Volume 8, Number 2, 2011
Special Issue on Computational Methods and Intelligence in Life System Modeling and Simulation (pp.147-192)
Optimal control of greenhouse climate is one of the key techniques in digital agriculture. Greenhouse climate, a nonlinear and uncertain system, consists of several major environmental factors such as temperature, humidity, light intensity, and CO2 concentration. Due to the complex coupled correlations, it is a challenge to achieve coordination control of greenhouse environmental factors. This paper proposes a model-free coordination control approach for greenhouse environmental factors based on Q-learning. Coordination control policy is found through systematic interaction with the dynamic environment to achieve optimal control for greenhouse climate with the control cost constraints. In order to decrease systematic trial-and-error risk and reduce the computational complexity in Q-learning algorithm, case-based reasoning (CBR) is seamlessly incorporated into the Q-learning process. The experimental results demonstrate that this approach is practical, highly effective and efficient.
A nutrient-phytoplankton-zooplankton-detritus (NPZD) type of marine ecosystem model was developed in this study, and was further coupled to a three-dimensional primitive-equation ocean circulation model with a river discharge model and a solar radiation model to reproduce the dynamics of the low nutrition level in the Bohai Sea (BS). The simulation results were validated by observations and it was shown that the seasonal variation in the phytoplankton biomass could be characterized by the double-peak structure, corresponding to the spring and summer blooms, respectively. It was also found that both nitrogen and phosphate declined to the lowest level after the onset of the summer bloom, since the large amounts of nutrients were exhausted by phytoplankton for photosynthesis, and the concentrations of nutrients could resume in winter after a series of the biogeochemical-physical processes. By calculating the nitrogen/phosphorus (N/P) ratio, it is easy to see that the phytoplankton dynamics is nitrogen-limited as a whole in BS, though the phosphorus limitation may occur in the Yellow River (YR) Estuary where the input of riverine nitrogen is much more than that of phosphate.
A novel nonlinear gray transform method is proposed to enhance the contrast of a typhoon cloud image. Generally, the typhoon cloud image obtained by a satellite cannot be directly used to make an accurate prediction of the typhoon's center or intensity because the contrast of the received typhoon cloud image may be bad. Our aim is to extrude the typhoon's eye in the typhoon cloud image. A normalized arc-tangent transformation operation is designed to enhance global contrast of the typhoon cloud image. Differential evolution algorithm is used to choose the optimal nonlinear transform parameter. Finally, geodesic activity contour model is used to extract the typhoon's eye to verify the performance of the proposed method. Experimental results show that the proposed method can efficiently enhance the global contrast of the typhoon cloud image while greatly extruding the typhoon's eye.
The IEEE 802.15.4a standard provides a framework for low-data-rate communication systems, typically sensor networks. In this paper, we established a realistic environment for the time delay characteristic of industrial network based on IEEE 802.15.4a. Several sets of practical experiments are conducted to study its various features, including the effects of 1) numeral wireless nodes, 2) numeral data packets, 3) data transmissions with different upper-layer protocols, 4) physical distance between nodes, and 5) adding and reducing the number of the wireless nodes. The results show that IEEE 802.15.4a is suitable for some industrial applications that have more relaxed throughput requirements and time-delay. Some issues that could degrade the network performance are also discussed.
Contract is a common and effective mechanism for supply chain coordination, which has been studied extensively in recent years. For a supply chain network model, contracts can be used to coordinate it because it is too ideal to obtain the network equilibrium state in practical market competition. In order to achieve equilibrium, we introduce revenue sharing contract into a supply chain network equilibrium model with random demand in this paper. Then, we investigate the influence on this network equilibrium state from demand disruptions caused by unexpected emergencies. When demand disruptions happen, the supply chain network equilibrium state will be broken and change to a new one, so the decision makers need to adjust the contract parameters to achieve the new coordinated state through bargaining. Finally, a numerical example with a sudden demand increase as a result of emergent event is provided for illustrative purposes.
