Modeling of pneumatic muscle with shape memory alloy and braided sleeve

Pneumatic muscle (PM) of flexible actuators used in bionic robot is an active area of recent research. A novel PM with shape memory alloy (SMA) braided sleeve is proposed in this paper, and SMA is used to improve PM working characteristics. Based on the principle of virtual work, output force model of PM and relationship with braided wire inner-stress are established, and analysis of PM deformation has shown that braided wire length is the key factor of output force characteristic. Based on the crystal structure transitions, the relationship of temperature with wire shrinkage is derived. Then, the synthetic dynamics of novel PM is established. A physical prototype of PM with SMA braided sleeve is developed, and test platform that is built for the experiment. Experiment and simulation test of static isometric-length, static isobaric-pressure, and dynamic characteristics are done. The experimental results are compared with the simulation of theoretical model. Moreover, based on experiment, model of output force was improved by adding a correction factor to deal with the elastic force of rubber tube. The results analysis demonstrates that the established models are correct, and SMA wires can reinforce PM and make PM working characteristics adjustable. PM proposed in this paper has greater output force and is beneficial to achieve more accurate control that is useful for manipulating fragile things.     

Hybrid control of a pneumatic artificial muscle (PAM) robot arm using an inverse NARX fuzzy model

We investigated the possibility of applying a hybrid feed-forward inverse nonlinear autoregressive with exogenous input (NARX) fuzzy model-PID controller to a nonlinear pneumatic artificial muscle (PAM) robot arm to improve its joint angle position output performance. The proposed hybrid inverse NARX fuzzy-PID controller is implemented to control a PAM robot arm that is subjected to nonlinear systematic features and load variations in real time. First the inverse NARX fuzzy model is modeled and identified by a modified genetic algorithm (MGA) based on input/output training data gathered experimentally from the PAM system. Second the performance of the optimized inverse NARX fuzzy model is experimentally demonstrated in a novel hybrid inverse NARX fuzzy-PID position controller of the PAM robot arm. The results of these experiments demonstrate the feasibility and benefits of the proposed control approach compared to traditional PID control strategies. Consequently, the good performance of the MGA-based inverse NARX fuzzy model in the proposed hybrid inverse NARX fuzzy-PID position control of the PAM robot arm is demonstrated. These results are also applied to model and to control other highly nonlinear systems.     

ESN网络在机器人轨迹跟踪控制中的应用

为了研究具有模型不确定性的机器人操作手的轨迹跟踪控制,采用一种新的递归神经网络——回声状态网络(ESN)设计了动态控制器.采用PID控制器补偿ESN网络的逆建模误差,并在网络训练过程中加入白噪声项,以保证动态系统的稳定性.最后针对两关节机械手的轨迹跟踪控制问题进行了数值仿真,仿真结果表明了该方法的有效性.     

状态和输入受限的切换奇异布尔控制网络的最优控制

本文研究了状态和输入均受限的切换奇异布尔控制网络的最优控制问题.利用矩阵半张量积方法获得受限切换奇异布尔控制网络的等价代数形式.然后通过类似针变化得到了存在最优控制的必要条件,并且提出了一个算法设计切换序列和控制策略使收益函数最大化.最后给出例子验证所得结果的有效性.     

改进粒子群算法优化回声状态网络的电力需求预测研究

首先引入自适应算子对标准粒子群优化算法PSO的惯性权重和学习因子进行改进,以提高其探索当前空间和开发未知空间之间的平衡性。同时,采用非线性函数来构建回声状态网络ESN储备池内部状态之间的非线性关系。接着利用改进的粒子群优化算法APSO对非线性回声状态网络NESN的关键参数进行优化,以构建APSO-NESN组合预测模型。最后运用该模型进行电力需求预测。实验结果表明,相比自回归移动平均模型、多元线性回归、标准ESN及其他预测模型,APSO-NESN模型具有更高的预测精度。     

Robust adaptive critic control of nonlinear systems using fuzzy basis function networks: An LMI approach

