Iterative Learning-Based Admittance Control for Autonomous Excavation

In this paper, a new iterative learning algorithm is proposed for repetitive nonlinear systems. The control system employs a combination of state feedback and iterative learning control (ILC) in which the coefficients of states are learned similar to ILC methods. The control system is in a closed loop format both in iteration domain (because of ILC) and in time domain (because of feedback control) which improves the robustness of the conventional ILC. The convergence of the control algorithm is also proved. Finally, simulation results for a 5-DOF manipulator have been presented to illustrate that the proposed algorithm is more robust than a first order P type ILC method at the presence of white Gaussian noise as a nonrepeating disturbance.

Admittance control strategy is a vital control method to implement the compliance control of robots. However, the constant admittance parameters often cannot meet the actual needs of dynamic interaction. Aiming to ameliorate the limitation of constant admittance control in the physical human-robot interaction process, fuzzy variable admittance control method on the basis of stiffness identification is put forward to achieve better compliance control capability of a collaborative robots. The operator’s arm stiffness is identified through the recursive least squares method to enhance controller’s stability. The stiffness information can change the upper limit of the adjustment range of the damping coefficient in real-time. Completed the experiment of free traction motion in the vertical z direction of the UR3e collaborative robot. The outcome indicates that the control method proposed can fulfill the changing requirements of the damping coefficient in each stage of the physical human-computer interaction process and improve the compliance motion performance of the cooperative robot pending the physical human-robot interaction process.

Predictive Iterative Learning Control using Laguerre Functions

In this paper, iterative learning identification algorithms are proposed to estimate the time-varying parameters in multi-input-single-output (MISO) Wiener nonlinear time-varying systems. The regression model of the Wiener system is built by using the polynomial expansion of the nonlinear inverse function. Then, two iterative learning algorithms, including iterative learning gradient identification and iterative learning least squares identification, are presented to estimate the time-varying parameters of the regression model. The convergence performance of the iterative learning identification algorithms is analyzed, and numerical simulations are provided to verify the effectiveness of the proposed algorithms.

With the increasing demand for high-precision dynamic interaction tasks in industrial automation, the 7 DDF robotic arm faces the dual challenges of insufficient kinematic modeling accuracy and inadequate end-effector force compliance in complex interaction tasks. This paper focuses on the Realman RM75 seven-degree-of-freedom robotic arm as the subject of study, enhancing its kinematic modeling through an Modified Denavit-Hartenberg (D-H) parameterization method and arm shape angle parameterization technique. Furthermore, it achieves force-compliant control of the robotic arm based on an admittance control strategy. First, a kinematic model of the robotic arm is developed using a Modified D-H parameter approach combined with an arm angle parameterization method to derive a closed-form solution for inverse kinematics. Second, an admittance control framework is designed to adjust the end-effector pose in real time via a virtual spring-damping system, facilitating compliant control in multi-dimensional force interaction scenarios. The experimental results demonstrate the designed control system can achieve good force compliance control at the end of the robotic arm under the action of external contact forces.

Within impedance control framework, since force tracking accuracy is affected by the robotic tracking error, an adaptive force control scheme combined with an iterative learning algorithm is proposed in this paper. First, the unknown environment stiffness and location are observed in real time based on the actual contact force and robot position, and the adaptive reference trajectory is obtained by the estimated environment information. Then, position-based impedance control scheme is adopted to adjust the dynamical relationship between the end-effect or position and the contact force. Considering the effect of the position tracking error on the force tracking accuracy, an iterative learning control method is utilized to compensate the motion error effectively by the force error. The proposed method can effectively reduce the steady state force error by simply combining the indirectly adaptive reference trajectory generation technique with position tracking error regulation method. Stability and convergence conditions are presented with a Lyapunov function and frequency domain method. Simulations and experiments are conducted on a biaxial platform to demonstrate the effectiveness of the proposed method.

Aiming at the problem of a great deal of blindly searching the proportional, differential, integral parameters in the process of designing PID controller, the new algorithm combining genetic algorithm with iterative learning algorithm is named as an iterative learning genetic algorithm (ILGA), which can be used to optimize three controller parameters, thus the optimal parameters can be achieved swiftly by virtue of less iterative learning times, and the design of the PID controller is simplified. As the simulation results shown, the effectiveness of the method is verified in optimizing the PID controller parameters

For the discrete linear multi-agent systems with random actuator faults and system disturbances, an iterative learning control strategy with the forgetting factor is proposed. Firstly, the random variation of actuator faults with the number of iterations is considered. A multiplicative stochastic fault model obeying a normal distribution is designed, and the correction mechanism is given. Secondly, an iterative learning consistency control algorithm with a forgetting factor is provided under the consideration of random disturbances in the system. The system stability is analyzed by using the mean square stability theory. A sufficient condition for the consistency in the multi-agent system is given through the relevant mathematical derivation, which makes it possible to realize the state consistency for the discrete multi-agent system under the occurrence of random faults in the actuator. Finally, the feasibility of the algorithm is verified by numerical simulation in a multi-agent system.

This paper discusses a complex-valued Hopfield associative memory with an iterative incremental learning algorithm. The mathematical proofs derive that the weight matrix is approximated as a weight matrix by the complex-valued pseudo inverse algorithm. Furthermore, the minimum number of iterations for the learning sequence is defined with maintaining the network stability. From the result of simulation experiment in terms of memory capacity and noise tolerance, the proposed model has the superior ability than the model with a complex-valued pseudo inverse learning algorithm.

