To achieve high-accuracy manipulation in the presence of unknown disturbances, we propose two novel efficient and robust motion control schemes for high-dimensional robot manipulators. Both controllers incorporate an unknown system dynamics estimator (USDE) to estimate disturbances without requiring acceleration signals and the inverse of inertia matrix. Then, based on the USDE framework, an adaptive-gain controller and a super-twisting sliding mode controller are designed to speed up the convergence of tracking errors and strengthen anti-perturbation ability. The former aims to enhance feedback portions through error-driven control gains, while the latter exploits finite-time convergence of discontinuous switching terms. We analyze the boundedness of control signals and the stability of the closed-loop system in theory, and conduct real hardware experiments on a robot manipulator with seven degrees of freedom (DoF). Experimental results verify the effectiveness and improved performance of the proposed controllers, and also show the feasibility of implementation on high-dimensional robots.

The existing inverse optimal methods for canonical nonlinear systems assume that the system is modeled precisely and accurately, but dynamic uncertainties commonly exist and are unavoidable and difficult to model in practical engineering and industrial systems. This work removes this limitation and solves the problem of inverse optimal adaptive control for canonical nonlinear systems with dynamic uncertainties. Technically, a criterion on inverse optimality under dynamic uncertainties is newly proposed based on a new auxiliary system and a meaningful cost functional. With the new criterion, a robust adaptive fuzzy inverse optimal control scheme is proposed to design an inverse optimal controller, which, however, is not necessarily a stable controller. To solve this issue, a projection-based adaptation law is proposed to update the inverse optimal controller. Then, a small-gain approach is proposed to construct the links between inverse optimality and stability and to render that the closed-loop system is input-to-state practically stable. The proposed methods are successfully applied to industrial robots for demonstrations.

Optimizing a performance objective during control operation while also ensuring constraint satisfactions at all times is important in practical applications. Existing works on solving this problem usually require a complicated and time-consuming learning procedure by employing neural networks, and the results are only applicable for simple or time-invariant constraints. In this work, these restrictions are removed by a newly proposed adaptive neural inverse approach. In our approach, a new universal barrier function, which is able to handle various dynamic constraints in a unified manner, is proposed to transform the constrained system into an equivalent one with no constraint. Based on this transformation, a switched-type auxiliary controller and a modified criterion for inverse optimal stabilization are proposed to design an adaptive neural inverse optimal controller. It is proven that optimal performance is achieved with a computationally attractive learning mechanism, and all the constraints are never violated. Besides, improved transient performance is obtained in the sense that the bound of the tracking error could be explicitly designed by users. An illustrative example verifies the proposed methods.

In this paper, we present a novel training scheme, namely Teach-DETR, to better train DETR-based detectors from versatile types of teacher detectors. We show that the predicted boxes from teacher detectors are effective medium to transfer knowledge of teacher detectors, which could be either RCNN-based or DETR-based detectors, to train a more accurate and robust DETR model. This new training scheme can easily incorporate the predicted boxes from multiple teacher detectors, each of which provides parallel supervisions to the student DETR. Our strategy introduces no additional parameters and adds negligible computational cost to the original detector during training. During inference, Teach-DETR brings zero additional overhead and maintains the merit of requiring no non-maximum suppression. Extensive experiments show that our method leads to consistent improvement for various DETR-based detectors. Specifically, we improve the state-of-the-art detector DINO Zhang et al. 2022 with Swin-Large Liu et al. 2021 backbone, 4-scale feature pyramid and 36-epoch training schedule, from 57.8% to 58.9% in terms of mean average precision on COCO 2017valset.

