Adaptive force–environment estimator for manipulators based on adaptive wavelet neural network

This study focuses on the accurate tracking control and sensorless estimation of external force disturbances on robot manipulators. The proposed approach is based on an adaptive Wavelet Neural Network (WNN), named Adaptive Force-Environment Estimator (WNN-AFEE). Unlike disturbance observers, WNN_AFEE does not require the inverse of the Jacobian transpose for computing the force, thus, it has no computational problem near singular points. In this scheme, WNN estimates the external force disturbance to attenuate its effects on the control system performance by estimating the environment model. A Lyapunov based design is presented to determine adaptive laws for tuning WNN parameters. Another advantage of the proposed approach is that it can estimate the force even when there are some parametric uncertainties in the robot model, because an additional adaptive law is designed to estimate the robot parameters. In a theorem, the stability of the closed loop system is proved and a general condition is presented for identifying the force and robot parameters. Some suggestions are provided for improving the estimation and control performance. Then, a WNN-AFEE is designed for a planar manipulator as an example, and some simulations are performed for different conditions. WNN_AFEE results are compared attentively with the results of an adaptive force estimator and a disturbance estimator. These comparisons show the efficiency of the proposed controller in dealing with different conditions.     

Adaptive output feedback stabilisation for planar nonlinear systems with unknown control coefficients

The paper is concerned with the global adaptive stabilisation via output feedback for a class of uncertain planar nonlinear systems. Remarkably, the unknowns in the systems are rather serious: the control coefficients are unknown constants which do not belong to any known interval, and the growth of the systems heavily depends on the unmeasured states and has the rate of unknown polynomial of output. First, a delicate state transformation is introduced to collect the unknown control coefficients, and subsequently, a suitable state observer is successfully designed with two different dynamic gains. Then, an adaptive output feedback controller is proposed by flexibly combining the universal control idea and the backstepping technique. Meanwhile, an appropriate estimation law is constructed to overcome the negative effect caused by the unknown control coefficients. It is shown that, with the appropriate choice of the design parameters, all the states of the resulting closed-loop system are globally bounded, and furthermore, the states of the original system converge to zero.     

Adaptive tracking control of leader–follower systems with unknown dynamics and partial measurements

In this paper, a decentralized adaptive tracking control is developed for a second-order leader–follower system with unknown dynamics and relative position measurements. Linearly parameterized models are used to describe the unknown dynamics of a self-active leader and all followers. A new distributed system is obtained by using the relative position and velocity measurements as the state variables. By only using the relative position measurements, a dynamic output–feedback tracking control together with decentralized adaptive laws is designed for each follower. At the same time, the stability of the tracking error system and the parameter convergence are analyzed with the help of a common Lyapunov function method. Some simulation results are presented to validate the proposed adaptive tracking control.     

Microinstrument contact force sensing based on cable tension using BLSTM–MLP network

Minimally invasive surgical robotic systems established the foundation for precise and refined surgery, and the intelligentization of robotic systems is an important direction for future development. Among the methods of intelligentization, microinstrument external force sensing is an open and challenging research area. Force sensing information is used not only to ensure that surgeons apply the appropriate amount of force but also to prevent unintentional tissue damage. Because a microinstrument is a compact and small-sized construction, indirect force sensing method instead of the integration of sensors into the microinstrument is used, yielding better biocompatibility, sterilizability and monetary cost savings. This paper focuses on microinstrument-tissue contact force sensing, and the microinstrument used is a three degrees of freedom cable-driven manipulator. A contact force estimation strategy based on the differences in cable tension is established with consideration of the kinematics, dynamics and friction of the manipulator. A principle prototype of a surgical microinstrument force measurement system is developed, and then zero-drift, hysteresis and force loading experiments are studied. Based on the experimental data of the force loading experiments, the relationship between cable tension and contact forces is established by using a bidirectional long short-term memory plus multilayer perceptron network. The results show that the L2 cost of the network in the training set converges to 0.006 and that the RMSE of the network in the testing set converges to 0.053, and the network can meet the measurement requirements without overfitting. Therefore, the indirect force estimation method is a viable method of measuring forces of cable-driven microinstrument and can be used to integrate force sensing information into surgical robotic systems to improve the operability of surgical robots.

