptive control strategy for robot manipulators, based on the coupling of the fuzzy logic control with the so‐called sliding mode control (SMC) approach. The motivation for using SMC in robotics mainly relies on its appreciable features. However, the drawbacks of the conventional SMC, such as chattering effect and required a priori knowledge of the bounds of uncertainties can be destructive. In this paper, these problems are suitably circumvented by adopting a reduced rule base single input fuzzy self tuning decoupled fuzzy proportional integral sliding mode control approach. In this new approach a decoupled fuzzy proportional integral control is used and a reduced rule base single input fuzzy self‐tuning controller as a supervisory fuzzy system is added to adaptively tune the output control gain of the decoupled fuzzy proportional integral control. Moreover, it is proved that the fuzzy control surface of the single‐input fuzzy rule base is very close to the input/output relation of a straight line. Therefore, a varying output gain decoupled fuzzy proportional integral sliding mode control approach using an approximate line equation is then proposed. The stability of the system is guaranteed in the sense of the Lyapunov theorem. Simulations using the dynamic model of a 3DOF planar manipulator with uncertainties show the effectiveness of the approach in high speed trajectory tracking problems. The simulation results that are compared with the results of conventional SMC indicate that the control performance of the robot system is satisfactory and the proposed approach can achieve favorable tracking performance, and it is robust with regard to uncertainties and disturbances. Copyright © 2011 John Wiley & Sons, Ltd.

Adaptive fuzzy sliding mode control using supervisory fuzzy control for 3 DOF planar robot manipulators

Control of an industrial robot includes nonlinearities, uncertainties and external perturbations that should be considered in the design of control laws. This paper presents a control strategy for robot manipulators, based on the coupling of the fuzzy logic control with the so-called sliding mode control, SMC, approach. The motivation for using SMC in robotics mainly relies on its appreciable features, such as design simplicity and robustness. Yet, the chattering effect, typical of the conventional SMC, can be destructive. In this paper, this problem is suitably circumvented by adopting an adaptive fuzzy sliding mode control, AFSMC, approach with a proportional-integral-derivative, PID sliding surface. For this proposed approach, we have used a fuzzy logic control to generate the hitting control signal. Moreover, the output gain of the fuzzy sliding mode control, FSMC, is tuned on-line by a supervisory fuzzy system, so the chattering is avoided. The stability of the system is guaranteed in the sense of the Lyapunov stability theorem. Numerical simulations using the dynamic model of a 3 DOF planar rigid robot manipulator with uncertainties show the effectiveness of the approach in trajectory tracking problems. The simulation results that are compared with the results of conventional SMC with PID sliding surface indicate that the control performance of the robot system is satisfactory and the proposed AFSMC can achieve favorable tracking performance, and it is robust with regard to uncertainties and disturbances.

A novel robust adaptive sliding mode control using fuzzy self-tuning PID controller for 3 DOF planar robot manipulators

This paper presents a new robust adaptive control strategy for robot manipulators, based on the coupling of the fuzzy PID logic control with the so-called sliding mode control, SMC, approach. The motivation for using SMC in robotics mainly relies on its appreciable features, such as design simplicity and robustness. However, the drawbacks of conventional SMC, such as chattering effects and a required priori knowledge of the bounds of uncertainties can be destructive. In this paper, these problems are suitably circumvented by adopting a robust adaptive fuzzy sliding mode control, RAFSMC, approach with a PID sliding surface and fuzzy self-tuning PID, FSTPID, controller. In this new approach the discontinuous term is replaced by a new efficient estimation approach to estimate the perturbation; and a fuzzy self-tuning PID controller as a feedback term to enhance closed-loop stability by using a supervisory fuzzy system to adaptively tune the control gain of the PID control. The stability of the system is guaranteed in the sense of the Lyapunov stability theorem. Numerical simulations using the dynamic model of a three DOF planar rigid robot manipulator with uncertainties show the effectiveness of the approach in high speed trajectory tracking problems. The simulation results when compared with the results of conventional SMC indicate that the control performance of the robot system is satisfactory and that the proposed approach can achieve favorable tracking performance and is robust with regard to uncertainties and disturbances.

Design and experimental validation of a second-order sliding-mode motion controller for robot manipulators

This article presents an original motion control strategy for robot manipulators based on the coupling of the inverse dynamics method with the so-called second-order sliding mode control approach. Using this method, in principle, all the coupling non-linearities in the dynamical model of the manipulator are compensated, transforming the multi-input non-linear system into a linear and decoupled one. Actually, since the inverse dynamics relies on an identified model, some residual uncertain terms remain and perturb the linear and decoupled system. This motivates the use of a robust control design approach to complete the control scheme. In this article the sliding mode control methodology is adopted. Sliding mode control has many appreciable features, such as design simplicity and robustness versus a wide class of uncertainties and disturbances. Yet conventional sliding mode control seems inappropriate to be applied in robotics since it can generate the so-called chattering effect, which can be destructive for the controlled robot. In this article, this problem is suitably circumvented by designing a second-order sliding mode controller capable of generating a continuous control law making the proposed sliding mode controller actually applicable to industrial robots. To build the inverse dynamics part of the proposed controller, a suitable dynamical model of the system has been formulated, and its parameters have been accurately identified relying on a practical MIMO identification procedure recently devised. The proposed inverse dynamics-based second-order sliding mode controller has been experimentally tested on a COMAU SMART3-S2 industrial manipulator, demonstrating the tracking properties and the good performances of the controlled system.

