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Abstrak :
Sistem multi-agent adalah suatu sistem yang terdiri atas beberapa agen yang mampu melakukan interaksi satu sama lain untuk mencapai tujuan bersama. Salah satu permasalahan dalam sistem multi-agent adalah permasalahan konsensus untuk menyamakan keadaan seluruh agen dengan mengurangi perbedaan keadaan suatu agen dengan agen lainnya. Berbagai jenis protokol pengendali telah dikembangkan untuk membuat seluruh agen mencapai konsensus, tetapi sebagian besar pengendali masih menggunakan mekanisme time-triggered yang mengharuskan pengendali pada setiap agen untuk melakukan komputasi di setiap waktu cuplikan sehingga beban komputasi cukup tinggi. Penelitian ini membahas mengenai pengendali prediktif (MPC) terdistribusi untuk menyelesaikan permasalahan konsensus pada sistem multi-agent dengan mekanisme event-triggered. Permasalahan konsensus dapat diselesaikan dengan menggunakan pengendali prediktif terdistribusi yang bekerja dengan melakukan optimalisasi fungsi objektif. Untuk mengurangi beban komputasi, mekanisme event-triggered digunakan sehingga pengendali melakukan optimalisasi fungsi objektif hanya ketika kondisi trigger terpenuhi. Pada penelitian ini, pengendali prediktif dengan mekanisme event-triggered diujikan terhadap sistem dengan model agen linear vehicle dan nonholonomic mobile robot melalui simulasi. Hasil simulasi yang diperoleh menunjukkan performa yang baik dalam mencapai konsensus dengan total waktu komputasi yang lebih sedikit dibanding menggunakan mekanisme time-triggered.

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Multi-agent system is a system consisting of several agents who are able to interact with each other to achieve a common goal. One of the problems in a multi-agent system is the problem of consensus to equalize the state of all agents by reducing the differences of an agent with other agents. Various types of controlling protocols have been developed to make all agents reach consensus, but most controllers still use a time-triggered mechanism that requires controllers in each agent to do computation at each sampling time so that the computing load is high enough. This study examines the distributed predictive controller (MPC) to solve consensus problems on multi-agent systems with event-triggered mechanisms. Consensus problem can be solved using a distributed predictive controller that works by optimizing objective functions. To reduce computational load, an event-triggered mechanism is used so that the controller performs objective function optimization only when the trigger conditions are met. In this study, predictive controllers with event-triggered mechanisms were tested on systems with linear vehicle and nonholonomic mobile robot agents through simulation. The simulation results show good performance in reaching consensus with less computational time than using time-triggered mechanisms.

Abstrak :
Penerapan dan penelitian mengenai multi agent system (MAS) saat ini sedang cepat berkembang. Penggunaan pengendali Model Predictive Control (MPC) cukup populer digunakan untuk menyelesaikan permasalahan konsensus pada korvengensi dan stabilitas MAS. Namun pada MPC konvensional masih terdapat masalah, masalah optimasi yang harus diselesaikan secara periodik menyebabkan pemborosan sumber daya komputasi dan komunikasi antar agen. Oleh karena itu, mekanisme tambahan diperlukan agar masalah optimasi dapat diselesaikan secara aperiodik. Pada peneltian ini, mekanisme self triggered ditambahkan pada pengendali MPC, dan dirancang untuk menyelesaikan permasalahan formation pada MAS dengan model nonholonomic mobile robot. Penggunaan mekanisme ini memungkinkan komputasi MPC tidak perlu dilakukan secara terus menerus, tapi digunakan bila dibutuhkan. Pengendali yang dirancang dibentuk dengan mengkombinasikan antara teori graph sebagai topologi komunikasi antar agent, dengan algoritma MPC, dan ditambah dengan mekanisme self triggered untuk menentukan kondisi kapan MPC melakukan optimasi. Pengendali Prediktif atau MPC akan memprediksi sinyal kendali agent sepanjang horizon prediction. Beberapa simulasi dilakukan untuk menguji rancangan desain pengendali self-triggered MPC, kemudian diuji untuk menyelesaikan permsalahan formation, lama waktu komputasi dihitung dan dibandingkan dengan MPC konvensional. Hasil uji simulasi menunjukan bahwa mekanisme self triggered mampu mengurangi beban komunikasi dan beban komputasi pada pengendali MPC.

