Robust motion control of quadrotors

In this paper, the robust motion control problem is investigated for quadrotors. The proposed controller includes two parts: an attitude controller and a position controller. Both the attitude and position controllers include a nominal controller and a robust compensator. The robust compensators are introduced to restrain the influence of uncertainties such as nonlinear dynamics, coupling, parametric uncertainties, and external disturbances in the rotational and translational dynamics. It is proven that the position tracking errors are ultimately bounded and the boundaries can be specified by choosing controller parameters. Experimental results on the quadrotor demonstrate the effectiveness of the robust control method.     

Robust visual servoing control for quadrotors landing on a moving target

In this paper, a robust visual servoing control approach is proposed to address the landing problem for quadrotors on a moving platform. A vision system is implemented to estimate the position and velocity of the quadrotor. A robust cascade controller is proposed by following backstepping-like fundamentals and robust compensating theory. The effects of time-varying uncertainties, including parameter uncertainties and external disturbances, and time-varying delays resulted from image acquisition, image processing, and sensor measurement delays can be restrained. Experimental results using a quadrotor to land on a ground moving target illustrate the effectiveness of the proposed approach.     

Compound tracking control based on MPC for quadrotors with disturbances

In this paper, a compound control strategy is proposed to realize the trajectory tracking task of quadrotors under operating constraints and disturbances. Disturbances caused by model uncertainties, environmental noises, and measurement disturbances are divided into matched disturbances and unmatched ones, which are compensated and suppressed separately by using two control components. The integral sliding mode control component is designed to actively reject the matched disturbances, and the control system is then transformed into an equivalent control system subject to equivalent disturbances only related to the unmatched disturbances. The remaining equivalent disturbances are treated by a robust model predictive control component based on the idea of constraints tightening, which minimizes the tracking error in an optimization framework and takes both state and input constraints into account explicitly. The derived compound control strategy is based on these two control components. Conditions are provided to guarantee the robust constraint satisfaction, recursive feasibility and closed-loop stability of the tracking error system. An illustrative example on the quadrotors shows the efficiency and robustness of this compound tracking control algorithm.     

Distributed control of cluster lag consensus for first-order multi-agent systems on QUAD vector fields

This paper addresses the problem of cluster lag consensus for first-order multi-agent systems which can be formulated as moving agents in a capacity-limited network. A distributed control protocol is developed based on local information, and the robustness of the protocol is analyzed by using tools of Frobenius norm, Lyapunov functional and matrix theory. It is shown that when the root agents of the clusters are influenced by the active leader and the intra-coupling among agents is stronger enough, the multi-agent system will reach cluster lag consensus. Moreover, cluster lag consensus for multi-agent systems with a time-varying communication topology and heterogeneous multi-agent systems with a directed topology are studied. Finally, the effectiveness of the proposed protocol is demonstrated by some numerical simulations.     

Distributed event-triggered control for non-reliable networks

This paper presents a distributed event-based control approach to cope with communication delays and packet losses affecting a networked dynamical system. Two network protocols are proposed to deal with these communication effects. The stability of the system is analyzed for constant and time-dependent trigger functions, showing that asymptotic stability can be achieved with the latter design, and this also guarantees a lower bound for the inter-event times. Analytical expressions for the delay bound and the maximum number of consecutive packet losses are derived for different scenarios. Finally, the results are illustrated through a simulation example.     

Distributed event-triggered control for networked control systems with stochastic cyber-attacks

This paper is concerned with the problem of distributed event-triggered controller design for networked control systems (NCSs) with stochastic cyber-attacks. A decentralized event-triggered scheme is introduced to save the energy consumption and alleviate the transmission load of the network. Each sensor can make its own decision to determine whether the sampled data is delivered to the network or not. By taking two kinds of random cyber-attacks into consideration, a novel mathematical model is constructed for distributed event-triggered NCSs. Sufficient conditions which can guarantee the stability of the control system are obtained by applying Lyapunov stability theory, and the design method of the controller gain is presented in an exact expression. Finally, an example is given to demonstrate the effectiveness of the proposed method.     

Distributed semistable LQR control for discrete-time dynamically coupled systems

A distributed linear-quadratic-regulator (LQR) semistability theory for discrete-time systems is developed for designing optimal semistable controllers for discrete-time coupled systems. Unlike the standard LQR control problem, a unique feature of the proposed optimal control problem is that the closed-loop generalized discrete-time semistable Lyapunov equation can admit multiple solutions. Necessary and sufficient conditions for the existence of solutions to the generalized discrete-time semistable Lyapunov equation are derived and an optimization-based design framework for distributed optimal controllers is presented.     

