Purpose – This paper aims to use the Matsuoka’s neural oscillators as the basic units of central pattern generator (CPG), and to offer a new CPG architecture consisting of a dual neural CPG of circular three links responsible for oscillator phase adjustment, to which an external neural oscillator is added, which is responsible for oscillator amplitude adjustment, to control foot depth to balance itself when treading on an obstacle. Design/methodology/approach – It is equipped with a triaxial accelerometer and a triaxial gyroscope to obtain a real-time robot attitude, and to disintegrate the foot tilt in each direction as feedback signals to CPG to restore the robot’ horizontal attitude on an uneven terrain. The CPG controller is a distributed control method, with each foot controller consisting of a group of reciprocally coupling neural oscillators and sensors to generate different locomotion by different coupling patterns. Findings – The experiment results indicated that the gait design method succeeded in enabling a steady hexapod walking on a rugged terrain, the mode of response is such that adjustments can only be made when the tilt occurs. Practical implications – The overall control mechanism uses individual foot tilts as the feedback signal input to the neural oscillators to change the amplitude and compare against the reference oscillators of fixed amplitude to generate the foot height reference signals that can balance the body, and then convert the control signals, through a trajectory generator, to foot trajectories from which the actual rotation angle of servo motors can be obtained through inverse kinematics to achieve the effect of restoring the balance when traveling. Originality/value – The controller design based on the bionic CPG model has the ability to restore its balance when its body tilts. In addition to the model’s ability to control locomotion, from the response waveforms of this experiment, it can also be noticed that it can control the foot depth to balance itself when treading on an obstacle, and it can adapt to a changing environment. When the obstacle is removed, the robot can quickly regain its balance.
In this study, the robust H ∞ stochastic observer-based attack-tolerant guidance control strategy is designed for the nonlinear stochastic missile guidance control system under the external disturbance and measurement noise as well as actuator attack signal and sensor attack signal. To simplify the attack signal estimation, a novel nonsingular smoothed dynamic model is introduced to efficiently describe the actuator and sensor attack signals. Consequently, the state/attack signal estimation can be easily achieved by using conventional Luenberger observer. Next, to attenuate the effect of external disturbance, measurement noise and approximation errors of attack signal on the missile guidance control system, the robust H ∞ attack-tolerant guidance control performance is considered and the design condition is derived in terms of nonlinear Hamilton-Jacobi inequality (HJI) constrained problem. Since HJI can not be easily solved analytically or numerically, the Takagi-Sugeno (T-S) fuzzy modelling method is utilized to facilitate the robust H ∞ attack-tolerant guidance control strategy design. Thereafter, the H ∞ observer-based attacktolerant control design problem is transformed into linear matrix inequalities problem (LMIP) which can be solved very efficiently by using the convex optimization techniques. Simulation example, with the comparison between the proposed method and conventional robust missile guidance strategy, is given to illustrate the design procedures and validate the performance of the proposed method.
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