Existing methods on decentralized optimal control of continuous-time nonlinear interconnected systems require a complicated and time-consuming iteration on finding the solution of Hamilton-Jacobi-Bellman (HJB) equations. In order to overcome this limitation, in this article, a decentralized adaptive neural inverse approach is proposed, which ensures the optimized performance but avoids solving HJB equations. Specifically, a new criterion of inverse optimal practical stabilization is proposed, based on which a new direct adaptive neural strategy and a modified tuning functions method are proposed to design a decentralized inverse optimal controller. It is proven that all the closed-loop signals are bounded and the goal of inverse optimality with respect to the cost functional is achieved. Illustrative examples validate the performance of the methods presented.

Existing adaptive neural control methods for nonlinear multiagent systems (MASs) are only applicable under a fixed topology or are applicable under switching topologies but require some linear growth conditions on the nonlinear functions. Motivated by these limitations, a state-dependent adaptive neural design method is proposed in this article. Technically, our method is developed from a state-dependent Lyapunov function candidate, a switched control law, and a projection-based adaptation mechanism. To overcome the stability analysis difficulty caused by the new design of the Lyapunov function, a nonswitched compensation approach and a modified multiple Lyapunov functions method are proposed to derive a dwell-time condition, under which stability can be preserved. It is proved that in addition to stability, synchronization errors converge to a tunable residual around zero. Besides, the proposed scheme achieves the improvement of transient performance in terms of$L_{2}$norm and moreover, once there are no more topology switchings, asymptotic convergence of synchronization errors to a prescribed interval recovers automatically.

Existing studies on adaptive fault-tolerant control for uncertain nonlinear systems with actuator failures are restricted to a common result that only system stability is established. Such a result of not being asymptotically stable is a tradeoff paid for reducing the number of online learning parameters. In this article, we aim to obviate such restrictions and improve the bounded error control to asymptotic control. Toward this end, a resilient adaptive neural control scheme is newly proposed based on a new design of the Lyapunov function candidates, a projection-associated tuning functions method, and an alternative class of smooth functions. It is proved that the system stability is guaranteed for the case of an infinite number of failures and when the number of failures is finite, asymptotic tracking performance can be automatically recovered, and besides, an explicit bound for the tracking error in terms of$L_{2}$norm is established. Illustrative examples demonstrate the methods developed.

Optimized performance obtained from existing adaptive fuzzy optimal control methods comes at the cost of a intricate design procedure and a heavy computation of online parameter learning, and it is an under-explored problem on how to remove such a restriction. In this article, we tackle this problem and ensure the optimized performance using only one adaptive parameter. To this end, a direct adaptive fuzzy inverse approach is first proposed to design a switching-type inverse optimal controller and a one-parameter learning mechanism. It is proved that the proposed approach ensures the input-to-state stability of the control system and besides, the inverse optimality in regard to a meaningful cost functional is achieved. Illustrative examples verify the approach developed.

Most existing methods on optimal finite-time control are restricted to a complex design and learning procedure, and only practical finite-time stable is ensured, which greatly limits the desirable performance of optimal and finite-time control. To solve the problem, an adaptive fuzzy fixed-time inverse approach is first proposed in this article, which achieves the optimized performance without recourse to Hamilton–Jacobi–Bellman equations and improves practical finite/fixed-time stable to fixed-time stable. Technically, to overcome the inverse optimal design difficulty of a nonlinear fixed-time controller, a series of singularity-avoidance functions and a Sontag-type function are incorporated to design a specified form of auxiliary controller, based on which an inverse optimal fixed-time controller is designed. Then, by introducing a two-Lyapunov functions method, it is proved that inverse optimal stabilization is ensured and the tracking error goes to a prescribed interval asymptotically within a fixed-time. Effectiveness of the proposed methods are illustrated by two examples.

The search for new approaches for output feedback control of uncertain nonlinear systems with unknown input and output hysteresis is an interesting problem in control theory. One challenging issue obstructs the development of output feedback control design is that both the genuine system input and output are unknown signals and unable to be employed in the observer and controller design. To obviate such obstruction, a new control design framework for adaptively compensating the input and output hysteresis is proposed with two adaptive hysteresis inverse operators, which are also utilized to develop a novel adaptive hysteresis operator-based filter. It is proved that with the proposed control scheme, all the closed-loop signals are bounded and the tracking error ultimately converges to a tunable residual around zero. Simulation studies demonstrate the methods developed.