Distributed cruise control of high-speed trains

In this work, the cruise control problem of high-speed trains’ movements is investigated. Both cases of a single high-speed train and multiple high-speed trains are under consideration. Different with most existing studies where the centralized control or the decentralized control methods are adopted based on a single point mass model of the train, in this paper, a distributed control mechanism is proposed by virtue of the graph theory, and the high-speed train’s model is built as a cascade of point masses connected by flexible couplers. For a single high-speed train, the neighboring cars interact through the coupling force with each other, which can be described by a connected topological graph by regarding each car as a node. Besides, the speed information communication among the cars is considered to be described by another directed topological graph. A distributed control strategy is then developed, with which all the cars of a train track a desired speed asymptotically and the neighboring cars keep a safety distance from each other. For the multiple high-speed trains running on a railway line, the in-train force interaction topology and the speed information communication topology of all the trains are more complex than those of a single train. A new cluster consensus technique is developed, by which a distributed control law is designed. Under the control law, the trains can track the desired speeds asymptotically, the headway distance between adjacent trains and the distance between the neighboring cars of a train can be kept in appropriate ranges. Finally, simulations are provided to illustrate the effectiveness of the obtained theoretical results.     

Distributed control for output-constrained nonlinear multi-agent systems with completely unknown non-identical control directions

This paper studies the consensus problem for a class of nonlinear multi-agent systems with asymmetric time-varying output constraints and completely unknown non-identical control directions. Firstly, in order to deal with the problem of asymmetric time-varying output constraints, the original output-constrained multi-agent systems are transformed into new unconstrained multi-agent systems by constructing the state transformation for each agent. Secondly, the emergence of multiple Nussbaum-type function terms is avoided by introducing novel sliding-mode-esque auxiliary variables and consensus estimate variables, which allows the control directions to be completely unknown non-identical. Thirdly, a novel control strategy is proposed by combining novel variables with state transformation method for the first time, which makes the design of distributed consensus protocol more concise. Through Lyapunov stability analysis, the proposed distributed protocol ensures that the output constraints are never violated and the consensus can be achieved asymptotically. Finally, a practical simulation example is given to demonstrate the effectiveness of the proposed distributed consensus protocol.     

Distributed RISE control for spacecraft formation reconfiguration with collision avoidance

In this paper, we investigate the distributed formation reconfiguration problem of multiple spacecraft with collision avoidance in the presence of external disturbances. Artificial potential function (APF) based virtual velocity controllers for the spacecraft are firstly constructed, which overcome the local minima problem through introducing auxiliary inputs weighted by bump functions. Then, based on the robust integral of the sign of the error (RISE) control methodology, a distributed continuous asymptotic tracking control protocol is proposed, accomplishing both formation reconfiguration and the collision avoidance among spacecraft and with obstacles. Furthermore, using tools from graph theory, Lyapunov analysis and backstepping technique, we show the stability and collision avoidance performance of the closed-loop multiple spacecraft system. Numerical simulations for a spacecraft formation are finally provided to validate the effectiveness of the proposed algorithm.     

Distributed point-to-point iterative learning control for multi-agent systems with quantization

For multi-agent system (MAS), most of existing iterative learning control (ILC) algorithms consider about the tracking of reference defined over the whole trial interval, while the point-to-point (P2P) task, where the emphasis is placed on the tracking of intermediate time points, has not been explored. Thus, a distributed ILC method is proposed, in which each agent updates the feedforward control input by learning from the experience of itself and its neighbors in previous repeated tasks to achieve the goal of improving performance. In addition, for the sake of reducing the burden of data transmission in MAS, effective data quantization is essential. In this case, the quantitative measurement of the error of the tracking time points is further used in the ILC updating law. In order to accommodate this requirement, a distributed point-to-point iterative learning control (P2PILC) with tracking error quantization for MAS is first proposed in this paper. A necessary and sufficient condition is presented for the asymptotical stability of the proposed algorithm, and simulation results show the effectiveness of it finally.     

Distributed MPC-based adaptive control for linear systems with unknown parameters

This paper is concerned with the adaptive control problem for a class of linear discrete-time systems with unknown parameters based on the distributed model predictive control (MPC) method. Instead of using the system state, the state estimate is employed to model the distributed state estimation system. In this way, the system state does not have to be measurable. Furthermore, in order to improve the system performance, both the output error and its estimation are considered. Moreover, a novel Lyapunov functional, comprised of a series of distributed traces of estimation errors and their transposes, has been presented. Then, sufficient conditions are obtained to guarantee the exponential ultimate boundedness of the system as well as the asymptotic stability of the error system by solving a nonlinear programming (NP) problem subject to input constraints. Finally, the simulation examples is given to illustrate the effectiveness and the validity of the proposed technique.     

