A Robust Fuzzy Tracking Control Scheme for Robotic Manipulators with Experimental Verification

Fault Tolerant Control Scheme for Robotic Manipulators Affected by Torque Faults

The robot manipulators are highly nonlinear, time varying and one of the important challenges in the field of robotics is the effective control of manipulators. This paper presents a technique to extract the rules of Fuzzy Logic Computed Torque Controller for PUMA-560 robot arm with uncertainties. Fuzzy Logic Controllers are placed at the input of the PD Controller to make the gains adaptive. Prisoner's dilemma is employed to systematically tune the gains of the controller. The interrelations between inputs and outputs of a Fuzzy Linguistic Model are assigned using payoff matrix through Prisoner's Dilemma. The difficulty in designing of fuzzy controllers is the extraction of the rule base. The extraction of fuzzy control rules requires good understanding of the plant and control theory. The present paper utilizes Fuzzy Lyapunov Synthesis to constitute the rule base assuming that minimal knowledge about the plant to be controlled. Simulation results prove the effective performance of the proposed controller in minimizing the error in joint angles when compared to Proportional Derivative Computed Torque Controller (PD-CTC), normal Fuzzy Logic Controller (FLC) and that of the reference signal.

Various advanced control strategies are applied to a direct-drive SCARA robot and studied in computer simulations. Besides computed torque control and direct adaptive control, heuristic optimal control, a new path control scheme for robotic manipulators, is included in the comparison study. PD control, the traditional robot control method, is used for generating a comparing baseline. While all schemes are applied for the same tracking task, the effect of modelling errors and measurement noise is considered in robot performance evaluation. Simulation results show that (1) without model errors, all advanced control schemes can achieve higher tracking accuracy than PD control; (2) with a random measurement error of 1%, computed torque and direct adaptive control methods are inferior to PD control; (3) heuristic control proves to be the most robust control scheme in case of mixed model and measurement errors.

A neural network adaptive control scheme for robot manipulators is proposed which consists of one Adaline network to identify structured system dynamics and another one to compensate for both structured and unstructured dynamic uncertainties. The former is trained offline using an LMS (least mean square) type algorithm, while the latter uses an online stable weight updating mechanism determined using Lyapunov theory. Since Adaline nets match robot regressor dynamics perfectly, the training processes of the resulting simple neural networks are computationally efficient and the proposed adaptive control scheme has very high potential in real-time applications. The proposed control scheme is illustrated by simulation and comparison studies. >

This brief presents a contouring control scheme for robotic manipulators. The geometric properties of the desired contour are incorporated in the controller design phase, and the resulting controller has been structured as a two-layered hierarchical control scheme that consists of an outer loop and an inner loop. The outer loop is formed by kinematic control system in operational space, which can be designed to assign different dynamics to the tangential, normal, and binormal direction of the desired contour. It is shown that the outer loop can provide a joint velocity reference signal to the inner one. The inner loop is used to implement a velocity servo control system at the robot joint level. Meanwhile, a radial basis function network is adopted to compensate for the nonlinear dynamics of the robotic manipulator, where a robust control strategy is used to suppress the modeling error of neural networks. Experimental results on the Zebra-Zero robotic manipulator have demonstrated the effectiveness of the proposed control scheme in comparison with other control strategies.

A finite time synergetic control scheme for robot manipulators

A great deal of research has been done in fuzzy logic control (FLC) and its applications since Mamdani's pioneering papers in 1974 and 1977. FLC has also been applied to manipulator control which is a very challenging nonlinear control problem. Both classical and advanced robot controllers have problems because of high nonlinearity or uncertainties in robot dynamics. FLC, as an alternate, suffer from lack of analytical methods for design, tuning and stability analysis. A nonlinear controller which is robust in the presence of modeling errors and disturbances is presented in this paper. A computed torque controller can be designed based on an approximate model and FLC can be used to minimize the tracking error due to modeling errors and disturbance. Since the approximate model of the system reduces the overall nonlinearity, FLC works with very simple rules and it is easy to tune.

High efficiency due to reduced losses, low maintenance and low rotor inertia of the brushless dc (BLDC) motor have increased the demand of BLDC motors in high power servo and robotic applications. Since fuzzy logic with human like but systematic property can convert the linguistic control rules based on expert knowledge into automatic control strategy, it can be well applied to control the systems with uncertain or unmodelled dynamics. In view of fuzzy logic merits, this paper proposes three types of fuzzy logic controllers for BLDC motor drive using advanced simulation model and presents a comparative study of performance specifications of classical PI and PID controllers and three fuzzy logic controllers. The three fuzzy logic controllers considered are PI-like fuzzy logic controller (PI-Like FLC), Hybrid fuzzy logic controller (HFLC) and integrated fuzzy logic controller (IFLC). The steady state and dynamic characteristics of speed and torque are effectively monitored and analyzed using the proposed model. The aim of fuzzy logic controllers is to obtain improved performance in terms of disturbance rejection or parameter variation than obtained using classical controllers. In the HFLC, the proportional term of the traditional PID controller is replaced with an incremental fuzzy logic controller. For the PI-Like FLC, the output of the controller is modified by a rule base with the error and change of error of the controlled variable as the inputs. The IFLC is constructed by using Fuzzy logic controller and PID controller. A performance comparison of classical and fuzzy logic controllers has been carried out by several simulations at different speeds and different load conditions.