In this article, we consider the leader-follower consensus control problem of uncertain multiagent systems, aiming to achieve the improvement of system steady state and transient performance. To this end, a new adaptive neural control approach is proposed with a novel design of the Lyapunov function, which is generated with a class of positive functions. Guided by this idea, a series of smooth functions is incorporated into backstepping design and Lyapunov analysis to develop a performance-oriented controller. It is proved that the proposed controller achieves a perfect asymptotic consensus performance and a tunable L2transient performance of synchronization errors, whereas most existing results can only ensure the stability. Simulation demonstrates the obtained results.

It is interesting to study the problem of adaptive fuzzy control for uncertain nonlinear systems with input and state hysteresis. However, since the hysteresis behavior, especially the state hysteresis, present at the sensors is complicated, mainly in view of its multivalues and rate-dependent features, it is a challenging task to develop the backstepping-based control design. So far, there is still no available result in addressing the problem. In this article, we will pursue this task. A novel indirect fuzzy control scheme is proposed with an alternative adaptive hysteresis inverse, which is used to cancel the unknown input hysteresis. To tackle the effects of state hysteresis, a new dynamic compensation is developed to adaptively accommodate the uncertain dynamics involved in the sensors. It is proved that in addition to system stability, the proposed scheme enables the tracking error to approach a prescribed interval asymptotically. Illustrative examples are used to verify the method developed.

Most current methodologies on fixed-time adaptive fuzzy control for uncertain nonlinear systems result in practical fixed-time stability but not fixed-time stability, or require prior knowledge of the system dynamics. To obviate such restrictions, a fixed-time adaptive fuzzy control scheme is newly proposed with the discoveries of a singularity-avoidance virtual control design, a modified class of tuning functions and a projection operator-based adaptation mechanism. Fixed-time stability is established in the sense that the tracking error asymptotically converges to a user-defined interval within a prescribed fixed time. Illustrative examples verify the approaches developed.

In controlling nonlinear uncertain systems, compensating for rate-dependent hysteresis nonlinearity is an important, yet challenging problem in adaptive control. In fact, it can be illustrated through simulation examples that instability is observed when existing control methods in canceling hysteresis nonlinearities are applied to the networked control systems (NCSs). One control difficulty that obstructs these methods is the design conflict between the quantized networked control signal and the rate-dependent hysteresis characteristics. So far, there is still no solution to this problem. In this paper, we consider the event-triggered control for NCSs subject to actuator rate-dependent hysteresis and failures. A new second-order filter is proposed to overcome the design conflict and used for control design. With the incorporation of the filter, a novel adaptive control strategy is developed from a neural network technique and a modified backstepping recursive design. It is proved that all the control signals are semiglobally uniformly ultimately bounded and the tracking error will converge to a tunable residual around zero.

The application of most existing adaptive dead-zone compensation schemes is limited to the situation where the dead-zone parameters remain unchanged during the system operation. If this is not the case in practice, the closed-loop system stability may no longer be ensured with those schemes because the negative-definite or negative semi-definite property of Lyapunov function may not be satisfied when the dead-zone parameters change in real time. Also, the Lyapunov function is not differentiable from the view on the whole time when the dead-zone parameters change piecewise. Motivated by the observations, in this paper we investigate the output tracking problem for uncertain nonlinear systems in the presence of actuator dead-zone nonlinearity with piecewise time-varying parameters. Technically, by using the projection technique and a modified tuning functions approach, a new adaptive fuzzy control design and a new piecewise Lyapunov function analysis are then developed. It is established that in addition to the system stability, a better quantification of the system performance is achieved in the sense that the tracking error will be controlled within prescribed bounds regardless of the abrupt jumps of the parameters. Finally, simulation results demonstrate the obtained theoretical findings.