In this article, a robust adaptive intelligent fault-tolerant controller with prescribed performance is proposed for an uncertain quadruped robot with actuator fault. The control system comprised of three terms: (1) a full-state feedback controller which includes the prescribed performance function, (2) an adaptive intelligent wavelet-based Takagi-Sugeno fuzzy network (TSFN), and (3) a robust control term. The proposed controller does not utilize the robot dynamic model. A wavelet-based TSFN is utilized to approximate adaptively the lumped nonlinear terms, parameter uncertainties, and defective torque signal. The wavelet block acts as a feature extractor, reduces the number of fuzzy rules, and also acts as a normalization function. The parameters of TSFN are tuned online by an adaptive law based on Lyapunov stability theory. The proposed controller guarantees the desired specification such as minimum speed of convergence, maximum steady-state error, overshoot concerning the position tracking error, and also bounded closed-loop signals. Numerical simulations on MATLAB/SimMechanics environment demonstrate the stable walking of the quadruped robot in the presence of the actuator faults and parameter uncertainties.

The paper reports on flight tests at hovering of the COLIBRI robot. After a short review of the control model and the stabilization strategy, two different approaches are considered for the attitude reconstruction from the MEMS Inertial Measurement Unit (IMU): the complementary filter and the full-state dynamic observer, implemented in a specially designed flight control board. It is shown that both strategies provide adequate stabilization at hovering in spite of the strong vibration excitation resulting from the flapping of the wings. Moreover, it is shown that the residual wandering due to noise, robot imperfection, etc., can be significantly reduced by a cascade control loop based on the axial and lateral velocities reconstructed by the full-state observer. Experiments show that this approach based on onboard measurements allows for a station keeping as good as that obtained with velocities reconstructed from an external tracking system. The paper also reports endurance tests conducted with two different robot configurations; the maximum flight time observed is 4 min 30 s.

After a short overview of the COLIBRI project, this paper considers the cycle-averaged flight dynamics of a flapping-wing robot near hovering, taking advantage of the weak coupling between the roll and pitch axes. The system is naturally unstable; it needs to be stabilized actively, which requires an attitude reconstruction. Due to the flapping of the wings, the system is subject to a strong periodic noise at the flapping frequency and its higher harmonics; the resulting axial forces and pitch moments are characterized from experimental data. The flapping noise propagates to the six-axis Inertial Measurement Unit (IMU) consisting of three accelerometers and three gyros. The paper is devoted to attitude reconstruction in the presence of flapping noise representative of flight conditions. Two methods are considered: (i) the complementary filter based on the hovering assumption and (ii) a full-state dynamic observer (Kalman filter). Unlike the complementary filter, the full-state dynamic observer allows the reconstruction of the axial velocity, allowing us to control the hovering without any additional sensor. A numerical simulation is conducted to assess the merit of the two methods using experimental noise data obtained with the COLIBRI robot. The paper discusses the trade-off between noise rejection and stability.

This paper investigates a model-free reinforcement learning-based approach that enables the quadruped robot to manipulate objects while maintaining its balance and dynamic stability during walking. At first, the dynamics of quadruped robots in two sub-spaces, position control space and force control space, are developed. Then, a new long-term performance index is introduced, and a radial basis function neural network as a critic network is presented to estimate the unobtainable long-term performance index. Based on the exported reinforcement signal, the actor neural network is introduced to generate the feedforward compensation term to cope with the nonlinear dynamics and the system uncertainties. The robustness of the actor-critic reinforcement learning algorithm is enhanced by using a fractional-order sliding-mode controller in the closed-loop system. The online adaptive laws for both the critic and actor-network weights are obtained using the Lyapunov stability theory. As a result, the uniformly ultimately boundedness of the position and the force tracking errors are proven. Finally, numerical simulations are conducted to illustrate the feasibility and effectiveness of the proposed adaptive actor-critic learning-based control scheme.