Adaptive Nonlinear PID Control and Rule-Based Compensation for Systems with Backlash

The control of dynamic nonlinear systems with unknown backlash was considered. By using an efficient approach to estimate the unknown backlash parameters, a rule? based backlash compensator was presented for canceling the effect of backlash. Adaptive nonlinear PID controller together with rule? based backlash compensator was developed and a satisfactory tracking performance was achieved. Simulation results demonstrated the effectiveness of the proposed method.

Uncoupled PID Control of Coupled Multi-Agent Nonlinear Uncertain Systems

In this paper, PID（proportional-integral-derivative） controllers will be designed to solve the tracking problem for a class of coupled multi-agent systems, where each agent is described by a second-order high-dimensional nonlinear uncertain dynamical system, which only has access to its own tracking error information and does not need to communicate with others. This paper will show that a 3-dimensional manifold can be constructed based on the information about the Lipschitz constants of the system nonlinear dynamics, such that whenever the three parameters of each PID controller are chosen from the manifold, the whole multi-agent system can be stabilized globally and the tracking error of each agent approaches to zero asymptotically. For a class of coupled first-order multi-agent nonlinear uncertain systems, a PI controller will be designed to stabilize the whole system.

Dynamics and Control of Grinding Machine with Micropositioning Workpiece Table

To improve the machining precision of a surface grinding machine, a micropositioning workpiece table with high performance was used as auxiliary infeed mechanism to implement nanometer level positioning and dynamic compensation. To better understand the characteristics of the grinding machine modulated with micropositioning workpiece table, the dynamic model of the grinding system was established with modal synthesis and Lagrange＇s equation methods. The grinding system was divided into five subsystems. For each subsystem, the generalized kinematic and potential energies were obtained. Accordingly the dynamic model of the grinding system was given in the modal domain. The waviness of the grinding process was achieved based on the wheel and workpiece vibration. A nonlinear proportional integral derivative （PID） controller with differential trackers was developed to realize dynamic control. The simulation results show that the machining accuracy of the workpiece can be effectively improved by utilizing the micropositioning workpiece table to implement dynamic compensation. An experimental test was carried out to verify the proposed method, and the waviness of the workpiece can be reduced from 0.46 μm to 0.10 μm.

Spatial Path Tracking Controllers for Autonomous Ground Vehicles:Conventional and Nonconventional Schemes

Unlike time-based path tracking controllers,theε-controller is a spatial path tracking controller.It is a purely geometric path tracking controller and essentially a P-controller to maintain the reasonable spatial distance,ε,from the vehicle to the desired path.In this paper,we present some enhancement schemes using the non-conventional PI control laws via optimization.We propose to use a nonlinear termε^(1/3)for the proportional controller.A fractionalorder integral used to achieve a PI^(α)control.Among the schemes,an optimization search procedure applied to-nd optimal controller gains by meshing the regions around the values from approximate linear designs.The performance index for parametric optimization is the integration of the absolute purely spatial deviation from the desired path.Three different types of road shape were chosen and the Gazebo-ROS simulation results were presented to show the effectiveness of the proposed enhancement schemes.The results show that in some cases a smaller J_(area)and O_(ι)can be achieved by using P^(1/3)controller,but its disadvantage is there may be some oscillation.For PI^(α)controller,there is an additional adjustable parameterα,better performance can be achieved without signi-cant disadvantages which is worth in-depth research.

Recurrent neural networks-based multivariable system PID predictive control

A nonlinear proportion integration differentiation(PID)controller is proposed on the basis of recurrent neural networks,due to the difficulty of tuning the parameters of conventional PID controller.In the control process of nonlinear multivariable system,a decoupling controller was constructed,which took advantage of multi-nonlinear PID controllers in parallel.With the idea of predictive control,two multivariable predictive control strategies were established.One strategy involved the use of the general minimum variance control function on the basis of recursive multi-step predictive method.The other involved the adoption of multi-step predictive cost energy to train the weights of the decou-pling controller.Simulation studies have shown the efficiency of these strategies.