     

H∞ Adaptive tracking control for switched systems based on an average dwell-time method

This paper investigates the H_∞ state tracking model reference adaptive control (MRAC) problem for a class of switched systems using an average dwell-time method. First, a stability criterion is established for a switched reference model. Then, an adaptive controller is designed and the state tracking control problem is converted into the stability analysis. The global practical stability of the error switched system can be guaranteed under a class of switching signals characterised by an average dwell time. Consequently, sufficient conditions for the solvability of the H_∞ state tracking MRAC problem are derived. An example of highly manoeuvrable aircraft technology vehicle is given to demonstrate the feasibility and effectiveness of the proposed design method.     

Robust variable admittance control for human–robot co-manipulation of objects with unknown load

Over the last years, physical Human–Robot Interaction (pHRI) has become a particularly interesting topic for industrial tasks. An important issue is allowing people and robots to collaborate in an useful, simple and safe manner. In this work, we propose a new framework that allows the person to collaborate with a robot manipulator, while the robot has its own predefined task. To allow the robot to smoothly switch from its own task to be a compliant collaborator for the person, a variable admittance control is developed. Furthermore, in general the task to accomplish requires the robot to carry variable, unknown, loads at the end-effector. To include this feature in our framework, a robust control is also included to preserve the performance of the robot despite uncertainties coming from the unknown load. To validate our approach, experiments were carried out with a Kuka LBR iiwa 14 R820, first to validate both parts of the controller, and finally, to study a use-case scenario similar to an industrial production line. Results show the efficiency of this approach to allow the person to collaborate at any moment while the robot is capable of performing another task. This flexible framework for object co-manipulation also allows unknown loads up to 2 kg to be handled without making the task more difficult for the person.     

Neural model-based adaptive control for systems with unknown Preisach-type hysteresis

An adaptive control scheme is presented for systems with unknown hysteresis. In order to handle the case where the hysteresis output is unmeasurale, a novel model is firstly developed to describe the characteristic of hysteresis. This model is motivated by Preisach model but implemented by using neural networks (NN). The main advantage is that it is easily used for controller design. Then, the adaptive controller based on the proposed model is presented for a class of SISO nonlinear systems preceded by unknown hysteresis, which is estimated by the proposed model. The hws for model updating and the control hws for the neural adaptive controller are derived from Lyaptmov stability theorem, therefore the semi - global stability of the closed-loop system is guaranteed. At last, the simulation results are illuswated.     

Sheet metal forming global control system based on artificial vision system and force–acoustic sensors

Forming processes are manufacturing processes based on deformation of raw material applying pressure in one or several stages until getting the final product. This process depends on many factors, e.g. process parameters, material properties or lubrication, leading to possible defective parts. Correct forming of parts is very important as any defective part may result in big economical losses, e.g. the return of a complete set of parts or the loss of some clients. Thus, in our European Craft Pro2Control project, leading German, French, Italian and Spanish companies, universities and forming industries are defining and implementing a zero-defect forming control system, minimizing costs and maximizing the throughput of parts.     

Research on vacuum gripper based on fuzzy control for micromanipulators

This paper presents a vacuum gripper （as an actuator of an intelligent micromanipulator） for micro objects （with a diameter of 100 - 300μm） assembly tasks. The gripper is composed of a vacuum unit and a control unit. The vacuum unit with a proportional valve and a pressure sensor, and the control unit with a PC ＋ MCU two-layered control architecture are designed. The mechanical structure, workflow and major programs of the micro-gripper are presented. This paper discusses the major components of the adhesion force acting on micro objects. Some equations of the operation conditions m three phases of pick, hold and place are derived by mechanics analysis. The pneumatic system＇s pressure loss is inevitable. There are some formulas for calculating the amount of the pressure loss, but parameters in formulas are diffficult to be quantified and evaluated. To control the working pressure accurately, a pressure controller based on fuzzy logic is designed. With MATLAB＇s fuzzy logic toolbox, simulation experiments are performed to validate the performance of the fuzzy PD controller. The gripper is characterized by a steady and reliable performance and a simple structure, and it is suitable for handling micro objects with a sub-millimeter size.     

