Dominik Schindele

Sliding-Mode Control of a High-Speed Linear Axis Driven by Pneumatic Muscle Actuators

This paper presents a cascaded sliding-mode (SM) control scheme for a new pneumatic linear axis which could be seen as alternative to an electric direct linear drive. Its guided carriage is driven by a nonlinear mechanism consisting of a rocker with an antagonistic pair of pneumatic muscle actuators arranged at both sides. This innovative drive concept allows for both an increased workspace of approximately 1 m as well as higher carriage velocities of approximately 1.3 m/s as compared to a direct actuation. Modeling of the muscle-driven positioning system leads to a system of four nonlinear differential equations including polynomial approximations of the volume characteristic as well as the force characteristic of the pneumatic muscles. The differential flatness of the system is exploited in combination with SM techniques to stabilize the error dynamics in view of unmodeled dynamics. The internal pressure of each pneumatic muscle is controlled by a fast underlying control loop. Hence, the control design for the outer control loop can be simplified by considering these controlled muscle pressures as ideal control inputs. The control design of the outer control loop involves a decoupling of rocker angle as well as mean internal pressure of both pneumatic muscles as flat outputs. Additionally, model uncertainties in the equation of motion like nonlinear friction are directly counteracted by an observer-based disturbance compensation which reduces the chattering problem. Experimental results show an excellent control performance that outperforms alternative control approaches in a comparison.

Fast Nonlinear MPC for an Overhead Travelling Crane

Abstract This paper presents a nonlinear model predictive control scheme for the two main axes of an overhead travelling crane, which guarantees both tracking of desired trajectories for the crane load and an active damping of crane load oscillations. The main idea of the used NMPC algorithm consists in a minimization of the tracking error at the end of the prediction horizon. That way the computation load can be kept relatively small. The varying length of the rope is considered by gain-scheduling techniques. The position of the crane load is measured by a CMOS camera using the spatial filtering principle. Desired trajectories for the crane load position in the three-dimensional workspace can be tracked independently with high accuracy. Experimental results from an implementation on a test rig show a high control performance.

Comparison of Model-Based Approaches to the Compensation of Hysteresis in the Force Characteristic of Pneumatic Muscles

In this paper, a comparison of three different feedforward compensation strategies that counteract hysteresis effects in the nonlinear force characteristic of pneumatic muscles is presented: The generalized Bouc-Wen model is a dynamic hysteresis model and enables a description of the given highly asymmetric hysteresis, and as alternative hysteresis models for the comparison, the quasi-static Maxwell-slip model and the Prandtl-Ishlinskii model are considered. The parameters of all these hysteresis models have been experimentally identified using evolutionary optimization algorithms. Each of the identified hysteresis models is suitable for an additional feedforward control action in an existing nonlinear control structure for a high-speed linear axis that is actuated by pneumatic muscles to further reduce the tracking error. This cascaded nonlinear control structure consists of fast underlying control loops for the internal muscle pressures and an outer adaptive backstepping control loop for both the carriage position and the mean muscle pressure. Here, the adaptive control part counteracts nonlinear friction and the remaining model uncertainty. Comprehensive experimental results from an implementation of the proposed control approach on a test rig at the Chair of Mechatronics, University of Rostock, Rostock, Germany, point out both the benefits and efficiency of the corresponding feedforward hysteresis compensation strategies.

Adaptive friction compensation based on the LuGre model for a pneumatic rodless cylinder

This paper deals with an adaptive friction compensation strategy based on the LuGre model for a pneumatically driven rodless cylinder. Due to their high dynamics, good force-to-weight characteristic, and their relatively small price pneumatic positioning systems offer an attractive alternative to electrical drives. However, pneumatic positioning systems also involve some difficulties like the nonlinear characteristic of the air mass flow through the servovalves and friction forces acting on the piston. To consider nonlinear friction in the control structure, a LuGre observer has been implemented. The update law for the estimated observer parameters is obtained by adaptive backstepping control. The employed control approach has a cascade structure: In an underlying control loop, the internal pressures of the left and right chamber of the cylinder are controlled, whereas the carriage position and the mean pressure of the two chambers represent the control variables of the outer loop. Experimental results from an implementation on a test rig show a high control performance.

Flatness-based control for an internal combustion engine cooling system

To further increase the efficiency of internal combustion engines, improved model-based control approaches for the thermal management become important. Due to time varying thermal loads in the dynamic operation of internal combustion engines active measures involving feedforward and feedback control must maintain a desired nominal engine temperature. Furthermore, both fuel consumption and harmful emissions could be reduced by a short heating-up phase till the optimal operating temperature of the engine. In this paper, hence, a control-oriented model of the thermal behaviour of the engine cooling system is presented. In this model, the volumes flows through the cooling unit as well as the bypass - which are decoupled from the engine angular velocity - are introduced as varying system parameters. Based on this system model, a nonlinear flatness-based control strategy with the engine temperature as flat output is developed. The control structure is extended by a disturbance observer providing estimates for the lost heat of the engine. The complete control structure is adapted to the varying volume flows using gain-scheduling. To allow for an experimental validation of the proposed control approach a small-scale test rig has been build up at the Chair of Mechatronics, University of Rostock. First experimental results show small tracking errors and an advantageous system behavior that allows for an optimization of heating-up strategies in future work.