This research paper focuses on the design and analysis of a high performance adaptive PD like fuzzy computed Torque controller for second order nonlinear uncertain (medical robot manipulator) system, in presence of uncertainties. The proposed approach effectively combines of design methods from Computed Torque controller (CTC), adaptive controller and fuzzy logic theory to improve the performance, stability and robustness of the CTC controller. Computed Torque Controller has two important challenges, system’s dynamic dependency and stability challenge. The nonlinearity term of the CTC is used to eliminate the decoupling and nonlinear term of link’s dynamic parameters. However, nonlinearity term of CTC is very essential to reliability but in uncertain condition or highly nonlinear dynamic systems, it can cause some problems. To solve this challenge the PD fuzzy logic controller is used as a model-based PD like fuzzy computed torque controller. The PD like fuzzy computed torque controller is updated based on online tuning coefficients. In order to reduce the online computation burden, the PD like fuzzy logic controller is also used to coefficients online tuning. As a result, in proposed method fuzzy logic controller is used to dynamic estimation and online tuning and as a result, improves the stability. This controller improves the stability and robustness, reduces the error as well and reduces the level of energy due to the torque performance as well.

This paper presents an adaptive control scheme for robot manipulators with flexible joints. Our control scheme theoretically guarantees that the tracking error converges to zero and that all the signals in the system remain bounded. We also analyze the robustness of the adaptive controller. To show the capability of the proposed controller, an example of a one-link robot manipulator with one revolute flexible joint is considered. The experimental results are given to demonstrate the effectiveness of this method.

SummaryThis study proposes a lifelong deep learning control scheme for robotic manipulators with bounded disturbances. This scheme involves the use of an online tunable deep neural network (DNN) to approximate the unknown nonlinear dynamics of the robot. The control scheme is developed by using a singular value decomposition‐based direct tracking error‐driven approach, which is utilized to derive the weight update laws for the DNN. To avoid catastrophic forgetting in multi‐task scenarios and to ensure lifelong learning (LL), a novel online LL scheme based on elastic weight consolidation is included in the DNN weight‐tuning laws. Our results demonstrate that the resulting closed‐loop system is uniformly ultimately bounded while the forgetting is reduced. To demonstrate the effectiveness of our approach, we provide simulation results comparing it with the conventional single‐layer NN approach and confirm its theoretical claims. The cumulative effect of the error and control input in the multitasking system shows a 43% improvement in performance by using the proposed LL‐based DNN control over recent literature.

In the study, we propose a novel adaptive visual servoing control scheme for robotic manipulators with kinematic and dynamic uncertainties, where the camera used is uncalibrated, which implies that its intrinsic and extrinsic parameters are unavailable for measurement. For our scheme, a depth-independent composite Jacobian matrix is constructed to make visual parameters and robotic physical parameters appear linearly in a parametrized uniform form so that an adaptive algorithm can be developed to estimate their values. With the raised adaptive algorithm, the potential singularity of the Jacobian matrix can be well circumvented by updating estimated parameters in an appropriate tiny range of actual values. With our scheme, the asymptotic convergence of the image tracking error to zero is established successfully, in addition to the signal boundedness of the closed-loop system. The effectiveness of the proposed scheme is confirmed by simulation results based on a 6-DOF PUMA manipulator.

A simplified approach to design fuzzy logic controller for an underwater vehicle

This paper presents the design and implementation of fuzzy based controller for the general form of two-link planar robotic manipulator. Also for comparative analysis, the conventional controllers, proportional-derivative (PD), proportional-integral-derivative (PID) and computed torque control are implemented on the same system under same conditions. The comparative analysis of the results demonstrates that the fuzzy logic controller is best among all the controllers and the simulation results confirm that the system can track the desired trajectory. Computer simulation results on a two-link planar robotic manipulator are presented to show tracking capability and effectiveness of the proposed control schemes. The simulations have been carried out using Matlab.

In this paper, an adaptive depth and heading control of an autonomous underwater vehicle using the concept of an adaptive neuro-fuzzy inference system (ANFIS) is designed. The autonomous underwater vehicle dynamics have six degrees of freedom, which are highly nonlinear and time-varying. It is affected by environmental effects such as ocean currents and tidal waves. Due to nonlinear dynamics designing, a stable controller in an autonomous underwater vehicle is a difficult end to achieve. Fuzzy logic and neural network control blocks make up the proposed control design to control the depth and heading angle of autonomous underwater vehicle. The neural network is trained using the back-propagation algorithm. In the presence of noise and parameter variation, the proposed adaptive controller’s performance is compared with that of the self-tuning fuzzy-PID and fuzzy logic controller. Simulations are conducted to obtain the performance of both controller models in terms of overshoot, and the rise time and the result of the proposed adaptive controller exhibit superior control performance and can eliminate the effect of uncertainty.