Stabilization of a pan-tilt system using a polytopic quasi-LPV model and LQR control

Linear parameter varying (LPV) models are widely used in control applications of the nonlinear MIMO dynamic systems. LPV models depend on the time varying parameters. This paper develops a polytopic quasi-LPV model for a nonlinear pan-tilt robotic system. A Linear Quadratic Regulator (LQR) that utilizes Linear Matrix Inequalities (LMIs) with well tuned weighting matrices is synthesized based on the developed LPV model. The number of time varying parameters in the developed polytopic LPV model is 4 so the number of vertices becomes 16. The desired controller is generated by the interpolation of LMIs at each vertex. The performance of the optimal LQR controller is evaluated by using the designed feedback gain matrix to stabilize the nonlinear pan-tilt system. Simulations performed on the nonlinear model of the pan-tilt system demonstrate success of the proposed LPV control approach

Stabilization of pan-tilt systems using acceleration based LMI-LQR controller

This paper extends the previous work on LPV modeling of a pan-tilt system [1] and tackles the robust stabilization problem by employing angular acceleration feedback in an LMI based optimal LQR controller. The state vector of the LPV model is augmented to include the integral of the position errors in addition to joint angles and velocities. Therefore, an extended polytopic quasi-LPV model of the pan tilt system is derived. The LMI based optimal LQR controller that utilizes acceleration feedback is synthesized based on the extended LPV model. Since the time varying parameter vector is 4 dimensional, the proposed controller is synthesized by interpolating LMIs at 16 vertices of the polytope. A cascaded nonlinear high gain observer is also designed to estimate reliable positions, velocities and accelerations from noisy encoder measurements. Simulation results show that the proposed LMI based optimal LQR controller outperforms the classical LMI based LQR controller

Supervision and fault tolerance for assistive robotics

In this Master Thesis, the supervision and control problem of service robots in unknown anthropic domains has been addressed from the Fault Detection and Diagnosis (FDD) framework, presenting a complete Fault-Tolerant scheme able to detect, isolate and compensate the effects of an exogenous force acting on a robotic manipulator. Therefore, a systematic approach has been presented, applied to the TIAGo head subsystem, to obtain a Takagi-Sugeno representation suitable for a Parallel Distributed Controller, with the main advantage of defining the complete behaviour of the system using only its representation at the operational limits. Additionally, the Robust Unknown Input Observer for Takagi-Sugeno Models has been implemented for an incomplete information model scenario, which allows decoupling the given estimation from the effect of exogenous faults, disregarding its behaviour nor eventuality. Finally, a characterization of the real robot actuators has been performed, in order to design the suitable mechanisms for their implementation into the complete Fault-Tolerant scheme

Reliable and Safe Motion Control of Unmanned Vehicles

Unmanned vehicles (UVs) are playing an increasingly significant role in modern daily life. In the past decades, numerous commercial, scientific, and military communities across the world are developing fully autonomous UVs for a variety of applications, such as environmental monitoring and surveillance, post-disaster search and rescue, border patrol, natural resources exploration, and experimental platforms for new technologies verification. The excessive opportunities and threats that come along with these diverse applications have created a niche demand for UVs to extend their capabilities to perform more sophisticated and hazardous missions with greater autonomy, lower costs of development and operation, improved personnel safety and security, extended operational range (reliability) and precision, as well as increased flexibility in sophisticated environments including so-called dirty, dull, harsh, and dangerous missions. In order to successfully and effectively execute missions and meet their corresponding performance criteria and overcome these ever-increasing challenges, greater autonomy together with more advanced reliable and safe motion control systems are required to offer the critical technologies for ensuring intelligent, safe, reliable, and efficient control of UVs in the presence of disturbances, actuator saturation, and even actuator faults, especially for practical applications. This thesis concentrates on the development of different reliable and safe motion control algorithms/strategies applicable to UVs, in particular, unmanned aerial vehicles (UAVs) and unmanned surface vehicles (USVs). A number of contributions pertaining to the fault detection and diagnosis (FDD), fault-tolerant control (FTC), disturbance estimation and compensation, and actuator saturation avoidance have been made in this thesis. In addition to the control problems, this thesis also presents several guidance-related contributions, including adaptive observer-based line-of-sight (LOS) guidance law, time-varying lookahead distance scheme, piecewise path switching criterion for guiding a single UV, as well as a proportional-integral (PI) type of leader-follower formation guidance strategy for a group of UVs