Adaptive functional link network control of near-space vehicles with dynamical uncertainties

The control law design for a near-space hypersonic vehicle（NHV） is highly challenging due to its inherent nonlinearity,plant uncertainties and sensitivity to disturbances.This paper presents a novel functional link network（FLN） control method for an NHV with dynamical thrust and parameter uncertainties.The approach devises a new partially-feedback-functional-link-network（PFFLN） adaptive law and combines it with the nonlinear generalized predictive control（NGPC） algorithm.The PFFLN is employed for approximating uncertainties in flight.Its weights are online tuned based on Lyapunov stability theorem for the first time.The learning process does not need any offline training phase.Additionally,a robust controller with an adaptive gain is designed to offset the approximation error.Finally,simulation results show a satisfactory performance for the NHV attitude tracking,and also illustrate the controller＇s robustness.

Robust Consensus Tracking Control of Uncertain Multi-Agent Systems With Local Disturbance Rejection

In this paper,a new distributed consensus tracking protocol incorporating local disturbance rejection is devised for a multi-agent system with heterogeneous dynamic uncertainties and disturbances over a directed graph.It is of two-degree-of-freedom nature.Specifically,a robust distributed controller is designed for consensus tracking,while a local disturbance estimator is designed for each agent without requiring the input channel information of disturbances.The condition for asymptotic disturbance rejection is derived.Moreover,even when the disturbance model is not exactly known,the developed method also provides good disturbance-rejection performance.Then,a robust stabilization condition with less conservativeness is derived for the whole multi-agent system.Further,a design algorithm is given.Finally,comparisons with the conventional one-degree-of-freedombased distributed disturbance-rejection method for mismatched disturbances and the distributed extended-state observer for matched disturbances validate the developed method.

Rotation Angle Control Strategy for Telescopic Flexible Manipulator Based on a Combination of Fuzzy Adjustment and RBF Neural Network

The length of fexible manipulators with a telescopic arm alters during movement.The dynamic parameters of telescopic fexible manipulators exhibit signifcant time-varying characteristics owing to variations in length.With an increase in the manipulators’length,the nonlinear terms caused by fexibility in the manipulators’dynamic equations cannot be ignored.The time-varying characteristics and nonlinear terms of telescopic fexible manipulators cause fuctuations in rotation angles,which afect the operation accuracy of end-efectors.In this study,a control strategy based on a combination of fuzzy adjustment and an RBF neural network is utilized to improve the control accuracy of fexible telescopic manipulators.First,the dynamic equation of the manipulators is established using the assumed mode method and Lagrange’s principle,and the infuence of nonlinear terms is analyzed.Subsequently,a combined control strategy is proposed to suppress the fuctuation of the rotation angle in telescopic fexible manipulators.The variation ranges of the feedforward PD controller parameters are determined by the pole placement strategy and length of the manipulators.Fuzzy rules are utilized to adjust the controller parameters in real-time.The RBF neural network is utilized to identify and compensate the uncertain part of the dynamic model of the fexible manipulators.The uncertain part comprises time-varying parameters and nonlinear terms.Finally,numerical simulations and prototype experiments prove the efectiveness of the combined control strategy.The results prove that the proposed control strategy has a smaller standard deviation of errors.Therefore,the combined control strategy is more suitable for telescopic fexible manipulators,which can efectively improve the control accuracy of rotation angles.

Autonomous planning and control strategy for space manipulators with dynamics uncertainty based on learning from demonstrations

Autonomous planning is a significant development direction of the space manipulator,and learning from demonstrations(LfD)is a potential strategy for complex tasks in the field.However,separating control from planning may cause large torque fluctuations and energy consumptions,even instability or danger in control of space manipulators,especially for the planning based on the human demonstrations.Therefore,we present an autonomous planning and control strategy for space manipulators based on LfD and focus on the dynamics uncertainty problem,a common problem of actual manipulators.The process can be divided into three stages:firstly,we reproduced the stochastic directed trajectory based on the Gaussian process-based LfD;secondly,we built the model of the stochastic dynamics of the actual manipulator with Gaussian process;thirdly,we designed an optimal controller based on the dynamics model to obtain the improved commanded torques and trajectory,and used the separation theorem to deal with stochastic characteristics during control.We evaluated the strategy with locating pre-screwed bolts experiment by Tiangong-2 manipulator system on the ground.The result showed that,compared with other strategies,the strategy proposed in this paper could significantly reduce torque fluctuations and energy consumptions,and its precision can meet the task requirements.

Safety Control of a Class of Fully Actuated Systems Subject to Uncertain Actuation Dynamics

Safety is an essential requirement for control systems.Typically,controlled mobile robots are subject to safety constraints,to which control laws in typical forms may not be directly applicable.This paper employs barrier function to describe safety constraints,analyzes the interaction between the barrier function and the uncertain actuation dynamics by employing the ideas of interconnected systems,and proposes a quadratic-programming-based integration of the control algorithms subject to the safety constraint for a class of fully actuated systems.

Dynamic system uncertainty propagation using polynomial chaos

The classic polynomial chaos method（PCM）, characterized as an intrusive methodology,has been applied to uncertainty propagation（UP） in many dynamic systems. However, the intrusive polynomial chaos method（IPCM） requires tedious modification of the governing equations, which might introduce errors and can be impractical. Alternative to IPCM, the non-intrusive polynomial chaos method（NIPCM） that avoids such modifications has been developed. In spite of the frequent application to dynamic problems, almost all the existing works about NIPCM for dynamic UP fail to elaborate the implementation process in a straightforward way, which is important to readers who are unfamiliar with the mathematics of the polynomial chaos theory. Meanwhile, very few works have compared NIPCM to IPCM in terms of their merits and applicability. Therefore, the mathematic procedure of dynamic UP via both methods considering parametric and initial condition uncertainties are comparatively discussed and studied in the present paper. Comparison of accuracy and efficiency in statistic moment estimation is made by applying the two methods to several dynamic UP problems. The relative merits of both approaches are discussed and summarized. The detailed description and insights gained with the two methods through this work are expected to be helpful to engineering designers in solving dynamic UP problems.

Robust integral control for a class of nonlinear systems with unknown control coefficients

The robust integral control problem is studied for a class of nonlinear systems with input-to-state stable （ISS） unmodeled dynamics in this paper. It does not require a priori knowledge of the control coefficients. Combining the Nussbaum-type gain technique and the backstepping design, we propose a state feedback controller, which could achieve the global asymptotic tracking for any constant reference signal, irrespective of the unmeasured dynamic disturbance. It is shown that the proposed methodology further extends the existing robust nonlinear integral control results. Simulation results verify the correctness of the theoretical analysis.

Robust Event-Triggered Control of Nonlinear Systems Under a Global Sector-Bound Condition

This paper studies an event-tniggered control problem for nonlinear systems subject to both external disturbancoes and dy namic uncertainties.It is assumed that the system satisfies a global sector bound condition.To avold infnitely fast samplng,a novel eventriggred sampling mechanism is propoeed,which use8 not only the measuned system state but also an estimation of the inluence of the disturbances.With the propoeed design,the intersampling intervals an be lower bounded by a poeitive constant,and it is independent of botb external disturbances and dynamie umcertainties.Moreover,the doeedl loop event-tniggered system i proved to be input-torstate stable with repect to the extemal disturbances.Advanced smalgain techmigues are;used for the stability analysis of the dloeeil-bop system.