Effective position–posture control strategy based on switching control for planar three-link underactuated mechanical system

A planar three-link passive–active–active (PAA) underactuated mechanical system is a kind of nonlinear system with a passive first joint. The position–posture control objective for the planar PAA system is to move the end effector from an initial position to a target position with a specified posture. This paper presents a switch control strategy to solve the position–posture control problem. First, a Lyapunov function is constructed based on the system control objective. Then, a set of main controllers based on this Lyapunov function are designed. However, the main controllers may make the system stabilise at one of equilibrium points, which is not the system target position. To avoid the above phenomenon, when the system is about to stabilise at one non-target position, the main controllers are switched to a set of sub-controllers, which are designed according to another Lyapunov function constructed based on the control objective of the active links. When the sub-controllers are running, their design parameters are adjusted to try to keep the derivative of the first Lyapunov function being a non-positive function. Therefore, the switch control between the main controllers and the sub-controllers realises the position–posture control objective of the system. Finally, the simulation results demonstrate the effectiveness of the switch control strategy.     

Adaptive sliding-mode controller based on the “Super-Twist” state observer for control of the Stewart platform

To provide a highly efficient control of nonlinear systems in the presence of nonmodeled dynamics and external perturbations, a new control law with feedback based on the sliding modes with an observer of the “Super-Twist” kind was proposed. For acceptable use of the continuous observer signal in the controller, presented were adaptive laws for adjustment of the control system parameters. Using the methods of Lyapunov function, system stability (convergence to a zone) was proved. This technique was proposed as an example of control and stabilization of the position of a parallel manipulator (Gough–Stewart platform). The presented mechanism with six degrees of freedom is used to control the secondary mirror of the “Large Millimeter Telescope Alfonso Serrano” situated in the state of Puebla, Mexico.     

Adaptive control with discontinuous σ-factor and saturation for improved robustness

An adaptive control law is proposed to improve the robustness of the adaptive system with respect to fast unmodelled dynamics represented by a singular perturbation. The new law is based on saturation of the regressor and control parameter vector with inclusion of a discontinuous σ-factor. The analysis of the resulting system is presented and the conclusions are illustrated by simulations. The introduction of a discontinuous σ-factor represents an extension of an earlier work. The advantages of this extension include global stability, with less restrictive assumptions, and better transient behaviour.     

Novel Adaptive Balloon Active Contour Method based on internal force for image segmentation – A systematic evaluation on synthetic and real images

Many studies have been conducted on digital image segmentation, seeking to overcome the limitations of different methods for specific applications. Thus, existing techniques are improved and new methods created. This paper proposes a new Active Contour Method (ACM) applied to the segmentation of objects in digital images. The proposed method is called Adaptive Balloon ACM and its main contribution is the new internal Adaptive Balloon energy that minimizes the energy of each point on the curve using the topology of its neighboring points, and thus moves the curve toward the object of interest. The method can be initialized inside or outside the object of interest, and can even segment objects that have complex shapes. There are no limitations as to its startup location. This work evaluates the proposed method in several applications and compares it with other ACMs in the literature. This new method obtained superior results, especially when the objects to be segmented were tubular and had bifurcations. Thus the proposed method can be considered effective in segmenting complex shapes in digital images and gave promising results in various applications.     

