Adaptive Control Protocol Research Articles

Cross-Layer-Based Adaptive Traffic Control Protocol for Bluetooth Wireless Networks

Bluetooth technology is particularly designed for a wireless personal area network that is low cost and less energy consuming. Efficient transmission between different Bluetooth nodes depends on network formation. An inefficient Bluetooth topology may create a bottleneck and a delay in the network when data is routed. To overcome the congestion problem of Bluetooth networks, a Cross-layer-based Adaptive Traffic Control (CATC) protocol is proposed in this paper. The proposed protocol is working on backup device utilization and network restructuring. The proposed CATC is divided into two parts; the first part is based on intra-piconet traffic control, while the second part is based on inter-piconet traffic control. The proposed CATC protocol controls the traffic load on the master node by network restructuring and the traffic load of the bridge node by activating a Fall-Back Bridge (FBB). During the piconet restructuring, the CATC performs the Piconet Formation within Piconet (PFP) and Scatternet Formation within Piconet (SFP). The PFP reconstructs a new piconet in the same piconet for the devices which are directly within the radio range of each other. The SFP reconstructs the scatternet within the same piconet if the nodes are not within the radio range. Simulation results are proof that the proposed CATC improves the overall performance and reduces control overhead in a Bluetooth network.

Energy Efficiency Comparison of a State Based Adaptive Transmission Protocol with Fixed Power Transmission for Mobile Wireless Sensors

In this paper a state-based adaptive power control protocol (SAPC) has been compared with classical fixed power communication for mobile wireless sensors. The distance between the transmitter and the base station is often not fixed as in the case of body wearable sensors. There can also be unaccounted obstructions in between the transmitter and the receiver. Since signal level attenuates with distance, it is important to choose the right power level that will not only deliver the packets with minimum error but conserve energy at the same time. The proposed adaptive algorithm does not transmit beacon or probe packet for channel quality estimation using the received signal strength before transmitting actual packets. It uses the present and past history of the outcome of packet transmission to evaluate and track link quality. The unique SAPC algorithm also controls the number of re-transmissions in each state. Experimental validation has been done using nRF24L01p transceiver modules. This algorithm can adapt itself to an unknown and variable radio channel in an energy-efficient manner. Experiments were conducted in indoor office environment within a university building and results show that SAPC uses up to 30% less energy than the fixed power communication

An adaptive cross-layer error control protocol for wireless multimedia sensor networks

In this paper, we introduce a new adaptive cross-layer error control protocol (NAC) for reliable and efficient real-time multimedia streaming in Wireless Sensor Networks (WSN). The proposed protocol uses an adaptive Forward Error Correction (FEC) algorithm in the application layer, a dynamic hybrid FEC/ARQ method, and an Unequal Error Protection (UEP) mechanism in the wireless link layer. Moreover, it dynamically tunes the amount of erasure codes per group of packets based on mean absolute deviation of recently measured average round-trip times, and cross-layer information such as arrival of acknowledgment packets, network traffic load, and wireless channel state. In fact, by dynamically determining suitable redundancy for the source packet based on the packet loss rate in the hybrid FEC/ARQ error control algorithm in the wireless link layer, we achieve significant improvements over the existing error control schemes, while decreasing the number of retransmissions in ARQ mechanism. Our comprehensive simulations show that the proposed NAC protocol achieves better performance compared to the popular error control schemes in terms of energy efficiency, frame loss rate, frame Peak Signal-to-Noise Ratio (PSNR), and delay-constrained PSNR for real-time multimedia streaming over sensor networks.

Adaptive low-priority congestion control for high bandwidth-delay product and wireless networks

The low-priority service is an exciting and attractive choice for networking applications (e.g. automatic update, backup, peer-to-peer file share) which create traffic that is considered less urgent than that of others and become a renewed interest at the Internet Engineering Task Force (IETF). A low-priority protocol, which provides the low-priority service, can exploit the residual bandwidth of the bottleneck link and achieve the high throughput, low-latency data delivery in traditional networks. However, due to the conservative and inappropriate congestion control mechanisms, the existing low-priority protocols (e.g. LEDBAT) cannot effectively utilize the residual bandwidth of the bottleneck link in high bandwidth-delay product (HBDP) and wireless networks. In this paper, we propose an adaptive Congestion level-based Low-Priority congestion Control (CLPC) protocol to improve the efficiency of low-priority protocols and maintain the low-priority features. Specifically, the CLPC sender adopts an one-way path delay to estimate the congestion level and adjust the aggressiveness of congestion control mechanisms. Different from other low-priority protocols, the CLPC protocol is more aggressive when the bottleneck link of HBDP and wireless networks has residual bandwidth. This makes a faster convergence of link utilization in HBDP networks. Combining the random loss detection, CLPC can achieve the high throughput in wireless networks. The extensive simulations in NS-2 show that CLPC can improve the transmission performance significantly as compared to other low-priority protocols in HBDP and wireless networks. Furthermore, we implement the CLPC protocol in the Linux kernel (3.13) and setup a testbed to measure it. The results also indicate the feasibility and effectiveness of CLPC.