Laser Metal Deposition (LMD) is a layer-based manufacturing process in which a laser and powdered metal are used to create a molten bead that is then traced along a path to create functional parts. The properties of the structure, including shape and material microstructure, are the result of complex interactions between the laser, the powder, the part substrate and other factors. Thus, a control algorithm is needed to accurately produce the designed part. However, feedback control of the process can create phase lag in the resulting control structure, which in turn can create dimensional instability. Additionally, the LMD process has been shown to change with part height or layer number. Taking these issues into account, a feed-forward, adaptive-type controller that changes with each fabricated layer, should be used. This paper first presents a dynamic model for the LMD process that incorporates the dependency of the process on part height. Then, an optimal Iterative Learning Process Control algorithm is presented to regulate the melt pool morphology of a deposited part using layer number as the iteration axis. A simulation study on the LMD process using the designed process controller shows that it is able to achieve good tracking performance.

The authors show that a local iterative learning algorithm of Diederich and Opper (1987) converges for any set of patterns, including linearly dependent sets, and that the resulting coupling constants for a given set of patterns are the same as those given by the pseudoinverse rule for any maximal linearly independent subset of the given set. Thus, the matrix of couplings produced by the algorithm is the desired projection matrix onto the subspace spanned by the training set.

Muscle weakness is the primary impairment causing mobility difficulty among stroke survivors. Millions of people are unable to live normally because of mobility difficulty every year. Strength training is an effective method to improve lower extremity ability but is limited by the shortage of medical staff. Thus, this paper proposes a robot-assisted active training (RAAT) by an adaptive admittance control scheme with virtual reality interaction (AACVRI). AACVRI consists of a stiffness variable admittance controller, an adaptive controller, and virtual reality (VR) interactions. In order to provide human-robot reality interactions corresponding to virtual scenes, an admittance control law with variable stiffness term was developed to define the mechanics property of the end effector. The adaptive controller improves tracking performances by compensating interaction forces and dynamics model deviations. A virtual training environment including action following, event feedback, and competition mechanism is utilized for improving boring training experience and engaging users to maintain active state in cycling training. To verify controller performances and the feasibility of RAAT, experiments were conducted with eight subjects. Admittance control provides desired variable interactions along the trajectory. The robot responds to different virtual events by changing admittance parameters according to trigger feedbacks. Adaptive control ensures tracking errors at a low level. Subjects were maintained in active state during this strength training. Their physiological signals significantly increased, and interaction forces were at a high level. RAAT is a feasible approach for lower limb strength training, and users can independently complete high-quality active strength training under RAAT.

Gimbal-less dual axis point-to-point (quasistatic) MEMS mirrors have very wide bandwidths for laser beam steering, however users are often limited to only a third of the bandwidth due to the high Q-factor and use of low-pass filters in open-loop operation to avoid overshoot and oscillation. Closed-loop driving enables the use of the full bandwidth with additional complexity in optics and electronics, which can be undesirable in some low SWaP-C systems. But for many applications which require scanning repetitive patterns, such as LiDAR and biomedical imaging, bandwidth utilization and linearity can be greatly increased without any real-time feedback control by training the device and finding an optimal driving waveform using an iterative learning algorithm. The algorithm drives the device with a trial waveform, measures the scan on a Position Sensing Device (PSD), calculates the error between the desired waveform and the measured position, and adjusts the drive waveform for the next iteration based on an approximate linear device model. This is repeated until the error is reduced to below an acceptable specification. The waveform is then saved in the MEMS Controller and can be reliably used for extended periods of operation. Multiple such drive signals can be trained and stored on the controller to perform different types of scans. Several MEMS mirrors, including single- and dual-axis designs, were studied and three are reported here. Overall, in all cases a high accuracy of optical scans is achieved, typically to within ±0.025° of nominal. Repeatability after training, then running in open loop is better than ±0.01° - however, this measurement was limited by the lower resolution of the position detecting sensor. Scan rates achieved vary based on mirror design, but in each case are greatly improved from those achievable with basic driving approaches. Each mirror demonstrated higher quality vector graphics content at faster refresh rates and stable linear rasters at rates below resonance where lines are scanned with uniform velocity. Additionally, each mirror could achieve stable fast rasters with the line-scanning axis rates just below resonance, giving sinusoidal scans with line rates of ~1.6fres. Finally, each mirror was also demonstrated achieving rasters with rates above resonance, giving line rates of ~2.5fres. In all of those cases the other axis could scan linear and sharp sawtooth or triangle waveforms. Based on the symmetry of the MEMS design, we demonstrated the same performance at ddegerent angles, e.g. rastering at a 45° angle.

Constrained Point-to-Point Iterative Learning Control

Due to the nonlinearity, strong coupling, and uncertain parameters of autonomous underwater vehicle (AUV), it is difficult to build an accurate dynamic model, which makes precise control of AUV extremely challenging. To handle the precise heading-following problem of AUV, this paper proposes an iterative learning-based redefine model-free adaptive heading control (IL-RMFAC) method for the underactuated AUV with unknown disturbances based on data driven. The control scheme consists of a learning control algorithm, a parameter iterative update algorithm, and a parameter reset algorithm. It is designed using only the input and output (I/O) data of the controlled system and is a data-driven control method. The pseudo partial derivative (PPD) can be iteratively calculated through the parameter iterative update algorithm and reset algorithm to adjust the learning gain, solving the problem of strictly limited initial position of the traditional fixed learning gain iterative learning control (ILC). A linear combination of angle and angular velocity is introduced in the kinematic layer to avoid overshooting of the expected following target, and an iterative learning method is introduced in the dynamics to improve the accuracy. As the number of iterations increases, the steady-state error is gradually decreased. Finally, by comparing traditional proportional–integral–derivative (PID) simulations, the proposed algorithm’s effectiveness and outstanding performance for the AUV heading tracking are confirmed.