This paper proposes an adaptive critic tracking control design for a class of nonlinear systems using fuzzy basis function networks (FBFNs). The key component of the adaptive critic controller is the FBFN, which implements an associative learning network (ALN) to approximate unknown nonlinear system functions, and an adaptive critic network (ACN) to generate the internal reinforcement learning signal to tune the ALN. Another important component, the reinforcement learning signal generator, requires the solution of a linear matrix inequality (LMI), which should also be satisfied to ensure stability. Furthermore, the robust control technique can easily reject the effects of the approximation errors of the FBFN and external disturbances. Unlike traditional adaptive critic controllers that learn from trial-and-error interactions, the proposed on-line tuning algorithm for ALN and ACN is derived from Lyapunov theory, thereby significantly shortening the learning time. Simulation results of a cart-pole system demonstrate the effectiveness of the proposed FBFN-based adaptive critic controller.     

Design and experimental evaluation of a dynamical adaptive backstepping-sliding mode control scheme for positioning of an antagonistically paired pneumatic artificial muscles driven actuating system

Pneumatic artificial muscles (PAMs) are a class of pneumatic drives that have received considerable attention for applications related to bio-inspired robotics. Nevertheless, servo control of PAMs is challenging due to the compressibility and nonlinear flow characteristics of air, hysteresis behaviour as well as uncertainties present. In this paper, positioning of an antagonistically paired PAM with mass flow rate of compressed air regulated by a 5/3-way proportional directional valve and driven by a dynamical adaptive backstepping-sliding mode control (DAB-SMC) scheme is investigated. Implemented for the first time on a PAM-driven actuating system, derivation of this model-based nonlinear control scheme is presented first followed by experimental evaluation. Positioning performance is studied using a sinusoidal trajectory with tracking frequencies of 0.05, 0.1, 0.2 and 0.5 Hz, and a multiple-step polynomial input having step sizes of 0.7°, 1.4°, 2.9° and 5.7°. Over various operating conditions, average root mean square error value of 0.16° and steady-state error value of 0.04° are achieved for position tracking and regulating, respectively. The adaptive LuGre friction observer embedded in the control scheme effectively compensates hysteresis behaviour of the PAMs and helps to improve the performance. The proposed DAB-SMC scheme outperforms the classical SMC scheme by 33% in accuracy. The control scheme has also demonstrated a robust performance towards the uncertainties including loading. In addition, a slight performance compromise in both tracking and regulating tasks was observed, when the 5/3-way proportional valve is replaced by cost-effective 2/2-way pulse width modulation controlled on–off valves.     

Modeling and simulation of an artificial muscle and its application to biomimetic robot posture control

The shape memory effect exhibited by Nitinol wire can be utilized to construct an artificial muscle. The muscle is activated by an electric current, which produces heat and initiates a phase transformation. The Nitinol artificial muscle stress–strain–power relationship was determined by experiments, and a mathematical model was developed. The artificial muscle model was utilized for the posture control of a biomimetic underwater robot. The optimal activation patterns for height, pitch, and roll postures were determined. Simulation results for the height postures are in agreement with the experiments. The separation between the center of gravity and the centroid of the robot has a stabilizing effect on pitch and roll postures.     

受扰布尔控制网络全局可重构分析与状态估计

受扰布尔控制网络的状态转移,因受未知干扰影响而具有不确定性,这对状态观测器设计带来了困难.本文主要研究了受扰布尔控制网络全局可重构性问题,并在此基础上设计状态观测器.首先,将受扰布尔控制模型转化为多个子系统的切换未知布尔控制网络模型,在此基础上,提出了受扰布尔控制网络的4种不同状态集.其次,基于状态集估计方法,对受扰布尔控制网络状态估计问题进行分析.再次,提出有限时间可重构与全局可重构性概念;同时,根据状态集估计与状态转移分析,分别给出有限时间可重构判定算法与全局可重构性证明的充要条件.最后,给出观测器设计方法,并通过例子证明了本文提出方法的可行性.     