Distributed consensus of non‐linear fractional‐order multi‐agent systems with directed topologies

In this study, Mittag–Leffler stability and the fractional Lyapunov direct method are used to study fractional-order leaderless and leader-following consensus algorithms with non-linear dynamics under a directed network topology. The sufficient conditions are given to guarantee that the consensus can be achieved in the non-linear fractional-order systems with and without a virtual leader, and the adaptive feedback control protocols are also given in this study. Finally, some numerical simulations are presented to demonstrate the effectiveness of the theoretical results.

Consensus for multi‐agent systems with distributed adaptive control and an event‐triggered communication strategy

This study addresses the consensus problem of multi-agent systems by combining adaptive control protocol and event-triggered communication strategy. Two adaptive protocols are designed to guarantee the consensus. The event-triggered communication strategy is utilised to save communication and reduce channel occupation. Each agent in the system sends its state to neighbours only at some irregular event instants determined by an appropriately designed event-triggering condition. At first, the distributed event-triggering condition of an agent only employs the state of its own and the state of the neighbours at their latest event instants. Later, another event-triggering condition is given such that no global parameter is required for agents, such as the maximum eigenvalue of the Laplacian matrix. It is proven that the system can reach consensus by utilising the proposed control method. Illustrative examples are given to show the effectiveness of the control strategy.

Neural-Network-Based Distributed Adaptive Robust Control for a Class of Nonlinear Multiagent Systems With Time Delays and External Noises

A class of nonlinear multiagent systems with time delays and external noises is investigated, and a distributed adaptive robust control protocol is developed. It is the first time for a class of multiagent systems to take both time delays and external noises into consideration. By virtue of Lyapunov–Krasovskii functional and Young’s inequality, the effects of time delay can be eliminated. Then, to exclude external noises, a robustifying term is introduced to eliminate the negative effects of these noises. Moreover, neural networks are utilized to learn the unknown nonlinear terms to adapt to the complex external environment. Finally, a numerical simulation is conducted to validate the effectiveness of our distributed control protocol.

Distributed adaptive time-varying formation for multi-agent systems with general high-order linear time-invariant dynamics

This paper studies distributed time-varying formation control problems for general high-order linear time-invariant (LTI) multi-agent systems using an adaptive based approach. Firstly, a time-varying formation control protocol is constructed by the states of neighboring agents, which the adaptive gain scheduling technique is employed to estimate the coupling weights between neighboring agents. Different from the existing results on formation control, the formation can be specified by piecewise continuously differentiable vectors and no global information about the interaction topologies is required. Then an algorithm with two steps is presented to design the distributed adaptive formation control protocol, where a description of the feasible time-varying formation set is given. The stability of the algorithm is proved using the Lyapunov theory. It is shown that if the predefined time-varying formation belongs to the feasible formation set and each agent is stabilizable, then time-varying formation can be achieved by general high-order LTI multi-agent systems using the distributed adaptive formation protocol designed in the proposed algorithm. Finally, numerical examples are given to demonstrate the effectiveness of the theoretical results.

Distributed adaptive iterative learning control for multiple robot manipulators

A hybrid adaptive and iterative learning method was proposed to obtain distributed control protocols for multiple manipulator systems with undirected interaction topology to achieve consensus tracking of the specified desired reference trajectory. By introducing an appropriate adaptive iterative learning parameter,the proposed adaptive iterative learning control( AILC) protocol can overcome the effects of disturbances and model uncertainties of manipulators,where the AILC law of each manipulator needs only the relative information between it and its nearest neighbors. Moreover,it is shown that all manipulators can be rendered to achieve the perfect tracking of the desired reference trajectory though its information can be accessed by only a portion of manipulators,where the boundedness of both the tracking error and the control input can be simultaneously guaranteed. In addition,the Lyapunov analysis method is employed to validate the obtained results,and the effectiveness of the proposed AILC protocol is illustrated through an example.

Adaptive Tracking Control of Second-Order Multiagent Systems with Jointly Connected Topologies

This paper considers a consensus problem of leader-following multiagent system with unknown dynamics and jointly connected topologies. The multiagent system includes a self-active leader with an unknown acceleration and a group of autonomous followers with unknown time-varying disturbances; the network topology associated with the multiagent system is time varying and not strongly connected during each time interval. By using linearly parameterized models to describe the unknown dynamics of the leader and all followers, we propose a decentralized adaptive tracking control protocol by using only the relative position measurements and analyze the stability of the tracking error and convergence of the adaptive parameter estimators with the help of Lyapunov theory. Finally, some simulation results are presented to demonstrate the proposed adaptive tracking control.

