Recent Developments in Cooperative Control and Optimization

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One reason for all the excitement is that research has been so incredibly diverse -spanning many scientific and engineering disciplines. This latest volume in the Cooperative Systems book series clearly illustrates this trend towards diversity and creative thought. And no wonder, cooperative systems are among the hardest systems control science has endeavored to study, hence creative approaches to model- ing, analysis, and synthesis are a must!

The definition of cooperation itself is a slippery issue. As you will see in this and previous volumes, cooperation has been cast into many different roles and therefore has assumed many diverse meanings. Perhaps the most we can say which unites these disparate concepts is that cooperation 1 requires more than one entity, 2 the entities must have some dynamic behavior that influences the decision space, 3 the entities share at least one common objective, and 4 entities are able to share information about themselves and their environment.

Optimization and control have long been active fields of research in engi- neering. Product details Format Paperback pages Dimensions x x This part will investigate 1 The DLT for the repeatable operation systems. A particular interest will be focused on the data driven iterative learning control ILC system design; 2 data driven optimal iterative learning system design, including point-to-point ILC and terminal ILC; 3 stability analysis; 4 simulations and applications. Part 4. MFAC for complex connected systems, modulized designing with the model based control methods.

This part will present the MFAC methods for the complex connected systems, and the modulized designing with the model based control methods, and further research topics. Workshops and Tutorials. Workshop speakers: Brian D. Workshop speakers: Wei-Yu Chiu, Department of Electrical Engineering, National Tsing Hua University, Taiwan Workshop Abstract: There are several challenges for the current electricity grid: growing electricity demand, an aging grid infrastructure, ever-increasing penetration of renewables, and significant uptake of electric vehicles and energy storage with behind-the-meter applications for residential and commercial buildings.

The tutorial is structured in 3 presentations: 1 Stability and stabilizability concepts for linear infinite dimensional dynamical systems Marius Tucsnak.

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The second part of this presentation is devoted to some by now classical tools to establish stability properties, namely in the frequency domain. Workshop speakers: Ronghu Chi, Qingdao University of Science and Technology Yuanming Zhu, East China University of Science and Technology Xuhui Bu, Henan Polytechnic University Zhongsheng Hou, Beijing Jiaotong University Workshop Abstract: With the development of information sciences and technologies, practical processes, such as chemical industry, metallurgy, machinery, electronics, transportation, and logistics, pose enormous research and technical challenges for control engineering and management due to their size, distributed and multi-domain nature, safety and quality requirements, complex dynamics and performance evaluation, maintenance and diagnosis.

This Tutorial consists of four parts: Part 1. Part 2.

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Controller dynamic linearization based MFAC. Part 3.

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Local information from neighbors is required in this research. It is proven that this approach is capable of solving consensus problem under obstacle avoidance scenarios. Deliberative approach relied on the high-level communication, rich sensor and complete representation of the environment which allow the planning action. This approach is also known as centralized approach. The input data usually from the static environment that represents the global map can be planned to drive the agents efficiently to the target point [ 6 , 24 , 25 ].

The hybrid approach represents the integration control between reactive and deliberative control. Both controls complement each other to find the robust control system in controlling multi-agents robot. In deliberative control, all of the planning processes are involved with the calculation of a global target. As for reactive control, it is more towards a local plan for the robot to avoid the obstacles. There are examples of hybrid approaches related to multi-agents research studies as shown in Table 2 [ 5 , 6 , 24 , 25 ].

Every researcher has used different types of control architecture that are suitable for their system. They have come out with their own idea about the control architectures. Based on [ 26 ], hybrid architectures offer the most widespread solution in controlling intelligent mobile robots.

Besides that, in a real world, agents also require acting in a dynamic and uncertain environment [ 6 ]. Subsequently, the hybrid approach allows the robot to navigate the target as well as avoiding the obstacles successfully within that environment [ 24 ]. The researchers who have focused on reactive architectures or known as decentralized approach [ 27 ] have claimed that decentralization will provide flexibility and robustness.

Sometimes, centralized control design totally depends on the system structure and it cannot handle structural changes. Once removed, it needs to be designed all over again.

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It is also costly and complex in terms of online computation and its control design. Cooperation is usually based on some forms of communication. Communication is a mode of interactions between multi-agent robots. With an efficient communication system, the robot is capable of interacting, sharing and exchanging information. Communication also determines the success in mobile robots cooperation [ 28 , 29 ]. However, this section will only focus on the two main types of interaction ii and iii which are important in the communication of mobile robots.

The agents will sense other agents by embedding a different kind of sensors among them. They will react to avoid obstacles among themselves if they sense signals from other agents [ 4 , 10 , 30 , 31 , 32 ]. However, due to limitation of hardware parts, the interaction via sensing has been replaced by using a radio or infrared communication.

"Cooperative Motion Planning, Navigation, and Control of Multiple ..." Prof. António Pascoal

Explicit communication refers to the direct exchange of information between agents or via broadcast messages. This often requires onboard communication modules. Issues on designing the network topologies and communication protocol arise because these types of communication are similar to the communication network [ 3 , 5 , 6 , 33 , 34 , 35 , 36 , 37 , 38 , 39 ].