Robust face tracking control of a mobile robot using self‐tuning Kalman filter and echo state network

This paper presents a novel design of face tracking algorithm and visual state estimation for a mobile robot face tracking interaction control system. The advantage of this design is that it can track a user's face under several external uncertainties and estimate the system state without the knowledge about target's 3D motion‐model information. This feature is helpful for the development of a real‐time visual tracking control system. In order to overcome the change in skin color due to light variation, a real‐time face tracking algorithm is proposed based on an adaptive skin color search method. Moreover, in order to increase the robustness against colored observation noise, a new visual state estimator is designed by combining a Kalman filter with an echo state network‐based self‐tuning algorithm. The performance of this estimator design has been evaluated using computer simulation. Several experiments on a mobile robot validate the proposed control system. Copyright © 2010 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society     

基于最小二乘算法及神经网络的非线性离散系统的自适应控制

针对有关文献所设计的控制律，在一较弱的条件下，去掉了符号运算部分，证明该控制律可避免“零除”问题，提高了运算速度。对于这种基于神经网络和LS算法的自适应控制问题，证明了系统状态落入一紧集中，闭环系统的所有信号都是有界的，且系统输出和参考输出之间的跟踪误差收敛于以零为原点的某一有界球中。     

Finite time adaptive filter control of nonlinear systems with actuator faults and state constraints

The finite time adaptive filter control problem is studied for strict-feedback nonlinear systems with actuator faults and state constraints in this paper. First, with the aid of the backstepping technique, the controller and the adaptive laws are designed to compensate the actuator faults and parameter uncertainties. Then, the dynamic surface control is used to establish a first-order filter to alleviate computational burden for the reason of the derivative of virtual control laws. In addition, none of the system states violate predefined constraint boundaries by utilizing a log-type barrier Lyapunov function. Furthermore, the boundedness can be ensured for all the closed-loop signals, and better tracking performance of the system is obtained within a finite-time. Finally, the simulation examples are shown to prove the feasibility and effectiveness of the proposed strategy.     

Modeling and analyzing dynamic social networks for behavioral pattern discovery in collaborative design

As a newly-developed information exchange and management platform, Building Information Modeling (BIM) is altering the way of collaboration among multi-engineers for civil engineering projects. During the BIM implementation, a large number of event logs are automatically generated and accumulated to record details of the model evolution. For knowledge discovery from huge logs, a novel BIM event log mining approach based on the dynamic social network analysis is presented to examine designers’ performance objectively, which has been verified in BIM event logs about an ongoing year-long design project. Relying on meaningful information extracted from time-stamped logs, networks on the monthly interval are built to graphically represent information and knowledge sharing among designers. Special emphasis is put on measuring designers’ influence by a defined new metric called “impact score”, which combines the k-shell method and 1-step neighbors to achieve comparatively low computational cost and high accurate ranking. Besides, an emerging machine learning algorithm named CatBoost is utilized to predict designers’ influence intelligently by learning features from both network structure and human behavior. It has been found that twelve networks can be easily distinguished into two collaborative patterns, whose characteristics in both network structures and designers’ behaviors are significantly different. The most influential designers are similar within the same group but varied from different groups. Extensive analytical results confirm that the method can potentially serve as month-by-month feedback to monitor the complex modeling process, which further supports managers to realize data-driven decision making for better leadership and work plan towards an optimized collaborative design.     

A comparison between functional networks and artificial neural networks for the prediction of fishing catches

In recent years, functional networks have emerged as an extension of artificial neural networks (ANNs). In this article, we apply both network techniques to predict the catches of the Prionace Glauca (a class of shark) and the Katsowonus Pelamis (a variety of tuna, more commonly known as the Skipjack). We have developed an application that will help reduce the search time for good fishing zones and thereby increase the fleets competitivity. Our results show that, thanks to their superior learning and generalisation capacities, functional networks are more efficient than ANNs. Our data proceeds from remote sensors. Their spectral signatures allow us to calculate products that are useful for ecological modelling. After an initial phase of digital image processing, we created a database that provides all the necessary patterns to train both network types.     

Motion synchronization of dual-cylinder pneumatic servo systems with integration of adaptive robust control and cross-coupling approach

We investigate motion synchronization of dual-cylinder pneumatic servo systems and develop an adaptive robust synchronization controller. The proposed controller incorporates the cross-coupling technology into the integrated direct/indirect adaptive robust control （DIARC） architecture by feeding back the coupled position errors, which are formed by the trajectory tracking errors of two cylinders and the synchronization error between them. The controller employs an online recursive least squares estimation algorithm to obtain accurate estimates of model parameters for reducing the extent of parametric uncertainties, and uses a robust control law to attenuate the effects of parameter estimation errors, unmodeled dynamics, and disturbances. Therefore, asymptotic convergence to zero of both trajectory tracking and synchronization errors can be guaranteed. Experimental results verify the effectiveness of the proposed controller.     