Distributed Synchronization Control of Multiagent Systems With Unknown Nonlinearities.

This paper revisits the distributed adaptive control problem for synchronization of multiagent systems where the dynamics of the agents are nonlinear, nonidentical, unknown, and subject to external disturbances. Two communication topologies, represented, respectively, by a fixed strongly-connected directed graph and by a switching connected undirected graph, are considered. Under both of these communication topologies, we use distributed neural networks to approximate the uncertain dynamics. Decentralized adaptive control protocols are then constructed to solve the cooperative tracker problem, the problem of synchronization of all follower agents to a leader agent. In particular, we show that, under the proposed decentralized control protocols, the synchronization errors are ultimately bounded, and their ultimate bounds can be reduced arbitrarily by choosing the control parameter appropriately. Simulation study verifies the effectiveness of our proposed protocols.

Performance Comparison of a Novel Adaptive Protocol with the Fixed Power Transmission in Wireless Sensor Networks

In this paper, we compare the performance of a novel adaptive protocol with the fixed power transmission protocol using experimental data when the distance between the transmitter and the receiver is fixed. In fixed power transmission protocol, corresponding to the distance between the sensor and the hub, there is a fixed power level that provides the optimal or minimum value in terms of energy consumption while maintaining a threshold Quality of Service (QoS) parameter. This value is bounded by the available output power levels of a given radio transceiver. The proposed novel adaptive power control protocol tracks and supersedes that energy expenditure by using an intelligent algorithm to ramp up or down the output power level as and when required. This protocol does not use channel side information in terms of received signal strength indication (RSSI) or link quality indication (LQI) for channel estimation to decide the transmission power. It also controls the number of allowed retransmissions for error correction. Experimental data have been collected at different distances between the transmitting sensor and the hub. It can be observed that the energy consumption of the fixed power level is at least 25% more than the proposed adaptive protocol for comparable packet success rate.

Distributed consensus of multi-agent systems with nonlinear dynamics via adaptive intermittent control

The consensus problem is investigated for a class of multi-agent systems with nonlinear dynamics on a fixed directed information network, where each agent is assumed to share information only with its neighbors on some disconnected time intervals. A novel adaptive intermittent control protocol is first introduced, where only intermittent relative local information is used. Based on the Lyapunov stability theory combined with the method of the adaptive control and intermittent control, some novel and simple consensus criteria are derived under a fixed strongly connected topology, it is proved that consensus can be reached if the measure of communication is larger than a threshold value. The results are then extended to consensus with a virtual leader where the underlying topology is not necessarily strong connected or contains a directed spanning tree. Finally, two numerical examples are provided to demonstrate the effectiveness of the obtained theoretical results.

ACCPndn: Adaptive Congestion Control Protocol in Named Data Networking by learning capacities using optimized Time-Lagged Feedforward Neural Network

Named Data Networking (NDN) is a promising network architecture being considered as a possible replacement for the current IP-based Internet infrastructure. However, NDN is subject to congestion when the number of data packets that reach one or various routers in a certain period of time is so high than its queue gets overflowed. To address this problem many congestion control protocols have been proposed in the literature which, however, they are highly sensitive to their control parameters as well as unable to predict congestion traffic well enough in advance. This paper develops an Adaptive Congestion Control Protocol in NDN (ACCPndn) by learning capacities in two phases to control congestion traffics before they start impacting the network performance. In the first phase – adaptive training – we propose a Time-Lagged Feedforward Network (TLFN) optimized by hybridization of particle swarm optimization and genetic algorithm to predict the source of congestion together with the amount of congestion. In the second phase -fuzzy avoidance- we employ a non-linear fuzzy logic-based control system to make a proactive decision based on the outcomes of first phase in each router per interface to control and/or prevent packet drop well enough in advance. Extensive simulations and results show that ACCPndn sufficiently satisfies the applied performance metrics and outperforms two previous proposals such as NACK and HoBHIS in terms of the minimal packet drop and high-utilization (retrying alternative paths) in bottleneck links to mitigate congestion traffics.

Lag consensus of the second-order leader-following multi-agent systems with nonlinear dynamics

Lag consensus is a phenomenon where followers track the trajectory of the leader with a time delay. By using lag consensus, a protocol is designed for agents to behind the leader at different times, so as to avoid congestion. In this paper, aiming to the lag consensus of second-order nonlinear multi-agent systems, a control protocol for each follower based on local information of neighboring agents is proposed, and an adaptive feedback control protocol is also given. Moreover, the multi-agent systems with noisy environment are considered. The results suggest that our protocol is robust to the noise. Finally, simulation examples are given to illustrate our theoretical analysis.

