A robust and adaptive force/position control for two cooperating robot arms under uncertainty

Robots are used to provide help in unsafe, repetitive and dreary situations. This paper concentrates on a robotic arm which can be utilized for crossing of the dead wire structure to the jumper cable of an electrical transmission line. In order to achieve a good positional accuracy of the end-effector, the robotic arm is modeled and analyzed. Denavit-Hartenberg (DH) representation is used for the kinematic modeling and Lagrangian-Euler (LE) method for the dynamic modeling. Since the kinematic and dynamic modeling of robotic arm is highly nonlinear in nature, the optimum trajectory planning is a challenging issue in almost all obstacle avoidance problems. In trajectory planning, the desired trajectory needs to be followed for end-effector positioning. This paper presents kinematic modeling, dynamic modeling, workspace analysis and trajectory planning of a 5 Degree of freedom (DOF) robotic arm. The robotic arm is designed using SolidWorks 2014 software. The complete analysis of the proposed manipulator is simulated using MATLAB Software.

Robotic systems have experienced exponential growth in their utilization for manufacturing applications over recent decades. Control systems responsible for executing desired robot motion planning face increasingly stringent performance requirements. These demands encompass high precision, efficiency, stability, robustness, ease of use, and simplicity of the user interface. Furthermore, diverse modern manufacturing applications primarily employ robotic systems within disturbed operating scenarios. This paper presents a novel neural motion-tracking control scheme for mobile manipulation robotic systems. Dynamic position output error feedback and B–Spline artificial neural networks are integrated in the design process of the introduced adaptive robust control strategy to perform efficient and robust tracking of motion-planning trajectories in robotic systems. Integration of artificial neural networks demonstrates performance improvements in the control scheme while effectively addressing common issues encountered in manufacturing environments. Parametric uncertainty, unmodeled dynamics, and unknown disturbance torque terms represent some adverse influences to be compensated for by the robust control scheme. Several case studies prove the robustness of the adaptive neural control scheme in highly coupled nonlinear six-degree-of-freedom mobile manipulation robotic systems. Case studies provide valuable insights and validate the efficacy of the proposed adaptive multivariable control scheme in manufacturing applications.

In this paper, we investigate the trajectory tracking control problem of surface ships subject to unknown parameters, unknown time-varying disturbances, and input saturation. The input saturation is approximated using a Gaussian error function. The compounded uncertain term caused by the unknown parameters and disturbances is transformed into a linear parametric form with a single parameter called virtual parameter. Then, a novel robust adaptive ship trajectory tracking control law is designed using the adaptive vector-backstepping design method, where the adaptive law online provides the estimation of the virtual parameter. Further, a finite-time control scheme is developed injecting a power function vector of the position and velocity errors into the proposed control scheme such that the control performance is improved. Both our control schemes are simple to compute and easy to implement in engineering applications. Theoretical analysis indicates that the designed control laws can force the surface ship to track the desired trajectory while guaranteeing the global uniform ultimate boundedness of all the signals in the closed-loop trajectory tracking control system of surface ships. Simulation results and comparison verify the effectiveness of our novel robust adaptive trajectory tracking control schemes.

This article investigates the problem of attitude tracking control for spacecraft with limited communication, unknown system parameters, and external disturbances. An adaptive control scheme with an event-triggered mechanism (ETM) is proposed to alleviate the communication burden. Radial Basis Function Neural Network (RBFNN) estimation model is developed to provide the input signals for the control module in this control scheme. Estimated attitude information of the spacecraft generated from the estimation model will only be transmitted to the control module at the instants when the ETM is violated. The neural network (NN) and the estimation model will be updated complying with an adaptive algorithm at the discrete triggering instants. It’s substantiated that all the errors of attitude tracking converge towards corresponding residuals and there are no accumulated triggering instants. Numerical simulation also demonstrates the effectiveness of the proposed control method.