Dynamics and control of singular boolean networks

Consider a class of singular Boolean networks, which consist of two parts: difference part and algebraic part. Using the truth matrix of the algebraic part, the trajectories of the networks are obtained and certain properties are investigated. Then the results are extended to Boolean control networks and the controllability of singular Boolean control systems is investigated. Necessary and sufficient conditions are obtained.     

Adaptive consensus control for stochastic nonlinear multiagent systems with full state constraints

In this paper, the consensus tracking problem is investigated for stochastic nonlinear multiagent systems with full state constraints and time delays. The barrier Lyapunov functions proposed for single‐agent constrained systems are constructively extended to solve the consensus problem for multiagent systems with the full state constraints. Some Lyapunov‐Krasovskii functionals are introduced to compensate for state time delays, which are inherent in the complicated nonlinear systems. Based on the variable separation technique, the difficulty arising from the nonstrict‐feedback structure is overcome. Under a directed communication topology, the distributed neuroadaptive control protocols are proposed to guarantee that all the follower agents follow the trajectory of the leader agent and the full state constraints are not violated. The effectiveness of the proposed distributed adaptive control approach is verified via simulation examples.     

Nonlinear model identification and adaptive control of CO2 sequestration process in saline aquifers using artificial neural networks

In recent years, storage of carbon dioxide (CO₂) in saline aquifers has gained intensive research interest. The implementation, however, requires further research studies to ensure it is safe and secure operation. The primary objective is to secure the CO₂ which relies on a leak-proof formation. Reservoir pressure is a key aspect for assessment of the cap rock integrity. This work presents a new pressure control methodology based on a nonlinear model predictive control (NMPC) scheme to diminishing risk of carbon dioxide (CO₂) back leakage to the atmosphere due to a fail in the integrity of the formation cap rock. The CO₂ sequestration process in saline aquifers is simulated using ECLIPSE-100 as black oil reservoir simulator while the proposed control scheme is realized in MATLAB software package to prevent over-pressurization. A modified form of growing and pruning radial basis function (MGAP-RBF) neural network model is identified online for prediction of reservoir pressure behaviors. MGAP-RBF is recursively trained via extended Kalman filter (EKF) and unscented Kalman filter (UKF) algorithms. A set of miscellaneous test scenarios has been conducted using an interface program to exchange ECLIPSE and MATLAB in order to demonstrate the capabilities of the proposed methodology in guiding saline aquifer to follow some desired time-dependent pressure profiles during the CO₂ injection process.     

Controllability of time-variant Boolean control networks and its application to Boolean control networks with finite memories

This paper investigates the controllability of time-variant Boolean control networks(BCNs).For the time-variant BCNs,a necessary and sufcient condition for the controllability is given,and a control design algorithm is presented.For a BCN with fnite memories,an equivalent transformation to a time-variant BCN is constructed.Then a necessary and sufcient condition for the controllability and a control design algorithm are obtained.     

Maximum lifetime rate control and random access in multi-hop wireless networks

Network lifetime and transmission quality are of paramount importance for rate control in an energy constrained multi-hop wireless network. It is known that they depend on mechanisms that span several protocol layers due to the existing interference across collision links and the energy constrained nature of wireless nodes. Although separate consideration of these issues simplifies the system design, it is often insufficient for wireless networks when the overall system performance is required. In this paper, we present a framework for cross-layer rate control towards maximum network lifetime and collision avoidance. The main contributions of this paper are twofold. First, although the link attainable rate is typically a non-convex and non-separable function of persistent probabilities, we prove the convergence of this cross-layer algorithm to the global optimum of joint congestion control and random access algorithm under the framework of nonlinear programming. Second, by adjusting a parameter in the objective function, we achieve the tradeoff between transmission quality and network lifetime. Simulations illustrate the desirable properties of the proposed algorithm, including convergence to the global optimum, better performance than the layered scheme, desirable tradeoff between transmission quality and network lifetime.