Adaptive Dead-zone Compensation Research Articles

Deep Reinforcement Learning-Based Control for Asynchronous Motor-Actuated Triple Pendulum Crane Systems With Distributed Mass Payloads

For transporting large cargoes in practice, the triple pendulum hoisting pattern is widely utilized, where the distributed mass payload (DMP) is suspended under the hanger, the hanger is suspended on the hook, and the hook is connected with the hoisting mechanism. However, existing studies mainly focus on single pendulum and double pendulum hoisting patterns, and few of them pay attention to triple pendulum hoisting patterns. Compared with single pendulum and double pendulum cranes, triple pendulum cranes, which control 4 degrees of freedom (DoFs) with merely 1 control input, have a higher underactuation degree, with higher control difficulties. In addition, the nonlinear characteristics of alternating current (AC) asynchronous motors, such as unknown nonlinear dead zones and time delays, will degrade tracking accuracy and control performance. To solve the above problems, a model-free deep reinforcement learning-based trajectory planning method is proposed for triple pendulum DMP cranes. Furthermore, an identification model for AC asynchronous motors is established. On this basis, a trajectory tracking method including an adaptive dead zone compensation (ADC) and a modified Smith predictor (MSP) is proposed. Finally, experiments are carried out on a self-built 2-ton crane experimental platform, which is actuated by AC asynchronous motors. Hardware experiments demonstrate that triple pendulum cranes can accurately track the planned trajectory, and the DMP residual swing is effectively suppressed.

A Design Method of Automatic Driving Control Systems with Adaptive Dead-Zone Compensation for Tracked Vehicles

A Design Method of Automatic Driving Control Systems with Adaptive Dead-Zone Compensation for Tracked Vehicles

Adaptive Fuzzy Tracking Control of Uncertain Nonlinear Systems Subject to Actuator Dead Zone With Piecewise Time-Varying Parameters

The application of most existing adaptive dead-zone compensation schemes is limited to the situation where the dead-zone parameters remain unchanged during the system operation. If this is not the case in practice, the closed-loop system stability may no longer be ensured with those schemes because the negative-definite or negative semi-definite property of Lyapunov function may not be satisfied when the dead-zone parameters change in real time. Also, the Lyapunov function is not differentiable from the view on the whole time when the dead-zone parameters change piecewise. Motivated by the observations, in this paper we investigate the output tracking problem for uncertain nonlinear systems in the presence of actuator dead-zone nonlinearity with piecewise time-varying parameters. Technically, by using the projection technique and a modified tuning functions approach, a new adaptive fuzzy control design and a new piecewise Lyapunov function analysis are then developed. It is established that in addition to the system stability, a better quantification of the system performance is achieved in the sense that the tracking error will be controlled within prescribed bounds regardless of the abrupt jumps of the parameters. Finally, simulation results demonstrate the obtained theoretical findings.

Identification and control of class of non‐linear systems with non‐symmetric deadzone using recurrent neural networks

In this study, a neuro-controller with adaptive deadzone compensation for a class of unknown SISO non-linear systems in a Brunovsky form with uncertain deadzone input is presented. Based on a proper smooth parameterisation of the deadzone, the unknown dynamics is identified by using a continuous time recurrent neural network whose weights are adjusted on-line by stable differential learning laws. On the basis of this neural model so obtained, a feedback linearisation controller is developed in order to follow a bounded reference trajectory specified. By means of Lyapunov analysis, the boundedness of all the closed-loop signals as well as the weights and deadzone parameter estimations is rigorously proven. Besides, the exponential convergence of the actual tracking error to a bounded zone is guaranteed. The effectiveness of this scheme is illustrated by a numerical simulation.

Adaptive cascade control of a hydraulic actuator with an adaptive dead-zone compensation and optimization based on evolutionary algorithms

This work presents an adaptive cascade controller tuned by using evolutionary algorithms for the trajectory tracking control of a hydraulic actuator with an overlapped proportional valve. The hydraulic actuator mathematical model includes the dead-zone nonlinearity due to the use of the overlapped valve. By considering the hydraulic actuator as a mechanical subsystem driven by a hydraulic one, a cascade strategy is proposed. Such cascade strategy is based on the order reduction and allows one to propose different control laws for each subsystem. Adaptive algorithms are used to compensate the parametric uncertainties in the hydraulic and mechanical subsystems including an adaptive compensation for the valve dead-zone. In the sequence, evolutionary algorithms are used to tune the proposed controller for performance optimization. Simulation results illustrate the main characteristics of the proposed controller and the performance of the evolutionary algorithm called differential evolution in tuning of proposed controller.

Robust adaptive deadzone compensation of DC servo system

Deadzone in DC servo system is often poorly known and it can severely limit system performance. A robust adaptive deadzone compensation scheme is presented for general DC servo system with deadzone of unknown width. The tuning algorithms were rigorously shown to guarantee small tracking errors as well as bounded deadzone parameter estimates using Lyapunov stability. The other prominent feature of the controller is that it can deal with the uncertainty in both inertial and friction parameters. The simulation results are presented to demonstrate its efficacy.

Adaptive dead-zone compensation for output-feedback canonical systems

An adaptive output feedback dead-zone compensation scheme is designed for systems with an unknown dead-zone at the input of an n th-order smooth nonlinear dynamics in the output-feedback canonical form. The proposed adaptive controller employs an adaptive dead-zone inverse to cancel the dead-zone and uses a backstepping design for adaptive output feedback control. It has a new state observer parametrization that is needed to handle the dead-zone uncertainty and a new robust control law that is suitable for the parameter projection needed to implement an adaptive dead-zone inverse. The adaptive dead-zone compensation design ensures closed-loop signal boundedness and improves system tracking performance.