Coordinated controller tuning of the boiler turbine unit is a challenging task due to the nonlinear and coupling characteristics of the system. In this paper, a new variant of binary particle swarm optimization (PSO) algorithm, called probability based binary PSO (PBPSO), is presented to tune the parameters of a coordinated controller. The simulation results show that PBPSO can effectively optimize the control parameters and achieves better control performance than those based on standard discrete binary PSO, modified binary PSO, and standard continuous PSO.
Marginal Fisher analysis (MFA) not only aims to maintain the original relations of neighboring data points of the same class but also wants to keep away neighboring data points of the different classes. MFA can effectively overcome the limitation of linear discriminant analysis (LDA) due to data distribution assumption and available projection directions. However, MFA confronts the undersampled problems. Generalized marginal Fisher analysis (GMFA) based on a new optimization criterion is presented, which is applicable to the undersampled problems. The solutions to the proposed criterion for GMFA are derived, which can be characterized in a closed form. Among the solutions, two specific algorithms, namely, normal MFA (NMFA) and orthogonal MFA (OMFA), are studied, and the methods to implement NMFA and OMFA are proposed. A comparative study on the undersampled problem of face recognition is conducted to evaluate NMFA and OMFA in terms of classification accuracy, which demonstrates the effectiveness of the proposed algorithms.
This paper presents an up-to-date study on the observer-based control problem for nonlinear systems in the presence of unmodeled dynamics and actuator dead-zone. By introducing a dynamic signal to dominate the unmodeled dynamics and using an adaptive nonlinear damping to counter the effects of the nonlinearities and dead-zone input, the proposed observer and controller can ensure that the closed-loop system is asymptotically stable in the sense of uniform ultimate boundedness. Only one adaptive parameter is needed no matter how many unknown parameters there are. The system investigated is more general and there is no need to solve Linear matrix inequality (LMI). Moreover, with our method, some assumptions imposed on nonlinear terms and dead-zone input are relaxed. Finally, simulations illustrate the effectiveness of the proposed adaptive control scheme.
This paper investigates a parameterization method of adaptive H controllers for dissipative Hamiltonian systems with disturbances and unknown parameters. The family of adaptive H controllers with full information is obtained by interconnecting an adaptive H controller with a generalized zero-energy-gradient (ZEG) detectable, free generalized Hamiltonian system. The present parameterization method avoids solving Hamilton-Jacobi-Issacs equations and thus the controllers obtained are easier in operation as compared to some existing ones. Simulations show the effectiveness and feasibility of the adaptive control strategy proposed in this paper.
In order to apply the terminal sliding mode control to robot manipulators, prior knowledge of the exact upper bound of parameter uncertainties, and external disturbances is necessary. However, this bound will not be easily determined because of the complexity and unpredictability of the structure of uncertainties in the dynamics of the robot. To resolve this problem in robot control, we propose a new robust adaptive terminal sliding mode control for tracking problems in robotic manipulators. By applying this adaptive controller, prior knowledge is not required because the controller is able to estimate the upper bound of uncertainties and disturbances. Also, the proposed controller can eliminate the chattering effect without losing the robustness property. The stability of the control algorithm can be easily verified by using Lyapunov theory. The proposed controller is tested in simulation on a two-degree-of-freedom robot to prove its effectiveness.
In networked control systems (NCS), the control performance depends on not only the control algorithm but also the communication protocol stack. The performance degradation introduced by the heterogeneous and dynamic communication environment has intensified the need for the reconfigurable protocol stack. In this paper, a novel architecture for the reconfigurable protocol stack is proposed, which is a unified specification of the protocol components and service interfaces supporting both static and dynamic reconfiguration for existing industrial communication standards. Within the architecture, a triple-level self-organization structure is designed to manage the dynamic reconfiguration procedure based on information exchanges inside and outside the protocol stack. Especially, the protocol stack can be self-adaptive to various environment and system requirements through the reconfiguration of working mode, routing and scheduling table. Finally, the study on the protocol of dynamic address management is conducted for the system of controller area network (CAN). The results show the efficiency of our self-organizing architecture for the implementation of a reconfigurable protocol stack.
