Supplementary Figure 1. Schematic of high-content imaging-based internalization assay. Supplementary Figure 2. OVCAR3 xenografts were grown subcutaneously in NSG mice and treated with a single i.v. dose of 10 mg/kg control IgG1 or CDH6-targeting antibodies conjugated to SMCC-DM1. Supplementary Figure 3. Tumors of the PDX model HOVX2263 were grown subcutaneously in female nude mice randomized into groups of equal mean tumor volume and treated every two weeks with a 5 mg/kg i.v. dose of either IgG1-SPDB-DM4, or CDH6-targeting antibodies conjugated to SPDB-DM4. Supplementary Figure 4. Interaction analysis of anti-CDH6 antibody and CDH6 ECD protein. Supplementary Figure 5. Unenrolled NSG mice bearing OVCAR3 tumors from a separate efficacy study were allowed to grow to ~600 mm3 before being treated with either IgG1-SPDB-DM4 or CDH6-SPDB-DM4 at 8.5 mg/kg i.v. on day 34 post implant and re-dosed as indicated by arrows. Supplementary Figure 6. Representative CDH6 IHC images of the OVCAR3 subcutaneous xenograft grown in NSG mice (A), OVCAR3Luc intraperitoneal xenograft grown in SCID beige mice (B), HOVX2263 ovarian PDX subcutaneous xenograft grown in female nude mice (C), and HOVX4863 ovarian PDX subcutaneous xenograft grown in female nude mice (D). Supplementary Figure 7. (A) Correlation plot of response to HKT288 by best average response vs. CDH6 RNA expression. (B) Waterfall plot of percent best average response to CDH6-sulfoSPDB-DM4 treatment in PCT.

This file contains supplementary tables describing parameters for antitumor activity (T/C analyses), PK and protein crystallography.

This file describes supplementary material and methods used.

This file contains the figure legends for supplementary figures S1-S7.

Compared with most existing results concerning unmanned aerial vehicles (UAVs) wherein two-degree or only attitude/longitudinal dynamics are considered, this paper proposes an event-based fault-tolerant coordinated control (FTC) for multiple fixed-wing UAVs such that the consensus tracking of velocity and attitude is achieved in the presence of actuator faults, external disturbances and modeling uncertainties. More precisely, as opposed to static event-triggered communication mechanisms, a dynamic event-triggered communication mechanism (DECM) is devised to schedule the connected communications while avoiding the unnecessary information exchanges among UAVs, which reduces the communication burden and saves on the network resources. Meanwhile, the Zeno phenomenon is excluded in terms of guaranteeing that the period between two consecutive triggering communication is lower bounded by a positive constant. Moreover, the actuator fault, external disturbance as well as model uncertainty are treated as the lumped disturbances and estimated via the disturbance observer technique. By strict Lyapunov arguments, all closed-loop signals are proved to be uniformly ultimately bounded (UUB) and the tracking errors of velocity and attitude converge to a residual set around origin. Finally, simulation results are presented to illustrate the validity and superiority of proposed event-based control scheme.

Different from the finite/fixed-time control methodologies on longitudinal/attitude synchronization or 2-D motion of UAVs, this article attempts to propose a distributed adaptive specified-time control scheme for synchronization tracking of networked 6-degree-of-freedom (DOF) UAVs. To be specific, the novel specified-time performance functions (STPFs) are designed in such a way that the desired performance bounds can be imposed on velocity and attitude tracking errors. Based on the transformed errors, by utilizing the barrier Lyapunov functions (BLFs), a distributed specified-time control scheme is constructed with adaptive robustifying terms to enhance the fault-tolerant ability and compensate the modeling uncertainties. By means of Lyapunov stability theory, it is proved that the resulting control scheme can guarantee the boundedness of all closed-loop state variables, and preserve the guaranteed performance bounds for synchronization tracking errors of velocity and attitude at the same time. Theoretical results are confirmed by experiment and simulation validations.

In contrast with most existing results concerning unmanned aerial vehicles (UAVs) wherein material points or only attitude/longitudinal dynamics are considered, this article proposes a distributed fixed-time fault-tolerant control methodology for networked fixed-wing UAVs whose dynamics are six-degree-of-freedom with twelf-state-variables subject to actuator faults and full-state constraints. More precisely, state transformations with the scaling function are devised to keep the involved velocity and attitude within their corresponding constraints. The fixed-time property is obtained in the sense of guaranteeing that the settling time is lower bounded by a positive constant, which is independent of initial states. The actuator faults as well as the network induced errors are handled via the bound estimation approach and well-defined smooth functions. By strict Lyapunov arguments, all closed-loop signals are proved to be semiglobally uniformly ultimately bounded, and the tracking errors of velocity and attitude converge to the residual sets around origin within a fixed time.