Table 4 shows an example of explicit communications being used in the robot systems. Although researchers in recent years have addressed the issues of multi-agent robot systems MARS , the current robot technology is still far from achieving many real world applications. Therefore, in this paper, problems and issues related to cooperative multi-agent systems are discussed to improve the current approaches and to further expand the applications of MARS.

Based on Section 2. However, some problems and issues of both control system in coordinating multi agents will be discussed. By having centralized control, the global information of the environment has been used to calculate the path, trajectory or position of the agents before all [ 5 , 6 , 24 , 25 , 26 , 40 ].

Recent Developments in Cooperative Control and Optimization -

The information then can be sent directly to the agents by using a suitable communication medium. This is one advantage of this control where the agents can obtained the information directly from its central. Research by Azuma [ 33 ] shows that the central will sent the updated location directly to the agents by using a WIFI continuously until the agents reach the target point. The quadratic equation is used to calculate agent performances while Simultaneous Perturbation Stochastic Approximation is the algorithm used for the control design [ 41 ].

The main issue in centralized control exists when the number of agents is expanding.

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The computation will become high since there is only one centralized processor that control over all of the system. Effect of this high computation, the time as well as the energy consumption will be effected at some point. Therefore, to solve this problem, hybrid control approach [ 5 , 6 , 24 , 25 ] has been proposed with objective to balance between centralized control and distributed control [ 23 , 26 , 48 , 49 , 50 , 51 , 52 ].

Besides that, alternative towards optimizing or minimizing the trajectory length, time and energy consumption [ 24 ] as well as adding the intelligences [ 11 , 20 , 22 , 27 , 31 , 32 , 53 ] has taken into consideration to reduce the computation time. In terms of scalability, adaptability and flexibility of the controller can be claimed lesser as compared to distributed control. Any changes especially dealing with dynamics will cause the repetition in the computing and sometimes will effect overall of the system with only a limited number of controllers.

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Thus, centralized control sometimes does not fit with the dynamic environment. Distributed control had proven scalable, adaptive, flexible and robust for multi agents system not only in static but also in a dynamic environment [ 54 ]. Many researchers had proven that their distributed controller can work efficiently for their multi agent robot systems [ 12 , 26 , 31 , 32 , 34 , 48 , 49 , 50 , 53 , 55 , 56 , 57 , 58 ]. In distributed, the main issue is the task has to be distributed in a robust an efficient manner to ensure that every agent is able to perform its individual task cooperatively with another agents to achieve certain target.

Distributing task among heterogeneous agents [ 11 , 15 ] is more crucial and complex comparing with homogeneous agents which are identical [ 20 , 21 , 22 , 59 ]. Limited sensing range and low bandwidth are also among physical constraints in distributed approach. With a limited local information, the agent cannot predict and cannot control the group behavior effectively in some sense. Another issues in distributed such as consensus, formation, containment, task allocation, optimization and intelligence will also discussed thoroughly in below section. Consensus refers to the degree of agreement among multi-agents to reach certain quantities of interest.

The main problem of consensus control in multi-agent robots is to design a distributed protocol by using local information which can guarantee the agreements between robots to reach certain tasks or certain states. Therefore, a large number of interest concerning on developing the consensus control distributed protocol for homogeneous and heterogeneous robots which can be classified into a leader following consensus [ 60 ] and leaderless consensus [ 61 , 62 , 63 , 64 , 65 , 66 ], to name a few , have been intensively studied by researchers recently [ 22 , 67 ].

Each of heterogeneity agents is not identical and the states between agents are different which will cause difficulties in finding consensus. This is known as cooperative output consensus problem. This is a challenging issue for heterogeneous robots and there are a number of researchers who focused on the leaderless output consensus problem [ 13 , 15 , 68 ] and leader-follower output consensus problem [ 13 , 15 , 69 , 70 , 71 ].

Research on finding consensus in the broadcasting area has also been carried out by few researchers. Li and Yan [ 72 ] solved the consensus in both fixing and switching type topology based on the spectrum radius of stochastic matrices.

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  • They introduced a concept of connected agent groups. They proved that their controller can work efficiently in a mixed environment with communication and broadcast. Besides that, research carried out by Das and Ghose [ 74 , 75 ] solved the positional consensus problem for multi-agents. Das and Ghose [ 74 ] proposed a novel linear programming formulation and random perturbation input in the control command to achieve consensus at the pre-specified location. The results showed that novel linear programming that is less intensive computation and perfect consensus can be obtained from random perturbation.

    Recent Developments in Cooperative Control and Optimization Recent Developments in Cooperative Control and Optimization
    Recent Developments in Cooperative Control and Optimization Recent Developments in Cooperative Control and Optimization
    Recent Developments in Cooperative Control and Optimization Recent Developments in Cooperative Control and Optimization
    Recent Developments in Cooperative Control and Optimization Recent Developments in Cooperative Control and Optimization
    Recent Developments in Cooperative Control and Optimization Recent Developments in Cooperative Control and Optimization
    Recent Developments in Cooperative Control and Optimization Recent Developments in Cooperative Control and Optimization
    Recent Developments in Cooperative Control and Optimization Recent Developments in Cooperative Control and Optimization
    Recent Developments in Cooperative Control and Optimization Recent Developments in Cooperative Control and Optimization
    Recent Developments in Cooperative Control and Optimization

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