This paper is concerned with the identification problem of two degree of freedom robot arm’s joints’ time-varying stiffness under time-varying torque loads. The industrial robot arms can be simplified into series hinged linkage mechanism. The unrestraint dynamic equation of two degrees of freedom robot arm can be obtained by using analytical mechanics method. The model which was established in Pro/E had been imported into ADAMS for simulation. And the simulation consisted of free vibration and forced vibration. Then by choosing limited memory least square method and using the post-processing measured variables, time-varying stiffness could have been identified. Finally, the calculative stiffness was compared to the “real” stiffness which was simulated in ADAMS. The whole process shows that the robot arm’s dynamic model and the method of identification are both effective. DOI: http://dx.doi.org/10.11591/telkomnika.v10i8.1644 Full Text: PDF

Data-driven model based adaptive feedback-feed forward control schemes for open cycle - OTEC process

The purpose of this chapter is to redesign the standard adaptive control schemes by using hybrid structure composed by Model Reference Adaptive Control (MRAC) or Adaptive Pole Placement Control (APPC) strategies, associated to Variable Structure (VS) schemes for achieving non-standard robust adaptive control strategies. The both control strategies is now on named VS-MRAC and VS-APPC. We start with the theoretical base of standard control strategies APPC and MRAC, discussing their structures, as how their parameters are identified by adaptive observers and their robustness properties for guaranteeing their stability. After that, we introduce the sliding mode control (variable structure) in each control scheme for simplifying their design procedure. These design procedure are based on stability analysis of each hybrid robust control scheme. With the definition of both hybrid control strategies, it is analyzed their behavior when controlling system plants with unmodeled disturbances and parameter variation. It is established how the adaptive laws compensates these unmodeled dynamics. Furthermore, by using simple systems examples it is realized a comparison study between the hybrid structures VS-APPC and VSMRAC and the standard schemes APPC and MRAC. As the hybrid structures use switching laws due to the sliding mode scheme, the effect of chattering is analyzed on the implementation and consequently effects on the digital control hardware where sampling times are limiting factor. For reducing these drawbacks it is also discussed possibilities which kind of modifications can employ. Finally, some practical considerations are discussed on an implementation on motor drive systems.

In this study, a switching adaptive controller is introduced to stabilise fractional‐order dynamical systems with completely unknown dynamics and structure. The boundedness property of the chaotic systems is used to design a switching adaptive control scheme which suppresses the chaotic behaviour of the fractional‐order systems. The analytical terms of this approach are proved based on the stability analysis theorem of the fractional‐order systems and the adaptive control method. Robust stability and simplicity in design are some interesting capabilities of the designed scheme. The proposed method is a general one in which it can overcome the system's unknown parameters and uncertainties. In addition, the proposed adaptive approach is applied for control of a class of three‐dimensional fractional‐order systems via a single control input. Some illustrative examples, including an application to stabilisation of a chaotic model of a power system, are given to show the efficacy of the proposed adaptive approaches.

In this paper, considering the simultaneous influence of multi-source disturbances, system modeling uncertainties and input–output constraints, an adaptive robust attitude tracking control scheme is proposed for near space vehicles (NSVs) which is expressed as a stochastic nonlinear system. A multi-dimensional Taylor polynomial network (MTPN) is utilized to handle the system uncertainties, and the nonlinear disturbance observer (NDO) based on MTPN is designed to estimate the external disturbances. Furthermore, by constructing the auxiliary system to tackle the input saturation and introducing the Tan-type barrier Lyapunov function (TBLF) to solve the output constraint, the constrained control strategy can be obtained. Combining with backstepping control (BC) technique and stochastic control method, an adaptive robust stochastic control scheme is developed based on NDO, MTPN, and auxiliary system, and the closed-loop system stability in the sense of probability is analyzed based on stochastic Lyapunov stability theory. Finally, numerical simulations further demonstrate the feasibility of the proposed tracking control scheme.