This paper presents a performance study of the distributed coordination function (DCF) of 802.11 networks considering erroneous channel and capture effects under non-saturated traffic conditions employing a basic access method. The aggregate throughput of a practical wireless local area network (WLAN) strongly depends on the channel conditions. In a real radio environment, the received signal power at the access point from a station is subjected to deterministic path loss, shadowing, and fast multipath fading. The binary exponential backoff (BEB) mechanism of IEEE 802.11 DCF severely suffers from more channel idle time under high bit error rate (BER). To alleviate the low performance of IEEE 802.11 DCF, a new mechanism is introduced, which greatly outperforms the existing methods under a high BER. A multidimensional Markov chain model is used to characterize the behavior of DCF in order to account both non-ideal channel conditions and capture effects.
In this paper, a fault tolerant control with the consideration of actuator fault for a networked control system (NCS) with packet loss is addressed. The NCS with data packet loss can be described as a switched system model. Packet loss dependent Lyapunov function is used and a fault tolerant controller is proposed respectively for arbitrary packet loss process and Markovian packet loss process. Considering a controlled plant with external energy-bounded disturbance, a robust H fault tolerant controller is designed for the NCS. These results are also expanded to the NCS with packet loss and networked-induced delay. Numerical examples are given to illustrate the effectiveness of the proposed design method.
The coefficient diagram method (CDM) is one of the most effective control design methods. It creates control systems that are very stable and robust with responses without the overshoot and small settling time. Furthermore, all control parameters of the control systems are changed by varying some adjustment parameters in CDM depending on the demands. The model reference adaptive systems (MRAS) are the systems that follow and change the control parameters according to a given model reference system. There are several methods to combine the CDM with MRAS. One of these is to use the MRAS parameters as a gain of the CDM parameters. Another is to directly use the CDM parameters as the MRAS parameters. In the industrial applications, the system parameters can be changed frequently, but if the controller, by self-tuning, recalculates and develops its own parameters continuously, the system becomes more robust. Also, if the poles of the controlled systems approach the jw axis, the response of the closed-loop MRAS becomes more and more insufficient. In order to obtain better results, CDM is combined with a self-tuning model reference adaptive system. Systems controlled by a model reference adaptive controller give responses with small or without overshoot, have small settling times, and are more robust. Thus, in this paper, a hybrid combination of MRAS and CDM is developed and two different control structures of the control signal are investigated. The two methods are compared with MRAS and applied to real-time process control systems.
This paper presents a new method to eliminate the chattering of state feedback sliding mode control (SMC) law for the mobile control of an autonomous underwater vehicle (AUV) which is nonlinear and suffers from unknown disturbances system. SMC is a well-known nonlinear system control algorithm for its anti-disturbances capability, while the chattering on switch surface is one stiff question. To dissipate the well-known chattering of SMC, the switching manifold is proposed by presetting a Hurwitz matrix which is deducted from the state feedback matrix. Meanwhile, the best switching surface is achieved by use of eigenvalues of the Hurwitz matrix. The state feedback control parameters are not only applied to control the states of AUV but also connected with coefficients of switching surface. The convergence of the proposed control law is verified by Lyapunov function and the robust character is validated by the Matlab platform of one AUV model.
A Survey on 3D Visual Tracking of Multicopters
Qiang Fu, Xiang-Yang Chen, Wei He
Toolpath Interpolation and Smoothing for Computer Numerical Control Machining of Freeform Surfaces: A Review
Deep Learning Based Hand Gesture Recognition and UAV Flight Controls
Bin Hu, Jiacun Wang