Efficient Low-cost Controllers for Constrained Manipulators with Uncertainties and Disturbances

This work presents an efficient control strategy for robot manipulators with constraints and affected by uncertainties and disturbances. The controller uses a combination of model predictive control (MPC) with an adaptive robust feedforward term. The predictive controller is based on interpolations between different simple solutions to guarantee the feasibility of the final solution applied to the manipulator. The proposed method improves the existing techniques in terms of robust capabilities. Feasibility is preserved with the MPC and applicability is also guaranteed as the computational load of the interpolation algorithm is low. The benefits of the strategy, compared with other existing controllers, are shown with simulation results obtained with a PUMA-560 manipulator.

Fault-tolerant robot manipulators based on output-feedback [formula omitted] controllers

This paper develops two fault-tolerant control strategies for robot manipulators. The first is based on linear parameter-varying systems and the second on Markovian jump linear systems. Firstly, it is shown that with the LPV approach post-fault stability is guaranteed only if the robot stops completely after a fault detection. Then, with an underactuated configuration, the manipulator can be controlled appropriately. Secondly, it is shown that with the fault-tolerant system based on Markovian jump linear systems, stability is guaranteed after a fault is detected even with the robot still moving. This approach incorporates all manipulator configurations in a unified model. Both strategies have been implemented based on output-feedback H ∞ controllers, which are the main focus of this paper. Experimental results illustrate the performance of each controller.

Adaptive control of nonlinear visual servoing systems for 3D cartesian tracking

This paper presents a control strategy for robot manipulators to perform 3D cartesian tracking using visual servoing. Considering a fixed camera, the 3D cartesian motion is decomposed in a 2D motion on a plane orthogonal to the optical axis and a 1D motion parallel to this axis. An image-based visual servoing approach is used to deal with the nonlinear control problem generated by the depth variation without requiring direct depth estimation. Due to the lack of camera calibration, an adaptive control method is used to ensure both depth and planar tracking in the image frame. The depth feedback loop is closed by measuring the image area of a target object attached to the robot end-effector. Simulation and experimental results obtained with a real robot manipulator illustrate the viability of the proposed scheme.

Quantitative Safety Guarantees for Physical Human-Robot Interaction

If robots are to be introduced into the human world as assistants to aid a person in the completion of a manual task two key problems of today's robots must be solved. The human-robot interface must be intuitive to use and the safety of the user with respect to injuries inflicted by collisions with the robot must be guaranteed. In this paper we describe the formulation and implementation of a control strategy for robot manipulators which provides quantitative safety guarantees for the user of assistant-type robots. We propose a control scheme for robot manipulators that restricts the torque commands of a position control algorithm to values that comply to preset safety restrictions. These safety restrictions limit the potential impact force of the robot in the case of a collision with a person. Such accidental collisions may occur with any part of the robot and therefore the impact force not only of the robot's hand but of all surfaces is controlled by the scheme. The integration of a visual control interface and the safely con trolled robot allows the safe and intuitive interaction between a per son and the robot. As an example application, the system is pro grammed to retrieve eye-gaze-selected objects from a table and to hand them over to the user on demand.

Force Control of Robotic Manipulators Using a Fuzzy Predictive Approach

This paper proposes a force control strategy for robotic manipulators considering a non-rigid environment described by a nonlinear model. This approach uses a fuzzy predictive algorithm to generate, in an optimal way, the reference or virtual position to the classical impedance controller in order to apply a desired force profile on the environment. The main advantage of this control strategy is the possibility of including a nonlinear model of the environment in the controller design in a straightforward way, improving the global force control performance, especially in non-rigid environments. Moreover, in order to reduce the oscillations on the optimized reference position a fuzzy scaling machine is included on the force control strategy. The performance of the force control scheme is illustrated for a two degree-of-freedom PUMA 560 robot, which end-effector is forced to move along a flat surface located on the vertical plane. The simulation results obtained with the fuzzy control scheme reveal significant improvement in the force tracking performance, when compared to the impedance control with force tracking in non-rigid environments.

A compliance control strategy for robot manipulators under unknown environment

In this paper, a compliance control strategy for robot manipulators that employs a self-adjusting stiffness function is proposed. Based on the contact force, each entry of the diagonal stiffness matrix corresponding to a task coordinate in the operational space is adaptively adjusted during contact along the corresponding axis. The proposed method can be used for both the unconstrained and constrained motions without any switching mechanism which often causes undesirable instability and/or vibrational motion of the end-effector. The experimental results involving a two-link direct drive manipulator interacting with an unknown environment demonstrates the effectiveness of the proposed method.

Motion Control Strategy for Robot Manipulators in the Neighbourhood of Singularities

This work presents a control strategy applied to robot manipulators operating close to kinematic singularities. Considering the robot to be in contact with the environment, a PD controller is iInplelnented with inverse dynamics compensation to control the robot manipulator. In order to accomlTIodate the forces originated froln the interaction with the environment, the control systelTI includes an impedance loop that modifies the robot's references accordingly. In the vicinity of a singularity, the algorithm generates a fictitious force to which a second impedance function is applied. This causes a small deviation to the original robot trajectory to avoid the singularity region. The algoritlun is based on the minimwn singular value of the Jacobian matrix in order to generate the module and direction of the fictitious force. This strategy is applied to a teleoperation system without time delay