This paper presents a systematic methodology for the synthesis of global gain-scheduled controllers for nonlinear time-varying systems. A controller of this type is used to compute the pitch-axis autopilot of an air-air missile. The missile model used is considered a benchmark for testing autopilot controllers in the academic and industrial communities. The missile's dynamics are linearized at a small set of operating points for which proportional― integral/proportional-type controllers are designed, to shape the frequency response of the linear plants' dynamics. A new set of operating points is computed afterward using the connection between the gap metric and the H ∞ loop-shaping theory. Then, reduced-order, static, H ∞ loop-shaping controllers are designed for this set of points using linear matrix inequality optimization techniques. Finally, the global gain-scheduled controller is obtained by interpolating the proportional―integral/proportional and the loop-shaping controllers' gains over the missile's flight envelope. The simulation results given show the generality and effectiveness ofthe proposed control strategy in terms of the operating point selection, stability, performance and robustness, of the closed loop.

Missile autopilot design : gain scheduling and the gap-metric

In this paper, we propose a reinforcement learning structure for auto-tuning PID gains by solving an optimal tracking control problem for robot manipulators. Capitalizing on the actor–critic framework implemented by neural networks, we achieve optimal tracking performance, estimating unknown system dynamics simultaneously. The critic network is used to approximate the cost function, which serves as an indicator of control performance. With feedback from the critic, the actor network learns time-varying PID gains over time to optimize control input, thereby steering the system toward optimal performance. Furthermore, we utilize Lyapunov’s direct method to demonstrate the stability of the closed-loop system. This approach provides an analytical procedure for a stable robot manipulator system to systematically adjust PID gains, bypassing the ad-hoc and painstaking process. The resultant actor–critic PID-like control exhibits stable adaptive and learning capabilities while maintaining a simple structure and inexpensive online computational demands. Numerical simulations underscore the effectiveness and advantages of the proposed actor–critic neural network PID control. 

Actor–critic learning based PID control for robotic manipulators

Vibration suppression method for flexible link underwater manipulator considering torsional flexibility based on adaptive PI controller with nonlinear disturbance observer

A novel self-adaptive bilateral control strategy is introduced to manage uncertainties inherent in the teleoperation of an underwater manipulator system effectively. In response to uncertainties stemming from both the mathematical model and external disturbances, our approach offers innovative solutions. Firstly, to address uncertainties in the master model parameters, we propose a reference adaptive impedance control based on a nominal model. This control strategy dynamically adjusts the reference position of the desired model, leveraging adaptive control laws to compensate for model uncertainties. Additionally, to tackle uncertainties specific to the slave manipulator, we employ adaptive compensation using radial basis function (RBF) networks. Our unique combination of sliding mode variable structure controllers and robust adaptive controllers aims to mitigate approximation errors, ensuring precise tracking of the master manipulator’s position by the slave manipulator. By employing Lyapunov function analysis, we demonstrate the system’s superior tracking performance and global stability, with assured asymptotic convergence for force–position tracking. Through comprehensive experimentation, our results showcase the exceptional force–position tracking capabilities of the overall control system, even under challenging conditions of model uncertainties and external disturbances. Moreover, our system exhibits remarkable stability, reliability, and robustness, underscoring the effectiveness of our proposed adaptive control approach.

The Adaptive Bilateral Control of Underwater Manipulator Teleoperation System with Uncertain Parameters and External Disturbance

This paper comes up with the adaptive fuzzy scheme for multi-degree robotic manipulators with dead zone. Fuzzy logic system is employed to approximate unknown nonlinear functions and input nonlinear functions with dead zones that are introduced. The adaptive fuzzy technique is utilized to deal with the problems of multi-joint robotic manipulators with unknown nonlinear functions in dead zones. Based on Lyapunov criterion, all the states and signals involved in the system are maintained in a certain bounded region and the tracking error converges to a small domain of origin. Finally, a simulation example of robotic manipulators with two joints is structured to confirm the practicability of the raised scheme.

/pdf/adaptive-fuzzy-controller-design-for-uncertain-robotic-2n672qjv.pdf

Adaptive Fuzzy Controller Design for Uncertain Robotic Manipulators Subject to Nonlinear Dead Zone Inputs

Sliding mode disturbance observer and Q learning-based bilateral control for underwater teleoperation systems

An adaptive bilateral impedance control based on nonlinear disturbance observer for different flexible targets grasping

The nonlinear equations of motion were constructed for a supersonic anti-warship missile. In order to estimate the unknown angle-of-attack, a sliding mode observer was designed. The convergence capability of the sliding mode observer was analyzed according to the Lyapunov stability theory. A sliding mode controller was designed to drive the missile normal overload output to track its command, based on the output-redefinition approach. In order to confirm the performance of the designed sliding mode observer and controller, a simulation example was carried out for nonlinear missile model. The simulation results show the fast convergence capability of the designed sliding mode observer and controller.

Sliding Mode Observer and Controller Design for Nonlinear Supersonic Missile

Aiming at achieving high flexibility and safety, telerehabilitation systems and telesurgery systems often use flexible manipulators in the telerehabilitation systems. However, due to the structure of the flexible manipulator, it has strong model uncertainties and nonlinearity in its dynamic model which causes the difficulty of the accurate control. In order to accomplish accurate trajectory tracking of telerehabilitations systems with flexible manipulators, a bilateral controller is introduced on the basis of the sliding mode control strategy and a non-linear disturbance observer. The non-linear disturbance observer is applied to estimate the model uncertainties and external disturbance of both the master and the slave flexible manipulators in the telerehabilitation system. The asymptotic stability is analyzed by the Lyapunov function. Numerical simulations are performed and results show efficiency and effectiveness of our method. 

A Sliding Mode And Non-linear Disturbance Observer Based Bilateral Control For Telerehabilitation Systems With Flexible Manipulators

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Papers

Sliding mode disturbance observer and Q learning-based bilateral control for underwater teleoperation systems

An adaptive bilateral impedance control based on nonlinear disturbance observer for different flexible targets grasping

Sliding Mode Observer and Controller Design for Nonlinear Supersonic Missile

A Sliding Mode And Non-linear Disturbance Observer Based Bilateral Control For Telerehabilitation Systems With Flexible Manipulators