Three-dimensional finite-time guidance law based on sliding mode adaptive RBF neural network against a highly manoeuvering target

In order to intercept a highly manoeuvering target with an ideal impact angle in the three-dimensional space, this paper promises to probe into the problem of three-dimensional terminal guidance. With the goal of the highly target acceleration and short terminal guidance time, a guidance law, based on the advanced fast non-singular terminal sliding mode theory, is designed to quickly converge the line-of-sight (LOS) angle and the LOS angular rate within a finite time. In the design process, the target acceleration is regarded as an unknown boundary external disturbance of the guidance system, and the RBF neural network is used to estimate it. In order to improve the estimation accuracy of RBF neural network and accelerate its convergence, the parameters of RBF neural network are adjusted online in real time. At the same time, an adaptive law is designed to compensate the estimation error of the RBF neural network, which improves the convergence speed of the guidance system. Theoretical analysis demonstrates that the state and the sliding manifold of the guidance system converge in finite time. According to Lyapunov theory, the stability of the system can be guaranteed by online adjusting the parameters of RBF neural network and adaptive parameters. The numerical simulation results verify the effectiveness and superiority of the proposed guidance law.

Multivariable Adaptive Super-Twisting Guidance Law Based on Barrier Function

For tactical missiles, sliding mode control and super-twisting algorithms have been widely studied in the area of guidance law design. However, these methods require the information of the target accelerations and the target acceleration derivatives, which is always unknown in practice. In addition, guidance laws utilizing these tools always have chattering phenomena and large acceleration commands. To solve these problems, this article introduces a barrier function based super twisting controller and expands the controller to a multivariable adaptive form. Consequently, a multivariable adaptive super-twisting guidance law based on barrier function is proposed. Moreover, the stability of the guidance law is analyzed, and the effectiveness and the robustness are demonstrated by three simulation examples. Compared with previous guidance laws using sliding mode control or super-twisting algorithm, the one proposed in this paper does not require the information of target accelerations, nor target acceleration derivatives; it has smaller super-twisting gains so that has smaller acceleration commands; it can increase and decrease the gains to follow the target accelerations and maintain the sliding mode, and it does not chatter.

Humans can track but fail to predict accelerating objects

Objects in our visual environment often move unpredictably and can suddenly speed up or slow down. The ability to account for acceleration when interacting with moving objects can be critical for survival. Here, we investigate how human observers track an accelerating target with their eyes and predict its time of reappearance after a temporal occlusion by making an interceptive hand movement. Before occlusion, the target was initially visible and accelerated for a brief period. We tested how observers integrated target motion information by comparing three alternative models that predicted time-to-contact (TTC) based on the (1) final target velocity sample before occlusion, (2) average target velocity before occlusion, or (3) target acceleration. We show that visually-guided smooth pursuit eye movements reliably reflect target acceleration prior to occlusion. However, systematic saccade and manual interception timing errors reveal an inability to consider acceleration when predicting TTC. Interception timing is best described by the final velocity model that relies on extrapolating the last available velocity sample before occlusion. These findings provide compelling evidence for differential acceleration integration mechanisms in vision-guided eye movements and prediction-guided interception and a mechanistic explanation for the function and failure of interactions with accelerating objects.

A Shake Table Frequency-Time Control Method Based on Inverse Model Identification and Servoactuator Feedback-Linearization

Shake tables are one of the most widespread means to perform vibration testing due to their ability to capture structural dynamic behavior. The shake table acceleration control problem represents a challenging task due to the inherent non-linearities associated to hydraulic servoactuators, their low hydraulic resonance frequencies and the high frequency content of the target signals, among other factors. In this work, a new shake table control method is presented. The procedure relies on identifying the Frequency Response Function between the time derivative of pressure force exerted on the actuator’s piston rod and the resultant acceleration at the control point. Then, the Impedance Function is calculated, and the required pressure force time variation is estimated by multiplying the impedance by the target acceleration profile in frequency domain. The pressure force time derivative profile can be directly imposed on an actuator’s piston by means of a feedback linearization scheme, which approximately cancels out the actuator’s non-linearities leaving only those related to structure under test present in the control loop. The previous architecture is completed with a parallel Three Variable Controller to deal with disturbances. The effectiveness of the proposed method is demonstrated via simulations carried over a non-linear model of a one degree of freedom shake table, both in electrical noise free and contaminated scenarios. Numerical experiments results show an accurate tracking of the target acceleration profile and better performance than traditional control approaches, thus confirming the potential of the proposed method for its implementation in actual systems.

Derivation and Evaluation of a Poincare-Bendixson Theorem Based Target Acceleration Computation Algorithm for Autonomous Driving on Inverse TTC and Time Headway Plane

This paper describes derivation algorithm and evaluation results of a Poincare-Bendixson theorem based target acceleration computation algorithm for autonomous driving on inverse time to collision and time headway plane. Derivation of target acceleration is needed for longitudinal autonomous driving. Ellipsoidal driving area is derived for considering driver’s driving characteristic and safety in time headway-inverse time to collision (TTC) plane. And target acceleration computation algorithm has been proposed based on Poincare-Bendixson theorem. Ellipsoidal driving areas are divided main driving area and real-time driving area. Main driving area is derived based on limit of inverse TTC and time headway for takeover time and human factor, real-time driving area is derived through current driving point with ratio of main driving area. It is designed to computation the target acceleration after deriving the target direction by applying a specific angle based on the normal to the current driving point through the real-time driving area. Specific angle is arbitrary value applied acceleration limitation of actual vehicle. The performance evaluation of target acceleration computation algorithm is has been conducted in Matlab/Simulink environment. It is expected that the proposed algorithm can be used for longitudinal control algorithm for safety and personalization of autonomous vehicle.

Novel adaptive sliding mode guidance law against arbitrarily maneuvering target

A novel adaptive sliding mode guidance law is proposed in this article. The target is assumed to have an arbitrarily but upper bounded maneuvering acceleration which is considered as the system disturbances and uncertainties. The guidance law is consisted of three terms. The first one is a proportional navigation–type term. The second one is a term used for compensating the target maneuvering acceleration. And the last one is a term for controlling the convergence time of the line-of-sight angular rate. In this guidance law, the upper bound of the target acceleration is estimated by an adaptive estimator with a tunable updating law. Hence, the prior knowledge of the upper bound of the target acceleration is not essential for this guidance law. The novel adaptive sliding mode guidance law can guarantee the asymptotical convergence of the line-of-sight rate to zero or its neighborhood, or even the finite time convergence of the line-of-sight rate conditionally. Finally, the new theoretical findings are demonstrated by numerical simulations.