This paper proposes an enhanced active disturbance rejection control (ADRC) method for the attitude deformation system of a self-developed mobile robot in conjunction with evolutionary game theory-based pigeon-inspired optimization (EGPIO). To achieve precise control of the object, EGPIO is employed as an automatic tuning mechanism for ADRC, and simultaneously improves the rapidity and robustness of the controller. In this novel algorithm, individuals in pigeon-inspired optimization (PIO) adjust their strategies dynamically as the evolutionary game proceeds so as to promote the adaptability of the original PIO. Thus, the optimal solutions calculated by EGPIO will be adopted as the main parameters of ADRC to control the mobile robot. The effectiveness of the novel controller is validated by a series of simulations, including cases with normal conditions and situations with disturbances. Experimental results indicate that the scheme we designed possesses better control performances and shows attractive advantages for efficiency and fault tolerance compared with conventional ADRC.

Online state-of-charge (SOC) estimation for lithium-ion batteries is one of the most important tasks of the battery management system in ensuring its operation safety and reliability. Due to the advantages of learning the long-term dependencies in between the sequential data, recurrent neural networks (RNNs) have been developed and have shown their superiority over SOC estimation. However, only time-series measurements (e.g., voltage and current) are taken as inputs in these RNNs. Considering that the mapping relationship between the SOC and the time-series measurements evolves along with the battery degradation, there still remains a challenge for RNNs to estimate the SOC accurately throughout the battery’s lifetime. In this paper, a dual-input neural network combining gated recurring unit (GRU) layers and fully connected layers (acronymized as a DIGF network) is developed to overcome the above-mentioned challenge. Its most important characteristic is the adoption of the state of health (SOH) of the battery as the network input, in addition to time-series measurements. According to the experimental data from a batch of LiCoO₂ batteries, it is validated that the proposed DIGF network is capable of providing more accurate SOC estimations throughout the battery’s lifetime compared to the existing RNN counterparts. Moreover, it also shows greater robustness against different initial SOCs, making it more applicable for online SOC estimations in practical situations. Based on these verification results, it is concluded that the proposed DIGF network is feasible for estimating the battery’s SOC accurately throughout the battery’s lifetime against varying initial SOCs.

The complexity of control algorithms and their vulnerability to disturbances and failures are the main problems that restrict the operations of multi-legged mobile robots in more complex environments. In this paper, a multiple extended state observer (ESO) based control strategy is proposed to achieve stable tilt angle control for quadruped robots under the influence of disturbances and actuator failures. By treating the multiple legs as parallel control objects, more ESOs were added to improve the disturbance rejection ability of the linear active disturbance rejection control (LADRC). Correlation of interactive information about the legs is realized by the synthesis of multiple ESO information. Based on LADRC, this method has the advantages of easy parameter tuning, good robustness, and strong ability to cope with interference and fault conditions. A control system reliability evaluation method was proposed. The reliability and control performance of the multi-ESO based control system under leg stuck failure conditions were systematically analyzed. Simulation and experimental results for the level adjustment control system of a quadruped robot are provided to verify the disturbance rejection ability, feasibility and practicability of the proposed multi-ESO based control method