Neuro-adaptive augmented distributed nonlinear dynamic inversion for consensus of nonlinear agents with unknown external disturbance

This paper presents a novel neuro-adaptive augmented distributed nonlinear dynamic inversion (N-DNDI) controller for consensus of nonlinear multi-agent systems in the presence of unknown external disturbance. N-DNDI is a blending of neural network and distributed nonlinear dynamic inversion (DNDI), a new consensus control technique that inherits the features of Nonlinear Dynamic Inversion (NDI) and is capable of handling the unknown external disturbance. The implementation of NDI based consensus control along with neural networks is unique in the context of multi-agent consensus. The mathematical details provided in this paper show the solid theoretical base, and simulation results prove the effectiveness of the proposed scheme.Engineering and Physical Sciences Research Council (EPSRC): EP/R009953/1

Bipartite consensus of nonlinear agents in the presence of communication noise

In this paper, a Distributed Nonlinear Dynamic Inversion (DNDI)-based consensus protocol is designed to achieve the bipartite consensus of nonlinear agents over a signed graph. DNDI inherits the advantage of nonlinear dynamic inversion theory, and the application to the bipartite problem is a new idea. Moreover, communication noise is considered to make the scenario more realistic. The convergence study provides a solid theoretical base, and a realistic simulation study shows the effectiveness of the proposed protocol.Engineering and Physical Sciences Research Council (EPSRC): EP/R009953/

Adaptive finite-time control of multi-agent systems with partial state constraints and input saturation via event-triggered strategy

This paper focuses on the finite-time control problem of multi-agent systems with input saturation, unknown nonlinear dynamics, external disturbances and partial state constraints via output feedback. Fuzzy logic system and fuzzy state observer are introduced to approximate the uncertain nonlinearities and estimate the unmeasurable states, respectively. The partial state constraints are dealt with by using the barrier Lyapunov function, so that all states of the system do not exceed the preset boundary values. In order to reduce the computational complexity of the virtual controller and save communication resources, a first-order filter and an event-triggered mechanism are introduced, respectively. It is proved that the Zeno behavior does not occur via the proposed event-triggered controller. By stability analysis, the finite-time convergence of tracking error to a small neighborhood of the origin is proven. The effectiveness of the theoretical results is verified by examples.http://wileyonlinelibrary.com/iet-cthhj2023Electrical, Electronic and Computer Engineerin

Adaptive consensus based formation control of unmanned vehicles

Over the past decade, the control research community has given significant attention to formation control of multiple unmanned vehicles due to a variety of commercial and defense applications. Consensus-based formation control is considered to be more robust and reliable when compared to other formation control methods due to scalability and inherent properties that enable the formation to continue even if one of the vehicles experiences a failure. In contrast to existing methods on formation control where the dynamics of the vehicles are neglected, this dissertation in the form of four papers presents consensus-based formation control of unmanned vehicles-both ground and aerial, by incorporating the vehicle dynamics. First, neural networks (NN)-based optimal adaptive consensus-based formation control over finite horizon is presented for networked mobile robots or agents in the presence of uncertain robot/agent dynamics and communication. In the second paper, a hybrid automaton is proposed to control the nonholonomic mobile robots in two discrete modes: a regulation mode and a formation keeping mode in order to overcome well-known stabilization problem. The third paper presents the design of a distributed consensus-based event-triggered formation control of networked mobile robots using NN in the presence of uncertain robot dynamics to minimize communication. All these papers assume state availability. Finally, the fourth paper extends the consensus effort by introducing the development of a novel nonlinear output feedback NN-based controller for a group of quadrotor UAVs --Abstract, page iv

Computational intelligence approaches to robotics, automation, and control [Volume guest editors]

No abstract available

Robust and Cooperative Formation Control of Nonlinear Multi-Agent Systems

Compared with the conventional approach of controlling autonomous systems individually, building up a cooperative multi-agent structure is more robust and efficient for both research and industrial purposes. Among the many subbranches of multiagent systems, formation control has been a popular research direction due to its close connection with complex missions such as spacecraft clustering and intelligent transportation. Hence, this thesis focuses on providing new robust formation control algorithms for first-order, second-order and mixed-order nonlinear multi-agent systems to construct and maintain stable system structure in practical scenarios. System uncertainties and external disturbances are commonly seen factors that could negatively affect the formation tracking precision. Among the many popular tools of uncertainty estimation, the implementation of approaches including neural network adaptive estimation and observer-based approximation are discussed in this thesis. Regarding the neural-based approximation process, different neural network structures including Chebyshev neural network, radial basis function neural network, twolayer artificial neural network and three-layer artificial neural network are tested and implemented. The merits and drawbacks of each network design in the field of control is then analysed. Apart from that, this thesis also offers detailed comparison between the cooperative tuning approach and the observer-based tuning approach regarding the neural network structure to find their corresponding applicable scenarios. To ensure the safety of the formation control algorithms, the issues of obstacle avoidance and inter-agent collision avoidance are both considered. Although the method of constructing artificial potential fields is a popular approach in both the field of path planning and motion control, few have discussed the effect of the inter-agent communication on the collision avoidance scheme. For the obstacle avoiding scenarios, the passive correcting behaviour of individual agent is defined and investigated. A new algorithm is then introduced to modify the reference of individual agents to act as the mitigation. The issue of insufficient information accessibility is then discussed for multi-agent systems with a static and uncompleted communication topology. A distance-based communication topology is proposed to create necessary information exchange channel for unconnected agent pairs that are close enough. The actuator saturation issue is also considered for both first-order multi-agent systems and second-order multi-agent systems to increase the practicality of the formation control schemes. Apart from restricting the amplitudes of the control input, the effect of the input coupling phenomenon is investigated. The oscillation of states brought by the coupled and saturated control input is then summarised as the reverse effect. To attenuate the state oscillation, the methods of developing control input regulation algorithms and employing auxiliary compensator are discussed and validated. The last technical problem to discuss is the hierarchical control scheme. The issue of how to decouple the inter-agent communication and the motion dynamics is discussed for both unified-order and mixed-order multi-agent systems. By using a hierarchical formation control structure, the inter-agent communication process is considered based on a group of virtual agents with ideal characteristics, which can significantly reduce the complexity of the system design. Adaptive hierarchical control schemes are then proposed and validated for both unified-order and mixed-order multi-agent systems through the examples of a multi-drone system and a multiple omni-directional robot system, respectively.Thesis (Ph.D.) -- University of Adelaide, School of Electrical and Electronic Engineering, 202

Computational intelligence approaches to robotics, automation, and control [Volume guest editors]

No abstract available