Nonlinear model predictive control of a high-speed linear axis driven by pneumatic muscles

This paper presents a nonlinear predictive control scheme for a new linear axis. Its guided carriage is driven by a nonlinear mechanism consisting of a rocker with a pair of pneumatic muscle actuators arranged at both sides. This innovative drive concept allows for an increased workspace as well as higher carriage velocities as compared to a direct actuation. Modelling leads to a system of nonlinear differential equations including polynomial approximations of the volume characteristic as well as the force characteristic of the pneumatic muscles. For the control of the carriage position and the mean pressure a nonlinear model predictive trajectory control is designed. The main idea of the used method consists in a minimization of the tracking error at the end of the prediction horizon. That way the computation load can be kept relatively small. Remaining model uncertainties as well as nonlinear friction can be counteracted by an observer-based disturbance compensation. Experimental results from an implementation on a test rig show a high control performance.

Nonlinear model-predictive control with hysteresis compensation of an electro-pneumatic clutch for truck applications

This contribution presents two real-time capable nonlinear model-predictive control (NMPC) approaches for an electro-pneumatic clutch for heavy trucks: a centralized control approach and a cascaded one. A clutch is necessary at start-up or during gear shifts to connect or disconnect the combustion engine and the gear box. This automated actuator disburdens the driver and provides the necessary actuation force according to the large torque typically transmitted through the drive train. The force characteristic of the clutch, however, is subject to hysteresis, which is described by a generalized Bouc–Wen model and used for a feedforward hysteresis compensation in the control algorithm. The proposed NMPC-algorithm involves (i) a minimization of the difference between the desired and predicted state vector at the end of the prediction horizon and (ii) flatness-based techniques to compute desired trajectories for the complete state vector as well as the control input. The optimal control is given by an additional, minimum-norm control input that minimizes the difference between the predicted state vector and the desired one at the end of the prediction horizon. Thereby, the computation effort of the NMPC approaches can be kept relatively small, and a real-time evaluation becomes possible. A reduced-order observer estimates an effective pressure in the clutch that also accounts for an uncertain disturbance force. Thereby, a disturbance compensation and a high tracking accuracy is achievable for the piston position as controlled variable. The efficiency of the two proposed control structures is emphasized by experimental results from a dedicated test rig.

Nonlinear optimal control of an overhead travelling crane

This paper presents an H2-norm optimal control scheme for the three main axes of an overhead travelling crane, which guarantees both tracking of desired trajectories for the crane load and an active damping of crane load oscillations. For the model-based feedback control design series expansions are utilised to obtain an approximate solution of the corresponding Hamilton-Jacobi-Bellmann-equation. The tracking capabilities concerning desired trajectories for the crane load can be significantly improved by introducing feedforward control based on an inverse system model. The position of the crane load is measured by a CMOS camera using the spatial filtering principle. Desired trajectories for the crane load position in the three-dimensional workspace can be tracked independently with high accuracy. Experimental results from an implementation on a test rig show a high control performance.

Observer-based backstepping control of an electro-pneumatic clutch

This paper presents a nonlinear model-based control design for an electro-pneumatic clutch for heavy trucks, which is required at start-up or during gear shifts to disconnect the combustion engine from the gear box. This automated actuator disburdens the driver and provides the necessary actuation force according to the large torque transmitted through the powertrain. The proposed cascaded control structure consists of a fast inner control loop for the internal pressure as well as an outer control loop for the clutch position, which is extended by a reduced-order observer. The design of the feedback part is based on backstepping techniques. A reduced-order disturbance observer estimates the internal pressure, which is usually not measured in truck applications. Thereby, high tracking accuracy is achievable for the piston position as controlled variable. The efficiency of the proposed control structure is demonstrated by experimental results from a dedicated test rig.

ILC for a fast linear axis driven by pneumatic muscle actuators

Iterative learning control is a popular method for accurate trajectory tracking of systems that repeat the same motion many times. This paper presents two different approaches of iterative learning control for a novel linear axis. Its guided carriage is driven by a nonlinear mechanism consisting of two pulley tackles with a pair of pneumatic muscle actuators arranged at both sides. This innovative drive concept allows for an increased workspace as well as higher carriage velocities as compared to a direct actuation. The proposed control has a cascade structure. The internal pressure of each pneumatic muscle is controlled by a fast underlying control loop. Hence, the control design for the outer control loop can be simplified by considering these controlled muscle pressures as ideal control inputs. For the outer control loop a PID-type iterative learning control and, alternatively, a norm-optimal iterative learning control of the carriage position is proposed. Experimental results from an implementation on a test rig show an excellent control performance.