On the design of a nonlinear model predictive controller based on enhanced disturbance observer for dynamic walking of biped robots

In this paper a nonlinear model predictive control strategy based on enhanced nonlinear disturbance observer is proposed to control the dynamic walking of biped robots on the smooth surface considering double support phase, single support phase, and impact. Optimal tracking of reference trajectories via optimal joint torque is established via a nonlinear predictive controller with well-defined cost functions and associated constraints. The implementation of a conventional disturbance observer encounters numerous challenges due to the joint acceleration requirements. The proposed nonlinear disturbance observer here, which only requires the position and angular velocity, helps to estimate the disturbances introduced on the robot and reduce the complications. The simulation results performed on the dynamic walking of a 5-DOF biped robot on flat surface shows the merits of the proposed method in tracking arbitrary trajectories despite the disturbances

Robust fault detection and isolation in semi-actively controlled building structures using a set of unknown input observers

Building structures are subject to earthquakes and unwanted vibrations which can be effectively managed via controllers. Semi-actively controlled building structures are prone to sensor and actuator faults very similar to other dynamical systems. When a fault occurs in sensors or actuators of a controlled systems, the system faces a performances degradation or even failure. Consequently, it is vitally important to detect and isolate a fault at the right time in these systems. To do so, here, unknown input observers (UIO) are proposed for robust fault detection and isolation of actuators and sensors in buildings. For the proof of concept, a three-story structure with magnetorheological (MR) dampers is taken into account. Via designing these observers, each faulty actuator and/or sensor is detected and isolated. Here, the LQR controller is also used to facilitate an optimal control strategy over the system. The obtained simulation results utter the acceptable accuracy of the proposed method in the detection of fault (time and location) and the robustness of the fault detection method against external disturbances

Design of a nonlinear model-based predictive controller for a wind turbine based on PMSG using an augmented extended Kalman filter

In this paper, a novel nonlinear model-based predictive controller without speed/position sensors is designed for control of a wind turbine permanent magnet synchronous generator (PMSG) and grid. Here, both grid and generator sides are controlled via a predictive mechanism including an optimization subject to nonlinear constraints of current and voltage amplitudes as well as the harmonic distortion magnitude of currents. To have a sensorless design, a Kalman filter is also designed. First, an extended Kalman filter (EKF) is used to estimate the speed and then an augmented extended Kalman filter (AEKF) is designed to estimate the flux without the need to add complex equations. The simulation results show an acceptable performance of the proposed method despite the changes in the reference speed and disturbance

Observer-Based Suboptimal Controller Design for Permanent Magnet Synchronous Motors: State-Dependent Riccati Equation Controller and Impulsive Observer Approaches

Permanent Magnet Synchronous Motors (PMSMs) with high energy efficiency, reliable performance, and a relatively simple structure are widely utilised in various applications. In this paper, a suboptimal controller is proposed for PMSMs without sensors based on the state-dependent Riccati equation (SDRE) technique combined with customised impulsive observers (IOs). Here, the SDRE technique facilitates a pseudo-linearised display of the motor with state-dependent coefficients (SDCs) while preserving all its nonlinear features. Considering the risk of non-available/non-measurable states in the motor due to sensor and instrumentation costs, the SDRE is combined with IOs to estimate the PMSM speed and position states. Customised IOs are proven to be capable of obtaining quality, continuous estimates of the motor states despite the discrete format of the output signals. The simulation results in this work illustrate an accurate state estimation and control mechanism for the speed of the PMSM in the presence of load torque disturbances and reference speed changes. It is clearly shown that the SDRE-IO design is superior compared to the most popular existing regulators in the literature for sensorless speed control