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Application and study of multi-agent systems are growing fast. Model Predictive Control (MPC) has been popularly used to solve consensus problem in corvengence and stability of multi agent system (MAS). But conventional MPC is still having probrems, optimization problems is solved periodically, which may lead to a waste of computation and communication resources between agents. Therefore, additional mechanism is needed so optimization problems can be solved aperiodically. In this study, a self triggered mechanism was added to the MPC controller, which was designed to solve the formation problem for MAS on nonholonomic mobile robot. A self triggered condition allows MPC computing not to be done periodically, but is used if needed. The designed controller is build by combining graph theory as a communication topology beetwen agents, with the MPC algorithm, and added with a self triggered mechanism to determine the conditions when does the MPC solved optimization. Predictive controllers or MPC is used to predict control signals along the horizon prediction. Several simulation are conducted to test the self triggered MPC controller design, were then tested to solve the formation problem. The computation time is calculated and compared with conventional MPC. The simulation test results show that the self triggered mechanism is able to reduce the communication load and computational load on MPC controllers.

Abstrak :
Penelitian ini membahas permasalahan formasi sistem multi-agent menggunakan pengendali prediktif terdistribusi. Model yang digunakan adalah nonholonomic mobile robot. Setiap agen dapat memecahkan sendiri permasalahan optimasi dan mengimplementasikannya setiap cuplikan waktu. Ada dua permasalahan formasi yang dibahas, yaitu formation tracking control dan formation stabilization. Pada permasalahan formation tracking control, setiap agen harus mengikuti referensi trajektori yang telah dibuat dan mempertahankan jarak dengan agen tetangganya. Sedangkan pada permasalahn formation stabilization, setiap agen, dari posisi yang acak, membentuk suatu formasi yang telah ditentukan di posisi yang telah ditentukan juga. Hasil simulasi ditampilkan untuk menggambarkan efektivitas dari pengendali prediktif terdistribusi yang telah didesain. Diperoleh hasil dengan waktu konvergensi yang sangat cepat dibanding jika dengan pengendali lain.

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This study is concerned with the problem of formation using distributed model predictive control. The model that is used is nonholonomic mobile robot. All the agents are permitted to solve optimization problem by itself and implement them at each time step. There are two formation problem that will be discussed, i.e. formation tracking control problem and formation stabilization problem. On the formation tracking control problem, each agent is required to follow a reference trajectory that has been generated and maintain distance between agents. On the formation stabilization problem, each agent, with random initial condition, is required to form a formation at a specific position that has been determined. A numerical simulation is given to illustrate the effectivenes of the distributed model predictive control. The convergence rate of the result is much faster compared to other control law.

Abstrak :
This book contains a unique description of the nonholonomic motion of systems of rigid bodies by differential algebraic systems. Nonholonomic Motion of Rigid Mechanical Systems from a DAE Viewpoint focuses on rigid body systems subjected to kinematic constraints (constraints that depend on the velocities of the bodies, e.g., as they arise for nonholonomic motions) and discusses in detail how the equations of motion are developed. The authors show that such motions can be modeled in terms of differential algebraic equations (DAEs), provided only that the correct variables are introduced. Several issues are investigated in depth to provide a sound and complete justification of the DAE model. These issues include the development of a generalized Gauss principle of least constraint, a study of the effect of the failure of an important full-rank condition, and a precise characterization of the state spaces. In particular, when the mentioned full-rank condition is not satisfied, this book shows how a new set of equivalent constraints can be constructed in a completely intrinsic way, where, in general, these new constraints comply with the full-rank requirement. Several equivalent DAE formulations are discussed and analyzed thoroughly. The value of these DAE models rests upon the premise that they are more accessible than others to an effective numerical treatment. To substantiate this, a numerical algorithm is presented and numerical results for several standard problems are included to demonstrate the efficiency of this approach.

Abstrak :
This book shows how, through certain geometric transformations, some of the standard joints used in parallel robots can be replaced with lockable or non-holonomic joints. These substitutions allow for reducing the number of legs, and hence the number of actuators needed to control the robot, without losing the robot's ability to bring its mobile platform to the desired configuration. The kinematics of the most representative examples of these new designs are analyzed and their theoretical features verified through simulations and practical implementations.