This paper is devoted to the cooperative tracking control of multiple unmanned aerial vehicles with unknown faults. Considering the practical case that the unmanned aerial vehicles suffer from strong nonlinearities, external disturbances, actuator, and sensor faults, this brief investigates the distributed adaptive fault tolerant scheme for multi-unmanned aerial vehicle system within model predictive control framework. Firstly, for preparation to the fault occurrence case, the baseline model predictive control scheme is designed under fault-free case. Then considering the fault occurrence, a fault detection strategy is proposed by means of the moving horizon estimation and linearly parameterized approximation for actuator and sensor faults, respectively. Thereby with the characterized fault information, the fault tolerant model predictive control scheme is constructed by using an adaptive updating mechanism to compensate for actuator and sensor faults simultaneously. Finally, simulations well demonstrate the effectiveness of proposed control scheme.

In contrast with most existing results concerning unmanned aerial vehicles (UAVs) wherein two-degree or only attitude/longitudinal dynamics are considered, this article proposes an event-triggered cooperative synchronization fault-tolerant control (FTC) methodology for multiple fixed-wing UAVs whose dynamics are six-degree-of-freedom (DOF) with twelf-state-variables subject to actuator faults, modeling uncertainties, and external disturbances. More precisely, an event-triggering mechanism is devised to determine the time instants of updating control signals, which reduces the signal transmission burden, while saving on system resources. The Zeno phenomenon is excluded in the sense of guaranteeing that the time between two consecutive switchings is lower bounded by a positive constant. The actuator faults as well as the network induced errors are handled via the bound estimation approach and some well-defined smooth functions. By strict Lyapunov arguments, all closed-loop signals are proved to be semi-globally uniformly ultimately bounded (SGUUB) and the synchronization tracking errors of speed and attitude converge to a residual set around origin whose size can be made arbitrarily small through selecting appropriate design parameters.

This paper addresses the consensus tracking problem of leader-following heterogeneous multi-agent systems with iterative learning control. The model of heterogeneous multi-agent systems consists of first-order and second-order nonlinear dynamics. It is assumed that only a portion of following agents can receive the leader's information. The radial basis function neural network is introduced to deal with the nonlinear terms of following agents. Then, the distributed adaptive iterative learning control protocols with neural network are designed for following agents with different dynamics. Meanwhile, the adaptive update control laws for the time-varying parameters are proposed. Theoretical analysis shows that the consensus tracking problem of the given multi-agent systems can be guaranteed on the time domain and iterative domain. Finally, the validity of theoretical results is verified by a simulation example.

This paper studies the disturbance observer-based model predictive control approach to deal with the unmanned aerial vehicle formation flight with unknown disturbances. The distributed control problem for a class of multiple unmanned aerial vehicle systems with reference trajectory tracking and disturbance rejection is formulated. Firstly, a local distributed controller is designed by using the model predictive control method to achieve stable tracking, where the local optimization problem is solved by an adaptive differential evolution algorithm. Then, a feedforward compensation controller is introduced by using a disturbance observer to estimate and compensate disturbances, and improve the ability of anti-interference. Besides, the stability of the proposed composite controller is analyzed as well. Finally, the simulation examples are provided to illustrate the validity of proposed control structure.

Signal quantization can reduce communication burden in multi-agent systems, whereas it brings control challenge to multi-agent formation tracking. This paper studies the output feedback control problem for formation tracking of multi-agent systems with both quantized input and output. The agents are described by a nonlinear dynamic model with unknown parameters and immeasurable states. To estimate immeasurable states and solve the uncertainties, state observers are developed by using dynamic high-gain tools. Through proper parameter designs, an output feedback quantized controller is established based on quantized output signals, and the quantization effect on the control system is eliminated. Stability analysis proves that, with the proposed control scheme, multi-agent systems can track the reference trajectory while forming and maintaining the desired formation shape. In addition, all the signals in the closed-loop systems are bounded. Finally, the numerical simulation and practical experiment are provided to verify the theoretical analysis.

This article studies the adaptive model predictive control with extended state observers (ESO) to deal with multiple unmanned aerial vehicles formation flight in presence of external disturbances and system uncertainties. Specifically, to deal with the mismatch of predictive model caused by external disturbances and system uncertainties, ESOs are introduced to estimate the lumped disturbances, where the ultimately bounded property of observer system can be guaranteed by using the Lyapunov stability theorem. With these observations, the distributed adaptive model predictive controller is designed to achieve trajectory tracking and disturbance rejection simultaneously for multiple unmanned aerial vehicles, as well as taking the state and input saturation into account. Moreover, the stability of proposed model predictive controller is analyzed. Finally, the simulation examples are provided to illustrate the validity of the proposed control scheme.

