A trailer oscillation and stability control device including an accelerometer and an angular rate sensor. An oscillation detection discriminator detects oscillatory lateral trailer motion in response to trailer displacement data derived from inputs from the angular rate sensor and acceleration signals received from the accelerometer, and then generates corresponding oscillatory event data. A brake controller generates a braking control signal in response to oscillatory event data received from the oscillation detection discriminator.

A trailer oscillation and stability control device including an accelerometer and an angular rate sensor. An oscillation detection discriminator detects oscillatory lateral trailer motion in response to trailer displacement data derived from inputs from the angular rate sensor and acceleration signals received from the accelerometer, and then generates corresponding oscillatory event data. A brake controller generates a braking control signal in response to oscillatory event data received from the oscillation detection discriminator.

Systems and methods are used to adapt the coefficients of a proportional-integral-derivative (PID) controller through reinforcement learning. The approach for adapting PID coefficients can include an outer loop of reinforcement learning where the PID coefficients are tuned to changes in the environment and an inner loop of PID control for quickly reacting to changing errors. The outer loop can learn and adapt as the environment changes and be configured to only run at a predetermined frequency, after a given number of steps. The outer loop can use summary statistics about the error terms and any other information sensed about the environment to calculate an observation. This observation can be used to evaluate the next action, for example, by feeding it into a neural network representing the policy. The resulting action is the coefficients of the PID controller and the tunable parameters of things such as the filters.

Systems and methods are used to adapt the coefficients of a proportional-integral-derivative (PID) controller through reinforcement learning. The approach for adapting PID coefficients can include an outer loop of reinforcement learning where the PID coefficients are tuned to changes in the environment and an inner loop of PID control for quickly reacting to changing errors. The outer loop can learn and adapt as the environment changes and be configured to only run at a predetermined frequency, after a given number of steps. The outer loop can use summary statistics about the error terms and any other information sensed about the environment to calculate an observation. This observation can be used to evaluate the next action, for example, by feeding it into a neural network representing the policy. The resulting action is the coefficients of the PID controller and the tunable parameters of things such as the filters.

A method and system for reinforcement learning can include an actor-critic framework comprising an actor and a critic, the actor comprising an actor network and the critic comprising a critic network; and a controller comprising a neural network embedded in the actor-critic framework and which can be tuned according to reinforcement learning based tuning including anti-windup tuning.

A method and system for reinforcement learning can include an actor-critic framework comprising an actor and a critic, the actor comprising an actor network and the critic comprising a critic network; and a controller comprising a neural network embedded in the actor-critic framework and which can be tuned according to reinforcement learning based tuning including anti-windup tuning.

The aim of the invention is rapid automatic tuning the parameters of a digital proportional-integral-derivative (PID) controller by analog feedback of an actual value for automation of technological processes with programmable logic controllers (PLCs).

The proposed invention is based on the use of nine tuning equations derived by reverse engineering of a PID controller.

Adjusting the PID controller parameters K.sub.p, K.sub.i and K.sub.d is performed in a closed control loop with negative feedback separately in time, i.e. independently of each other in iteration steps k for K.sub.p, m for K.sub.i and n for K.sub.d (see FIG. 1).

The adaptive tuning method is compact, independent of other methods and algorithms, mathematically balanced (i.e. minimal computational resource requirements), and easy to implement.

Setting up a PID controller by this method does not require a preliminary evaluation of a controlled system and the creation of its mathematical model. This implies its universal applicability.

The aim of the invention is rapid automatic tuning the parameters of a digital proportional-integral-derivative (PID) controller by analog feedback of an actual value for automation of technological processes with programmable logic controllers (PLCs).

The proposed invention is based on the use of nine tuning equations derived by reverse engineering of a PID controller.

Adjusting the PID controller parameters K.sub.p, K.sub.i and K.sub.d is performed in a closed control loop with negative feedback separately in time, i.e. independently of each other in iteration steps k for K.sub.p, m for K.sub.i and n for K.sub.d (see FIG. 1).

The adaptive tuning method is compact, independent of other methods and algorithms, mathematically balanced (i.e. minimal computational resource requirements), and easy to implement.

Setting up a PID controller by this method does not require a preliminary evaluation of a controlled system and the creation of its mathematical model. This implies its universal applicability.

A smart electronic power steering system and method for a retrofitted electric vehicle are provided. In one embodiment, an electronic power steering system comprises a relief valve; a pump in communication with the relief valve; a motor configured to operate the pump; a motor controller configured to control the motor; and a processor. The processor is configured to receive a desired maximum pressure value from a retrofitted electric vehicle and configure the relief valve or motor controller to provide relief at the desired maximum pressure value; and receive a desired flow rate from the retrofitted electric vehicle and configure the motor controller to operate the motor at a speed to achieve the desired flow rate. Other embodiments are provided.

A smart electronic power steering system and method for a retrofitted electric vehicle are provided. In one embodiment, an electronic power steering system comprises a relief valve; a pump in communication with the relief valve; a motor configured to operate the pump; a motor controller configured to control the motor; and a processor. The processor is configured to receive a desired maximum pressure value from a retrofitted electric vehicle and configure the relief valve or motor controller to provide relief at the desired maximum pressure value; and receive a desired flow rate from the retrofitted electric vehicle and configure the motor controller to operate the motor at a speed to achieve the desired flow rate. Other embodiments are provided.