Distributed adaptive event-triggered protocol for tracking control of leader-following multi-agent systems

A novel adaptive event-triggered control protocol is developed to investigate the tracking control problem of multi-agent systems with general linear dynamics. By introducing the event-triggered control strategy, each agent can decide when to transfer its state to its neighbors at its own triggering instants, which can greatly reduce communication burden of agents. It is shown that the “Zeno phenomenon” does not occur by verifying that there exists a positive lower bound on the inter-event time intervals of agents under the proposed adaptive event-triggered control algorithm. Finally, an example is provided to testify the effectiveness of the obtained theoretical results.     

Super twisting control algorithm for the attitude tracking of a four rotors UAV

This paper deals with the design and implementation of a nonlinear control algorithm for the attitude tracking of a four-rotor helicopter known as quadrotor. This algorithm is based on the second order sliding mode technique known as Super-Twisting Algorithm (STA) which is able to ensure robustness with respect to bounded external disturbances. In order to show the effectiveness of the proposed controller, experimental tests were carried out on a real quadrotor. The obtained results show the good performance of the proposed controller in terms of stabilization, tracking and robustness with respect to external disturbances.     

P-type iterative learning control algorithm for a class of linear singular impulsive systems

This paper is focused on the iterative learning control problem for linear singular impulsive systems. For the purpose of tracking the desired output trajectory, a P-type iterative learning control algorithm is investigated for such system. Based on the fundamental property of singular impulsive systems and the restricted equivalent transformation theory of singular systems, the convergence conditions of the tracking errors for the system are obtained in the sense of λ norm. Finally, the validation of the algorithm is confirmed by a numerical example.     

Distributed control of time-delay interconnected nonlinear systems

This paper addresses the distributed control of delayed interconnected nonlinear systems with time-varying delays in both the local subsystems’ dynamics and the physical interconnections among the subsystems. The Takagi–Sugeno fuzzy model with nonlinear consequent parts (N-TS), which is capable to provide less complex representations than standard T–S fuzzy models, is considered to efficiently deal with this class of complex systems. Then, based on Lyapunov–Krasovskii stability arguments, a synthesis condition is proposed to design a distributed control law such that the origin of the closed-loop interconnected system is locally asymptotically stable together with a guaranteed set of admissible initial conditions for which the validity of the N-TS fuzzy model is ensured. Moreover, a quasi-convex optimization procedure is formulated to enlarge the set of admissible initial conditions. The effectiveness of the proposed synthesis condition is validated in two numerical examples, including an interconnected power network with seven generators.     

Distributed robust event-triggered adaptive control for a class of uncertain nonlinear multi-agent systems with actuator saturation

This paper studies the event-triggered consensus control problem for high-order uncertain nonlinear multi-agent systems with actuator saturation. By using a smooth Lipschitz function to approximate the saturation nonlinearity, an augment system and the Nussbaum function are adopted to deal with the residual terms of saturation nonlinearity based on adaptive backstepping method. Since excessive energy and communication resources will be consumed during the procedure to handle actuator saturation, two event-triggered mechanisms are proposed to save the communication resources and reduce the controllers’ update frequency. Whenever the triggered conditions are satisfied, the control signals transmitted to the actuators are updated and broadcasted to the neighboring area. A ’disturbance-like’ term is integrated so that the event-triggered control problem with actuator saturation can be transformed into a robust problem while the unknown disturbances are tackled by adaptive update laws. Moreover, the requirement for global communication topology known by all the agents is relaxed by introducing new estimators. All the signals in the closed-loop system are uniformly bounded and the consensus tracking errors are exponentially converged to a bounded set. Meanwhile, the Zeno behavior is excluded. Simulation results are employed to validate the advantages of our proposed methods.     

Distributed data driven control for multi-agent consensus with unknown system dynamics

This study investigates the consensus tracking problem for unknown multi-agent systems (MASs) with time-varying communication topology by using the methods of data-driven control and model predictive control. Under the proposed distributed iterative protocol, sufficient conditions for reducing tracking error are analyzed for both time invariable and time varying desired trajectories. The main feature of the proposed protocol is that the dynamics of the multi-agent systems are not required to be known and only local input-output data are utilized for each agent. Numerical simulations are presented to illustrate the effectiveness of the derived consensus conditions.