<Emphasis Type="Italic">HumanPT</Emphasis>: An Open-source,HumanPT Architecture-based,Robotic Application for Low Cost Robotic Tasks

In this paper, first HumanPT architecture for low cost robotic applications is presented. HumanPT architecture differs than other architectures because it is implemented on existing robotic systems (robot  robotic controller) and exploits the minimum communication facilities for real-time control that these systems provide. It is based on well-known communication methods like serial communication (USB, RS232, IEEE-1394) and windows sockets (server–client model) and permits an important number of different type of components like actuators, sensors and particularly vision systems to be connected in a robotic system. The operating system (OS) used is Microsoft Windows, the most widely spread OS. The proposed architecture exploits features of this OS that is not a real-time one, to ensure – in case that the robotic system provide such a facility – control and real time communication with the robotic system controller and to integrate by means of sensors and actuators an important number of robotic tasks and procedures. As implementation of this architecture, HumanPT robotic application and experimental results concerning its performance and its implementation in real tasks are provided. HumanPT robotic application, developed in Visual C++, is an integrated, but simultaneously an open-source software that can be adapted in different types of robotic systems. An important number of robotic tasks or procedures including sensors and particularly vision systems can be generated and executed. Small enterprises by means of the proposed architecture and the open source software can be automated at low cost enhancing in this way their production.     

Neural-Network-Based Nonlinear Model Predictive Tracking Control of a Pneumatic Muscle Actuator-Driven Exoskeleton

Pneumatic muscle actuators (PMAs) are compliant and suitable for robotic devices that have been shown to be effective in assisting patients with neurologic injuries, such as strokes, spinal cord injuries, etc., to accomplish rehabilitation tasks. However, because PMAs have nonlinearities, hysteresis, and uncertainties, etc., complex mechanisms are rarely involved in the study of PMA-driven robotic systems. In this paper, we use nonlinear model predictive control (NMPC) and an extension of the echo state network called an echo state Gaussian process (ESGP) to design a tracking controller for a PMA-driven lower limb exoskeleton. The dynamics of the system include the PMA actuation and mechanism of the leg orthoses; thus, the system is represented by two nonlinear uncertain subsystems. To facilitate the design of the controller, joint angles of leg orthoses are forecasted based on the universal approximation ability of the ESGP. A gradient descent algorithm is employed to solve the optimization problem and generate the control signal. The stability of the closed-loop system is guaranteed when the ESGP is capable of approximating system dynamics. Simulations and experiments are conducted to verify the approximation ability of the ESGP and achieve gait pattern training with four healthy subjects.      

Experimental evaluation of two complementary decentralized event-based control methods

Event-based control aims at reducing the feedback communication effort among the sensors, controllers and actuators in control loops to time instants at which the feedback of information is necessary to meet a desired control performance. This paper presents a new method for the decentralized event-based control of physically interconnected systems and shows its experimental evaluation. The novel method is based on two complementary approaches, called the global and the local approach, which jointly ensure the ultimate boundedness of the closed-loop system. The global approach steers the state of each subsystem into a target region, whereas the local approach keeps the state in this set in spite of exogenous disturbances and the effect of the interconnections to other subsystems. This event-based control method is applied to a continuous flow process to show its practical implementation and to evaluate the analytical results on the basis of experiments.     

Vision-Based Robotic Motion Control for Non-autonomous Environment

A visual servo control system with SOPC structure is implemented on a retrofitted Mitsubishi Movemaster RV-M2 robotic system. The hardware circuit has the functions of quadrature encoder decoding, limit switch detecting, pulse width modulation (PWM) generating and CMOS image signal capturing. The software embedded in Nios II micro processor has the functions of using UART to communicate with PC, robotic inverse kinematics calculation, robotic motion control schemes, digital image processing and gobang game AI algorithms. The digital hardware circuits are designed by using Verilog language, and programs in Nios II micro processor are coded with C language. An Altera Statrix II EP2S60F672C5Es FPGA chip is adopted as the main CPU of the development board. A CMOS color image sensor with 356 ×292 pixels resolution is selected to catch the environment time-varying change for robotic vision-based servo control. The system performance is evaluated by experimental tests. A gobang game is planned to reveal the visual servo robotic motion control objective in non-autonomous environment. Here, a model-free intelligent self-organizing fuzzy control strategy is employed to design the robotic joint controller. A vision based trajectory planning algorithm is designed to calculate the desired angular positions or trajectory on-line of each robotic joint. The experimental results show that this visual servo control robot has reliable control actions.     