This paper investigates the feasibility of adaptive control scheme for vibration suppression of flexible beam. The dynamic model of the flexible cantilever beam using finite element modeling is derived. Adaptive vibration controllers are good candidates for problems where parameters of a plant are unknown or there are uncertainties in a system. Parameters of adaptive controllers are adjusted on-line to achieve the best performance. Two adaptive vibration control schemes: adaptive feedforward control scheme using least mean square algorithm and model reference adaptive control algorithm are developed to suppress the vibration. Computer simulations are performed to verify the effectiveness of the proposed adaptive vibration control schemes and demonstrate their applications in vibration control.

In this work, we address the adaptive control and synchronization problems of Chua's circuits with unknown system parameters. Adaptive controllers with single state feedback are derived such that the trajectory of the Chua's circuit is globally stabilized to an equilibrium point of the uncontrolled system. In addition, the proposed adaptive control schemes are then applied to achieve state synchronization of two identical Chua's circuits. Furthermore, based on the Lyapunov approach, the closed‐loop systems are proved to be globally exponentially or asymptotically stable. Numerical results and Pspice simulations demonstrate the proposed control scheme's effectiveness.

The present study proposes a robot arm that can support precise positioning in operation by a human. The robot arm has passive and actuated joints. The passive joint is not mechanically constrained and can move passively with the motion of the output point of the robot arm. Therefore, a human can move the output point of the robot arm by his will. When a human tries to move the output point along an object path, the displacement of the passive joint is determined passively. Then, if the actuated joint is controlled so that the output point may be on the object path, the human can achieve precise positioning. Namely, using the proposed robot arm, the human can adjust the velocity, acceleration, applied force and so on by his will, while the position is preciously compensated by the actuated joint. This paper describes a mechanical form of such robot arm for drawing of planar figures and shows that the robot arm has specific singularity condition. In order to avoid the singularity condition, the proposed robot arm interchanges the actuated joint and the passive one situationally. The study fabricated the two degrees of freedom robot arm for positioning assistance and described its control method. Furthermore, its availability is confirmed by drawing some figures.

This article describes an adaptive robust control algorithm of the mobile manipulator based on robust neural network to solve disturbances, nonlinearity, nonholonomic constraints between the mobile platform and the mounted manipulator. According to the nonlinear mapping ability of neural network and fast learning ability, neural network and robust control strategies are integrated into the adaptive control algorithm for the mobile manipulator with nonlinear and nonholonomic constraints. Basis on the mobile manipulator dynamics model and its modeling analysis, the controller can drive the trajectory tracking error of the mobile manipulator to converge to zero. The simulation results show that the algorithm has higher precision of trajectory tracking control and can achieve good control effect.

In this paper we focus on the adaptive control of structural acoustics using intelligent structures with embedded piezoelectric (PZT) patches and low cost digital signal processor systems. After a discussion on the adaptive feedforward control scheme, a hybrid adaptive control scheme is proposed, which takes advantage of both feedback control and adaptive feedforward control. The two schemes are realized on a low-cost, small volume, convenient and universal digital signal processing (DSP) board. A carbon fiber reinforced polymer plate with two embedded PZT patches is developed and used in two experiments. The first experiment is adaptive interior noise control using the intelligent plate, in which the adaptive feedforward control scheme is employed. Obvious noise reduction is obtained for constant frequency, swept frequency and varying amplitude harmonic disturbances. The second experiment is adaptive control of sound-induced vibration of the plate, where two embedded PZT patches are used as an actuator and a sensor, respectively, and the hybrid adaptive controller is applied. The full vibration reduction for various harmonic excitations is obtained, verifying the advantage of the hybrid adaptive control. It is demonstrated that active control of structural acoustics can be efficiently achieved by employing intelligent structures, advanced adaptive control schemes and the low-cost DSP board.

A new fractional-order hyperchaotic memristor oscillator: Dynamic analysis, robust adaptive synchronization, and its application to voice encryption