Javad Taghia

A sliding mode controller with a nonlinear disturbance observer for a farm vehicle operating in the presence of wheel slip

A sliding mode controller with a nonlinear disturbance observer is proposed and developed to control a farm vehicle to accurately track a specified path. The vehicle is subjected to lateral and longitudinal slips at front and rear wheels. The unpredictability of ground contact forces which occur at the wheels while traversing undulating, rough and sloping terrains require the controllers to be sufficiently robust to ensure stability. The work presented in this paper is directed at the practicality of its application with both matched and unmatched uncertainties considered in the controller design. The controller is designed using an offset model derived from the kinematic model and its operation is verified by simulation and field experiments. In the simulations, the kinematic model based controller is used to control both a kinematic model and a dynamic model of a tractor to verify the performance of the kinematic model based controller. The proposed controller is compared with two other nonlinear controllers, namely, back stepping control and model predictive control. In the field experiments, the three controller were used to control the physical tractor to follow a specified path. Simulation and experimental results are presented to show that the proposed controller demonstrated the required robustness and accuracy at all times.

Derivation of error models incorporating slippage for a tracked vehicle coupled to a steerable trailer

This paper first presents a complete kinematic model and then presents kinematic model based error and offset models for tracked vehicles towing wheeled trailers or implements. In contrast to wheeled vehicles, the tracked vehicles have two tracks on either side of the vehicle. Steering is almost always through skid steer. They are fast becoming popular in agricultural fields due to excellent traction to weight ratio. As agricultural operations are progressing towards autonomous operations, it is important to develop models that can be used in developing controllers. In this paper kinematic models are used to develop two types of models one called the error model and the other called the offset model. They are developed for a system comprising a tracked vehicle towing a steerable wheeled trailer. The error model uses the errors in x, y and θ as the state variables of the model. The offset model uses the path offset and the alignment offset as state variables. Simulation results are presented to validate the error and offset models.

Path following control of an off-road track vehicle towing a steerable driven implement

In this paper, nonlinear controllers are designed for an accurate path-following of a track vehicle towing an active implement, which is steered and driven, in off-road terrains like farms where longitudinal and lateral slips at front tracks and rear steerable wheels are inevitable. These controllers are based on path offset values and a partial dynamic model. The controller is designed to minimize the lon-gitudinal force at the hitch point and steer the track vehicle and the implements wheels for an accurate path-following. Error vectors are introduced to combine the measured longitudinal force at the hitch point and the path offset values for both the track vehicle and the implement. Afterwards, two pairs of virtual states are introduced and nonlinear contracting sliding mode controllers are designed to control the error values. The two cases of the track vehicle without the implement control and the track vehicle with the implement control are compared using dynamic simulations. The results show improvements in the path-following accuracy and the drivability of the whole system when the track vehicle control and the steering and drive force controller of the implement is enabled on simulated off-road terrains.

Integration of sliding mode based steering control and PSO based drive force control for a 4WS4WD vehicle

The aim of this paper is to present a novel approach to enable a four-wheel steer four-wheel drive (4WS4WD) vehicle to follow a predefined path under force control. The novelty is in the combination of a sliding mode controller that determines the steering angles using a kinematic model and a real-time particle swarm optimization based controller that determines the drive torques using a dynamic model. The dynamic model takes into account all the slip forces acting on the vehicle. The combined controllers are then used to drive the 4WS4WD vehicle to follow a path. In order to enable the implementation of the controllers, the path to be followed is generated using 7-order Bézier curves that can provide smooth kinematic and dynamic reference profiles. Simulation results are provided to demonstrate the applicability of the proposed methodology and its robustness.

Force control of two-wheel-steer four-wheel-drive vehicles using model predictive control and sequential quadratic programming for improved path tracking:

This article proposes a novel approach for the two-wheel-steer four-wheel-drive vehicle path tracking based on force control. Both the kinematic and dynamic models are used to calculate the virtual inputs based on a model predictive control algorithm. By using these virtual inputs as the constraints of a real-time sequential quadratic programming optimization, the optimal real inputs such as the drive forces and the steering angles are obtained. Note that the system is discretized when designing the control algorithm. The novelty is that all the forces are taken into account in the dynamic model, and the controller uses optimal steering angles and drive forces to obtain the minimum path offset. The integrated algorithms are then used in tracking a predefined path. Simulation results are provided to demonstrate the effectiveness and applicability of the proposed approach.

A Comparison of some famous electromechanical soft actuators

In this paper, we investigate and compare some famous electromechanical soft actuators. Since electric motors are widely applicable in many industrial and non-industrial robots, application of electromechanical actuators are favorable. Even though they are precise, efficient and easy to control, majority of electric motors are not inherently safe enough in human-robot interactions due to lack of compliance. To provide safety and compliance, electromechanical soft actuators are designed and implemented. In this paper, two main categories of electromechanical soft actuators are analyzed and compared. The first group is series elastic actuators (SEAs) and the second category is mechanically adjustable compliance and controllable equilibrium position actuators, which are called (MACCEPAs). These two groups are compared in terms of compliance, mechanical structure and control strategy. The drawbacks and advantages of each design approach are investigated.

Adaptive position control of fluidic soft-robots working with unknown loads

The compliance of robot structure is essential to provide safety if robots are working in the direct contact with humans. Soft-actuators possess natural inherent compliance but they are characterized with highly non-linear dynamics making accurate control a challenging task. This paper presents the robust and adaptive position control of soft-robots based on pneumatic actuators with rotary elastic chambers (REC-actuators). Previous work shows that accurate control of soft-robots is possible if load parameters are known and taken into account in quasi-static model of soft-robots, that is used as feed-forward signal. This paper demonstrates that adaptation of the robot model provides flexibility and robustness when soft-robots deal with unknown loads. It is shown that due to adaptation the load parameters are not needed to be changed in the quasi-static robot model. The performance of the adaptive controller is proven in simulation and in experiments using firstly a simple 1 degree of freedom (DoF) and then a complex 6 DoF robot structure.