Non-approximation-based outputs stabilization for switched large-scale nonlinear systems with quantized inputs and sensor uncertainties

A non-approximation-based output feedback control strategy for a class of switched large-scale nonlinear systems with quantized inputs and sensor uncertainties is proposed. A dynamic gain, which is shared by the state observers and controllers of all the subsystems, is designed so that the effects of sensor uncertainties, quantized inputs, unknown parameters, and external disturbances can be compensated. By constructing some common Lyapunov functions (CLFs) shared by the switched systems, it is proved that with the proposed scheme, the closed-loop system stability can be guaranteed under arbitrary switching, and the outputs of all the subsystems can be steered to within arbitrarily small neighborhoods of the origin.     

基于演算子理论的IPMC人工肌肉精确位置控制

IPMC(Ionic Polymer Metal Composite)人工肌肉是一种离子交换聚合金属材料,由于具有在低电压作用下可以产生较大弯曲的特性,已经被作为分布式传感器和执行器广泛应用于各种仿生机器人构建中。为了在各种仿生机器人中应用IPMC人工肌肉,期望的位置或偏移量必须能够精确地控制。针对这个问题,通过应用鲁棒右互质分解方法,本文设计一种基于演算子理论的IPMC人工肌肉精确位置控制系统,该系统不仅保证了鲁棒稳定性,而且能够实现精确的位置跟踪。最后,通过仿真和实验结果,系统的有效性进一步得到验证。     

Type-2 fuzzy control for a flexible-joint robot using voltage control strategy

Type-1 fuzzy sets cannot fully handle the uncertainties. To overcome the problem, type-2 fuzzy sets have been proposed. The novelty of this paper is using interval type-2 fuzzy logic controller (IT2FLC) to control a flexible-joint robot with voltage control strategy. In order to take into account the whole robotic system including the dynamics of actuators and the robot manipulator, the voltages of motors are used as inputs of the system. To highlight the capabilities of the control system, a flexible joint robot which is highly nonlinear, heavily coupled and uncertain is used. In addition, to improve the control performance, the parameters of the primary membership functions of IT2FLC are optimized using particle swarm optimization (PSO). A comparative study between the proposed IT2FLC and type-1 fuzzy logic controller (T1FLC) is presented to better assess their respective performance in presence of external disturbance and unmodelled dynamics. Stability analysis is presented and the effectiveness of the proposed control approach is demonstrated by simulations using a two-link flexible-joint robot driven by permanent magnet direct current motors. Simulation results show the superiority of the IT2FLC over the T1FLC in terms of accuracy, robustness and interpretability.     

基于状态观测器的非理想输入不确定组合大系统的输出反馈分散镇定

基于状态观测器,讨论了不确定相似组合系统的鲁棒分散输出反馈镇定问题,系统的输入是非理想的,不确定项存在于系统内部和各子系统的关联项中,它们可能是非线性或时变的,且满足通常的匹配条件,使用变结构原理设计控制器,所设计的控制器保证系统渐近稳定,研究结果表明,系统的相似性有助于简化对系统的分析和设计;仿真结果表明,本文的方法是有效的。     

Sensor Fusion System Using Recurrent Fuzzy Inference

In robotic and manufacturing systems, it is difficult to measure the state of systems accurately because of many uncertain factors and noise, and it is very important to estimate the state of systems. We must measure the phenomena of systems by multiple sensors and estimate the state of systems by acquiring information of sensors. However, we can not acquire all of sensor information synchronically, because each sensor has particular sensor information and measuring time. For estimating the state of systems by multiple sensors, a multi-sensor fusion system fusing various sensory information is needed. In this paper, we propose a Recurrent Fuzzy Inference (RFI) with recurrent inputs and apply it to a multi-sensor fusion system for estimating the state of systems. The membership functions of RFI are expressed by Radial Basis Function (RBF) with insensitive ranges. The shape of the membership functions can be adjusted by a learning algorithm. The learning algorithm is based on the steepest descent method and incremental learning which can add new fuzzy rules. The effectiveness of the multi-sensor fusion system using RFI will be shown through a numerical experiment of moving robot and estimation of surface roughness in grinding process.     

Robust Control for a Class of Uncertain Switched Fuzzy Systems with Saturating Actuators

This paper investigates the robust control problem for a class of uncertain switched fuzzy systems with saturating actuators. The asymptotical stability for fuzzy subsystems subject to actuator saturation is not assumed. Based on the multiple Lyapunov functions method, we design a switching law and a state feedback control law such that the closed‐loop system is asymptotically stable. Additionally, the estimation of the domain of attraction is presented by solving an optimization problem. Finally, simulation results verify the feasibility and effectiveness of the proposed method.