LuGre dynamic friction model has been widely used in servo system for friction compensation, but it increases the difficulty of controller design because its parameters are difficult to be identified and its internal state is immeasurable. This paper presents a parameter identification technique based on novel evolutionary algorithm (NEA) for LuGre friction model. In order to settle the practical digital implementation problem of LuGre model, this paper also proposes a modified dual-observer with discontinuous mapping and smooth transfer function. On the basis of the parameter identification results and the modified dual-observer, this paper designs an adaptive control algorithm with dynamic friction compensation for hydraulic servo system. The comparative experiments indicate that the proposed parameter identification technique and the adaptive control algorithm with modified dual-observer are effective with high tracking performance.

Superficial flow velocity perception is inspired by aquatic organisms and has become increasingly important for underwater robots and vehicles. Existing marine electromagnetic velometers require very large AC coils with high power consumption for excitation, limiting their mobility and underwater applications. Permanent magnet excitation is preferable for miniature flow sensors because of low power consumption, small size, and high sensitivity. However, permanent magnet excitation has not been applicable due to the high output drift caused by electrode polarization voltage. This paper proposes a permanent magnet-based flow velocity meter that measures the velocity based on the electrode output current instead of the output voltage. The relationship between the output current and the fluid velocity, fluid conductivity, permanent magnet parameters, and electrode position during permanent magnet excitation is derived, and a finite element simulation is conducted. A signal processing circuit is designed to obtain the current output. The experimental results show that the output drift ratio of the proposed sensor is 92% lower than that of the sensor measuring the voltage output, and the measurement R ² is 0.998 in the test range of 0-0.875m/s.

In this article, a dynamic high-gain-based decentralized optimal fault-tolerant control (FTC) strategy for a class of strong interconnected systems with unknown actuator faults is proposed. The interactions considered are bounded by some functions that depend on all subsystems' states. A two-layer control framework is presented for each subsystem to steer its tracking error asymptotically to zero. In the upper layer, an error optimizer is designed to generate optimal error trajectory based on a local objective function that converges to zero. In the lower layer, with the aid of an auxiliary system, a trajectory-tracking fault-tolerant controller is given to adaptively accommodate the effects of parameteric uncertainties and actuator faults, where an adaptive high-gain approach is adopted to address the unknown strong interactions. In comparison with existing results, the proposed algorithm relaxes the requirement of output trajectory derivatives, and asymptotic tracking is accomplished. Application results obtained using an electrical power system case demonstrate the validity of the proposed algorithm.

In this article, an adaptive switching controller based on the dynamic zero-moment point for versatile hip exoskeleton is proposed. The linear finite hysteretic state machine is designed to recognize hybrid motion phases. The torque planning strategy based on dynamic zero-moment point is deployed to obtain assistant torque adaptively under different locomotion. Experiments are carried out to verify the performance of the controller, confirming the stability and accuracy of the motion phase recognition, which also demonstrates excellent kinematic performance. The net metabolic rate can be reduced by 6.93% while wearing the versatile hip exoskeleton walking. The integrated surface electromyography can be reduced by 54.8% while wearing the exoskeleton lifting objects. Compared with existing research, the performance of the proposed controller has significant advantages. The proposed controller is capable of multiple types of locomotion, including flat walking, stair climbing, and lifting heavy objects with low complexity and energy consumption.