In this paper, the consensus problem of leader-following nonlinear multi-agent systems with packet dropout is addressed. The iterative learning control method is applied to design the control protocol. Then, a distributed control protocol is presented, and a sufficient condition is derived. In addition, the Bernoulli distribution process is introduced to model the packet dropout case, where the dropout rate is converted into a stochastic parameter. The convergence of proposed control protocol is analyzed by norm theory. It is proved that, when there exists the packet dropout, the output of all the following agents can track the trajectory of leader under the proposed control protocol. Finally, two examples are provided to illustrate the validity of the theoretical analysis.

The complicated and constrained reconfiguration optimisation for unmanned aerial vehicles (UAVs) is a challenge, particularly when multi-mission requirements are taken into account. In this study, we evaluate the use of the adaptive differential evolution-based centralised receding horizon control approach to achieve the formation reconfiguration along a given formation group trajectory for multiple unmanned aerial vehicles in a three-dimensional (3D) environment. A rolling optimisation approach which combines the receding horizon control method with the adaptive differential evolution algorithm is proposed, where the receding horizon control method divides the global control problem into a series of local optimisations and each local optimisation problem is solved by an adaptive differential evolution algorithm. Furthermore, a novel quadratic reconfiguration cost function with the topology information of UAVs is presented, and the asymptotic convergence of the rolling optimisation is analysed. Finally, simulation examples are provided to illustrate the validity of the proposed control structure.

In this paper, the consensus tracking problem of leader-following nonlinear control time-delay multiagent systems with directed communication topology is addressed. An improved high-order iterative learning control scheme with time-delay is proposed, where the local information between agents is considered. The uniformly global Lipschitz condition is applied to deal with the nonlinear dynamics. Then, a sufficient condition is driven, which guarantees that all the following agents track the trajectory of leader. Also, the convergence of proposed control protocol is analyzed by the norm theory. Finally, two cases are provided to illustrate the validity of theoretical results.

An adaptive control approach is presented for time-varying formation control of multiple UAVs with nonholonomic constraints and input quantization. The UAVs are described by nonholonomic kinematic model and autopilot model with uncertainties. A transverse function is designed to release the nonholonomic constraints. To avoid chattering, an enhanced hysteretic quantizer is utilized to process the input signals. The quantized signals are analyzed by a new decomposition method to release some restrictions. Based on Lyapunov stability theory, the adaptive backstepping controller is proposed for the formation tracking of multiple UAVs. Tuning functions are devised to make estimations of the unknown parameters and disturbances. A transformation function is applied to the control inputs to eliminate quantization effect. Stability analysis proves that the tracking errors can converge to the origin asymptotically, and all the signals in the closed-loop system are globally bounded. A simulation example is provided to illustrate the effectiveness of the proposed approach. Based on the control approach, the multi-UAV system can track the reference trajectory while forming and maintaining the predefined formation shape.

In this paper, the consensus tracking control problem of leader-following nonlinear multiagent systems with iterative learning control is investigated. The model of each following agent consists of second-order unknown nonlinear dynamics and the external disturbance. Moreover, the input of each following agent is subject to saturation constraint. It is assumed that the information of leader is not available to any following agents, and the radial basis function neural network is introduced to approximate the nonlinear dynamics. Then, a distributed adaptive neural network iterative learning control protocol and the adaptive updating laws for the time-varying parameters are proposed, respectively. A new Lyapunov function is constructed to analyze the validity of the presented control protocol. Finally, a numerical example is provided to verify the effectiveness of theoretical results.

This paper presents a dynamical recurrent neural network- (RNN-) based model predictive control (MPC) structure for the formation flight of multiple unmanned quadrotors. A distributed hierarchical control system with the translation subsystem and rotational subsystem is proposed to handle the formation-tracking problem for each quadrotor. The RNN-based MPC is proposed for each subsystem, where the RNN is introduced as the predictive model in MPC. And to improve the modeling accuracy, an adaptive updating law is developed to tune weights online for the RNN. Besides, the adaptive differential evolution (DE) algorithm is utilized to solve the optimization problem for MPC. Furthermore, the closed-loop stability is analyzed; meanwhile, the convergence of the DE algorithm is discussed as well. Finally, some simulation examples are provided to illustrate the validity of the proposed control structure.

This paper studies the extended state observer-based state space predictive control approach to deal with the multiple unmanned aerial vehicle formation flight with unknown disturbances. The distributed control problem for a class of multiple unmanned aerial vehicle systems with reference trajectory tracking and disturbance rejection is formulated. Firstly, a local distributed controller is designed by using the state space predictive control approach based on an error model to achieve stable tracking. Then, a feedforward compensation controller is introduced by using the extended state observer to estimate and compensate disturbances and improve the ability of anti-interference. Besides, the bounded stability of the designed extended state observer is analyzed as well. Finally, the simulation examples are provided to illustrate the validity of the proposed control structure.