Adaptive robotic control driven by a versatile spiking cerebellar network.

The cerebellum is involved in a large number of different neural processes, especially in associative learning and in fine motor control. To develop a comprehensive theory of sensorimotor learning and control, it is crucial to determine the neural basis of coding and plasticity embedded into the cerebellar neural circuit and how they are translated into behavioral outcomes in learning paradigms. Learning has to be inferred from the interaction of an embodied system with its real environment, and the same cerebellar principles derived from cell physiology have to be able to drive a variety of tasks of different nature, calling for complex timing and movement patterns. We have coupled a realistic cerebellar spiking neural network (SNN) with a real robot and challenged it in multiple diverse sensorimotor tasks. Encoding and decoding strategies based on neuronal firing rates were applied. Adaptive motor control protocols with acquisition and extinction phases have been designed and tested, including an associative Pavlovian task (Eye blinking classical conditioning), a vestibulo-ocular task and a perturbed arm reaching task operating in closed-loop. The SNN processed in real-time mossy fiber inputs as arbitrary contextual signals, irrespective of whether they conveyed a tone, a vestibular stimulus or the position of a limb. A bidirectional long-term plasticity rule implemented at parallel fibers-Purkinje cell synapses modulated the output activity in the deep cerebellar nuclei. In all tasks, the neurorobot learned to adjust timing and gain of the motor responses by tuning its output discharge. It succeeded in reproducing how human biological systems acquire, extinguish and express knowledge of a noisy and changing world. By varying stimuli and perturbations patterns, real-time control robustness and generalizability were validated. The implicit spiking dynamics of the cerebellar model fulfill timing, prediction and learning functions.

Fully distributed tracking control for non‐identical multi‐agent systems with matching uncertainty

SummaryThis article addresses the fully distributed consensus tracking control problem of linear multi‐agent systems with parameter uncertainties. First, a new class of distributed protocol, based on the relative states of neighbors, is proposed. Theoretical analysis indicates that the considered problem can be solved if the control gain constant of the protocol is larger than the norm bound of the leader's nonlinear inputs. Furthermore, a distributed adaptive control protocol is proposed for the case without available global information. The distributed consensus tracking control problem of uncertain linear multi‐agent systems is solved based only on local information under the proposed adaptive protocol. Finally, an application in low‐Earth‐orbit satellite formation flying is provided to illustrate the theoretical results. Copyright © 2014 John Wiley & Sons, Ltd.

Fault‐tolerant output synchronisation control of multi‐vehicle systems

This study presents a fault-tolerant output synchronisation control approach for a class of non-linear multi-vehicle systems. Firstly, adaptive fault diagnosis observers are designed for vehicle systems by utilising local measurements. Adaptive thresholds for distributed fault-detection of each vehicle system are derived. Then an adaptive fault estimation algorithm is proposed. Using the online obtained fault information, an adaptive fault-tolerant control protocol is designed to ensure the output synchronisation of the networked vehicle systems. It is proved that the proposed active fault-tolerant control protocol is capable of maintaining the output synchronisation performance of the multi-vehicle systems, at an acceptable level, in the presence of faults. Finally, simulation results are presented to show the effectiveness of the proposed algorithms.

Cooperative controller design for synchronization of networked uncertain Euler-Lagrange systems

Summary This paper addresses the synchronization problems with/without a dynamic leader for a team of distributed Lagrange systems on digraph. A systematic way to design and analyze the distributed control algorithms is presented. The contributions of the paper are twofold. First, the adaptive coordination control protocols are proposed for synchronization of networked uncertain Lagrange systems with/without tracking. This protocol can guarantee synchronization in finite time. Second, the design of the distributed tracking controller for the networked dynamic systems is proposed by using Lyapunov methods. The development is suitable for the general digraph communication topologies. Simulation examples are included to demonstrate the effectiveness of the proposed algorithms. Copyright © 2014 John Wiley & Sons, Ltd.

Distributed containment control of second‐order multi‐agent systems with inherent non‐linear dynamics

This study considers the distributed containment control problem of second-order multi-agent systems with inherent non-linear dynamics. Two distributed control protocols with, respectively, static and adaptive control gains are proposed to ensure that the states of the followers asymptotically converge to the convex hull spanned by the corresponding states of the leaders. For the static control protocol, undirected and directed interaction topologies among the followers are taken into consideration, under which sufficient conditions on the control gains are obtained by using a Lyapunov-based approach. For the adaptive control protocol, it is proved that the containment control problem can be solved without requiring any global information if the interaction topology among the followers is undirected. Numerical examples are provided to demonstrate the effectiveness of our theoretical results.