A novel control design for delayed teleoperation based on delay-scheduled Lyapunov–Krasovskii functionals

This paper addresses the controller design problem for bilateral teleoperation over unreliable networks. The stability and tracking performance analyses are presented for a novel force-reflecting emulator control scheme. The performance (stability, synchronisation, transparency) is guaranteed by H_∞ control theory and delay-scheduled Lyapunov–Krasovskii functionals (LKF), which could improve the existing stability criterion. The design is achieved by using linear matrix inequality optimisation. For the simulation, first, numerical examples are given to demonstrate the effectiveness and benefits of the delay-scheduled LKF-based stability results; second, the proposed controller design solution is illustrated by various simulations and compared with other recent approaches under different working conditions, e.g. abrupt tracking motion and wall contact.     

Iterative learning control for hyperbolic distributed parameter systems based on sensor–actuator networks

This paper proposes two kinds of iterative learning control (ILC) schemes for a class of the distributed parameter systems based on sensor–actuator networks which can be described by hyperbolic partial differential equations. A D-type ILC algorithm is first considered and the convergent condition of the output error is obtained via the contraction mapping methodology. Then, the PD-type ILC algorithm is considered in this hyperbolic distributed parameter systems based on sensor–actuator networks. Finally, a cable equation with air and structural damping is given to illustrate the effectiveness of the proposed methods.     

Human–machine shared control for lane departure assistance based on hybrid system theory

To improve the human–machine cooperation of lane departure assistance system (LDAS), a human–machine shared control strategy based on hybrid system theory was proposed. By considering vehicle’s discrete and continuous states and time-varying longitudinal speed, the hybrid system was formalized as hybrid automaton, and the shared control strategy was built to govern the human–machine interaction. Robust gain-scheduling energy-to-peak method was adopted to design the assistance system controller. The D-stability of the system was also studied and guaranteed by solving the linear matrix inequality (LMI). The proposed human–machine shared control method was evaluated via the co-simulation of CarSim/Simulink and the hardware-in-loop (HIL) experiment. The results showed that the proposed approach can effectively keep the vehicle in lane and a good human–machine coordination was demonstrated.     

Adaptive efficient video transmission over the Internet based on congestion control and RS coding

An approach based on adaptive congestion control and adaptive error recovery with RS (Reed-Solomon) coding method is presented for efficient video transmission over the Internet. Featured by weighted moving average rate control and TCP-friendliness, AVSP, a novel adaptive video streaming protocol, is designed with adjustable rate control parameters so as to respond quickly to the QoS status fluctuation during video transmission over the Internet. Combined with congestion control policy, an adaptive RS coding error recovery scheme with variable parameters is presented to enhance the robustness of MPEG video transmission over the Internet with restriction to the total system bandwidth .     

Multi-model predictive control with local constraints based on model switching

Because model switching system is a typical form of Takagi-Sugeno（T-S） model which is an universal approximator of continuous nonlinear systems, we describe the model switching system as mixed logical dynamical （MLD） system and use it in model predictive control （MPC） in this paper. Considering that each local model is only valid in each local region,we add local constraints to local models. The stability of proposed multi-model predictive control （MMPC） algorithm is analyzed, and the performance of MMPC is also demonstrated on an inulti-multi-output（MIMO） simulated pH neutralization process.     

Adaptive control of stochastic Hammerstein–Wiener nonlinear systems with measurement noise

This paper deals with the adaptive control of a class of stochastic Hammerstein–Wiener nonlinear systems with measurement noise. Despite the fundamental progress achieved so far, a general theory framework about adaptive control of Hammerstein–Wiener models is still absent. Such situation is mainly due to the lack of an appropriate parameterisation model. To this end, this paper presents a novel parameterisation model that is to replace unmeasurable internal variables with their estimations. Then, the adaptive control algorithm to be applied is derived on the basis of self-tuning control. In addition, due to the use of the internal variable estimations, the stability and convergence properties are different from the self-tuning control. Our aim, in theoretical analysis, is to discover what limitations are in using the estimations instead of the true values in a control algorithm. Representative numerical examples are given and the simulation results verify the theoretical analysis.